Give me your opinion on Blockchain technology, and how do you think it will revolutionize cyber security?

"Anytime I see the introduction of a new technology or payment gateway, I take it upon myself to research that technology to see where it is relevant in the world of Cyber Security. When Blockchain came out, I did extensive research on where it might have vulnerabilities, and susceptible to attacks. According to my research, Blockchain has around for about a decade and was initially introduced to store and/or send crypto-currency like Bitcoin. Blockchains are distributed networks with millions of users all over the world. Since Blockchain uses cryptology, it's easier for businesses to authenticate devices and users without the need for a password. This definitely eliminates manual intervention in the process of authentication, thereby avoiding potential attacks."

Ever since Blockchain was introduced to the market, security technologists have been busy trying to keep Blockchain transactions secure through distributed networks so people can use bitcoin or crypto-currency as a payment gateway. Hiring managers, especially in the finance/banking industries, are well aware of this technology, so when they ask this question, they are looking for your opinion on how it applies to cyber security. If you have experience working in an environment that uses Blockchain, showcase that experience in a way that makes you stand out from the crowd. For example, use a scenario (without giving away sensitive company information) where you used it, and how you were able to protect web servers and ID systems so the transactions were safe and secure. If you have limited knowledge in Blockchain, show your eagerness to learn the technology, and how your skills would apply in those situations.

"The way I view Blockchain revolutionizing Cyber Security is through decentralized storage, record keeping, and peer-to-peer sharing. Furthermore, Blockchain users will be able to store all their data on their network or computer if they choose to do so. Basically, a blockchain is a decentralized, digitized, public ledger of all cryptocurrency transactions known as Distributed Ledger Technology. One of the big reasons why I think blockchain is going to be an integral part of Cyber Security is (Distributed Denial of Service. In a nutshell, Blockchain transactions can be denied easily if the send-receive participants are impeded from sending transactions. Blockchains provide a non 'hackable' entrance point, thereby, provide more security when compared with database-driven transactional structures."

What are your salary expectations?

"Currently, I earn a base salary of $66,000 per year plus health benefits. I would like to stay in the same range or slightly higher."

The best way to discuss your salary expectations is to use your current earnings as an example. Be open, and honest. Transparency is the best choice when salary based questions arise.

"I am looking for a base salary of $68-72,000 in my next position."

How will you detect an incident or data breach, and what steps do you take to prevent it from happening?

"An incident can happen at any time without warning, so being ready is critically important. I strongly believe that every company should have a plan B for attacks that take place without warning. Once a hacker gains access to a system, there are two main directives that they pursue. Doing reconnaissance, and exploring to understand the system. Their next steps are to find assets (Data), how to access them, all while acting in a stealth mode in the system. This is where I would be able to detect their movements, and track activity."

There are many software and hardware detection systems in the market that help prevent incidents and data breaches. Hiring managers will ask situational questions about detection and how they start. Explaining to the manager that organizations need to change their mindset about how detection is addressed will definitely give you points in the interview. Furthermore, Cyber experts should be able to articulate the procedures involved in tracking an intruder and where most companies fail with their detection systems. Don't be shy; tell the manager how much you enjoy your work, and how proud you are for setting up and maintaining an environment that that has 0% intrusion as a direct result of your security strategy and vision.

"The trick is having a strong detection system that finds an attacker early in the process to thwart or minimize data theft or other significant damage. In order to find an active attacker, there are two challenges must be addressed and solved: 1) You need to change your approach on what you look for and how you identify them 2) Look at full network activity, rather than limited packet routingI find that the whole mindset has to change, because identifying an active attacker through the typical process of pre-defined signatures, entry behaviors, and other signs isn't the most efficient way of protecting your systems. These hackers are meticulous, and follow a step-by-step campaign to gain entry."

There are different levels of data classification, how are they structured, and why are they required?

"Setting data classification policy if very important, because if you don't have a policy in place, you won't know what your level of sensitivity is, which means you have no baseline or security controls to protect your data. This is an important topic to me, and I take it very seriously. My involvement goes deeper into data classification than any other team member, so I typically take the lead for data classification in three main levels. Restricted Data, Private Data, and Public Data classification. Here's how I classify these three into workable tasks. 1. Restricted Data - I apply the highest level of security to a restricted classification, because it has the highest level of risk. 2. Private Data - This one is a moderate risk level, but should still be treated as private data and protected nonetheless. 3. Public Data - Normally this level is low or no risk. While there are still controls in place, some level of control is still required."

With a heightened level of data security taking place throughout the world, protecting customer data has never been more important. In this day and age, data has become a valuable commodity, and companies go to great lengths to protect it at all costs. When asked by the hiring manager, don't be afraid to offer examples of how you set data classification policies or reclassified data to a classified status with access limited to administrators for example. Also, highlight your knowledge about the different levels of data classification too (IE. Restricted, Private and Public).

"There are a number of different ways that classification of data can be performed. I've always had an interest in data collection and classification, which has led me into a Cyber Security occupation. Interestingly enough, many organizations collect and classify data in different ways. As a Data Steward, it is my obligation to reclassify data - this is conducted periodically - determine what frequency is most appropriate based on available - if after doing a data reclassification, it is determined that the data has changed or was modified, then I look at whether existing controls are consistent with the new data classification. If gaps are found within existing controls, they are immediately corrected."

Explain to me the concept of hyper-converged infrastructure.

"Hyperconverged infrastructure, or HCI, differs greatly from traditional three-tier architecture. It allows a data center to move away from the conventional storage, server, networking silo to a more all-in-one option. The benefits include better automation, simplification, and a more flexible structure."

Hyper-converged infrastructure refers to the streamlining of data center resources, preparing information for the cloud, allowing businesses to more easily scale. The interviewer would like to see that you understand this concept. Give a brief overview of your HCI knowledge. If you are unsure, there are many resources online where you can gain a better understanding. Some of the benefits of HCI are:- Flexible structure, allowing companies to scale much easier - Lower costs, due to being more efficient, amounting to less IT overhead - Automation, simplification, and better support - Increased data production

"Hyper-converged infrastructure is best explained as a method of streamlining a traditional data center setup. When a data center moves to HCI, as opposed to a traditional three-tier structure, they can ensure the integrity of data while maintaining high performance. Essentially, it's servers and storage compacted into one."

What is your knowledge of imputation? Would you kindly list different types of imputation techniques, and which method you find to be most favorable for your environment?

"When I work on different types of imputation techniques, the goal I want to achieve is what missing information am I looking for, and what am I suppose to do with it. Every company has different rules about imputation policies, and how to handle missing data. In most cases, the software you use defaults to a listwize method of cleaning up unwanted, unused or missing data. It's important to look into how much data you are storing, and how much of that is usable data that the business can use. Some of the methods I use to determine single or multiple imputations are the following:1. Hot Deck Imputation - (A missing value imputed from a random selection). 2. Cold Deck Imputation - (Same as hot-deck, but more advanced and selects from other datasets). 3. Mean Imputation - (Replacing missing values with predicted values). 4. Single Imputation - (Denotes that the missing value is replaced by a value)."

There are multiple imputation methods that can be used for missing data. It comes down to the experience one has using imputation techniques. In most cases, your software by default chooses Listwise deletion. This, of course, all depends on why and how much data is missing. Another approach is imputation which means replacing data values with an estimate, then analyzing the data to see if imputed values were observed. A question like this is posed to help the hiring manager determine your level of expertise with imputation. First, start with an overview of your knowledge with imputation and imputation techniques. They will probably be looking for something along the lines of mean imputation and single or multiple imputations. The way to answer this question with relation to mean imputation is to talk about Regression Imputation, Hot Deck Imputation, Cold Deck Imputation, Substitution, Stochastic Regression Imputation, and Interpolation and Extrapolation. If a question comes up about single or multiple imputations, it's going to depend on unbiased parameter estimates and accurate standard errors.

"Working with missing data can be a challenge for most people, but I find this type of work very rewarding professionally. My role requires me to work with a team of data architects and consultants who provide me with data, and then I apply imputation techniques to cleanse the data so that the company can use it for managing and growing the operations. It is important that I obtain the best quality data before making it ready for the business to use. This is why I apply these types of imputation techniques. 1. Multiple Imputation - (Estimates the values multiple times). 2. Regression Imputation - (Involves replacing missing values with a predicted value). 3. Stochastic Regression - (Same as regression, but adds a common regression variance to the imputation)."

Can you describe the difference between a Black Hat, White Hat and Grey Hat hacker?

"When I think of Black Hat Hackers, a few things come to mind. An individual or group of hackers whose intent is to either maliciously penetrate a company's system by writing and distributing malware that leaves a company vulnerable and susceptible to further attacks. The others are cyber espionage and political persuasion. I am directly responsible for following our company protocol when we are alerted that a Black Hat attack is in progress. Much like a chess game, I carefully engage and monitor the activities of the Black Hat Hacker to see what steps and moves I must execute to prevent loss of data or a breach. One of the biggest motivations for Black Hat hackers is personal or financial gain."

When it comes to cyber hackers, you will hear three terms used for hackers. They are Black Hat, White Hat, and Gray Hat hackers. These terms are also synonymous with Search Engine Optimization as well. For informational purposes, here's an overview of all three: Black Hat Hacker - Someone who has knowledge about breaking into or breaching computer systems and bypassing their security protocols. Their primary motivation is to financial or personal gain, and to spread malware or viruses in order to gain access to these computer systems. White Hat Hacker - Someone who is known to use their skills for good rather than evil. They typically work as Cyber Security consultants that are paid to find security vulnerabilities in systems, and work to strengthen those security holes for clients. In A hiring manager might pose a carefully crafted question to determine how well you know each one, and if you fall into one of the three categories. They want to know will you be able to defend against a cyber attack, and how you handle threats from hackers.

"On the flip side of Black Hat Hackers is White Hat Hackers, which serve a completely different purpose and role. White Hat Hackers are what's called ethical hackers. I worked with a company that employed a White Hat Hacker, and I was assigned to work with this individual on a project to perform penetration testing and vulnerability assessments on the security systems to attempt to find weak spots and holes in our system via various hacking methods. I'm also familiar with how Gray Hat Hackers work, and I'm careful how I work and interface with them. The reason being is they don't fall within the Black Hat or White Hat Hacker category, thus the name Gray Hat hacker. I've found that they tend to look for vulnerabilities in a system without notifying the owner of the systems knowledge, and offer to fix those issues for a small fee. There are also instances where I have seen Gray Hat Hackers where Gray Hat hackers post a company's vulnerability in a public forum or social media platform for all to see. I've found that not all hackers are created equal, but I do take precautions with all hackers anyway."

If a client is considering moving from an internal data center environment to our platform. What are the key issues you would recommend them addressing before making this move?

"When transitioning to a hosted cloud environment, there are several things you need to consider. The first and most important is business continuity. You need to make sure that there is no downtime during and after moving to the cloud and that no data is lost or compromised. Another key consideration it the integrity of the data; you don't want it to be hacked or lost between the user and the cloud. Maintaining availability and access to the cloud is also important. Capacity planning is critical, and measures need to be taken to ensure there are enough computing and storage resources available to the users, and that additional resources can be provisioned or de-provisioned as needed. Finally, the company needs to be aware of any regulatory or compliance issues around data integrity and privacy when utilizing cloud hosting."

By asking this question, the interviewer is attempting to understand the depth of your knowledge about a hosted cloud computing environment and your ability to plan and manage client's transitions to the cloud without causing any disruption in their business.

"The key considerations when moving to a cloud hosted environment center around data integrity, availability, and compliance. You need to take measures to ensure that the company's data is secure, can't be compromised when communicating with the cloud, and can be recovered if lost. Most cloud hosting service providers can assist with this through the transition planning and execution process. Having a good backup and disaster recovery plan is also important. Availability of the information, applications and computing resources is the next most critical issue. Selecting the right cloud hosting service provider is the first step. They should offer SLA guarantees and have contingencies in place when issues occur. Finally, it is the company's responsibility to make sure that they are in compliance with any regulations and customer privacy requirements when they move to a cloud environment. Again, although not primarily responsible, the service provider can assist in providing the guarantees and documentation necessary to address this issue."

How would you explain SSL Encryption to someone who does not have a background in tech?

"SSL is short for Secure Sockets Layer. Most websites should have an SSL Certificate, which adds an encryption link between a web server and a browser, allowing for a more secure connection for website visitors. It's a fundamental step that prevents a lot of hacking, identity theft, and other data security issues."

The interviewer would like to see that you can easily describe SSL Encryption. The better you understand the concept, the more easily you will be able to explain it to someone else. Keep your reply straightforward.

"When someone is not familiar with the concept of SSL Encryption, I tell them to look at the URL of the website they are visiting. If the URL starts with 'https' instead of 'http,' it means the site is secured using an SSL Certificate. The added 's' is a good thing, as it means your data is secure."

When dealing with data, in your opinion, is it better to have too many false positives, or too many false negatives? Please explain.

"The way I approach false negatives and false positives is to form a null hypothesis which leads you to try and reject it, thus giving me a positive result. I know that data scientists don't like the notion of swapping hypothesis, but they do give us situations where arriving at a false negative is not ideal. I've heard cases where false positives have had bad outcomes because of how the data was analyzed. Everything is not so Black and White. There are examples that have a hypothesis that cannot be switched due to the nature of science and law, which shows that errors are interchangeable. It comes down to how you design and research your study."

There has been much discussion and speculation about false positives and false negatives, and not all data scientists are in 100% agreement with what is interpreted as a false negative and false negative. Let's say for example you were diagnosed with not having a disease, but after numerous tests, it was verified and confirmed that the disease was in fact still present. That would be considered a false positive. If on the other hand you were diagnosed with a disease, but found out later that you didn't have a disease, which would be considered a false negative. In this type of interview situation, the hiring manager is looking for two things. Your opinion and explanation about false positives and false negatives when it comes to dealing with data management. Data results can be misleading at times, and you need to be able to explain why this is the case, and in your opinion how the data is viewed negatively or positively based on whatever tests were run to arrive at the result from the data. It would help to offer your perspective to the interviewer on the way you determine whether you think it's better to have too many false positives or too many false negatives to sort out.

"Although false positives and false negatives have been debated on probably every topic available, it has been my experience that having too many false negatives over false positive results have been better for our work environment. Especially when you're dealing with something as sensitive as data. There are times when I run QA or validation tests on data, and depending on what we're looking at, we could get a false positive or false negative. That's why having too many false negatives helps weed out the false positives."

Although you are not an experienced network administrator, you should be fluent in the terminology used in our industry. Can you explain to me the difference between symmetric and asymmetric encryption and the benefits of each one?

"Sure, I'd be happy to. Symmetric encryption uses the same key at both ends of the packet transmission. The two processes involve encrypting and then decrypting the packet. Asymmetric encryption employs two different keys for the two processes. Symmetric encryption is faster but poses a bigger risk since the channel the packet is sent through is unencrypted."

This is a direct question meant to test your knowledge of network security management and determine if you are qualified to make recommendations to the customers of the firm you are interviewing with. You should answer this directly and succinctly.

"Symmetric and asymmetric encryption differ in terms of the keys used to encrypt the data packets. Symmetric encryption involves using a signal key to encrypt and decrypt the packet. This is faster but increases the risk of data loss due to the channel being unencrypted. Asymmetric encryption uses two keys' one to encrypt the packet and a different one to decrypt it. This is a more secure method to transmit data, but the additional key slows the process down. Symmetric encryption is best for low-risk, low-latency applications while asymmetric is best for more secure situations."

When designing a machine learning model, what in your opinion is more important, model performance or model accuracy, and why?

"In my opinion, I believe that model performance and model accuracy have an equally important role to play in Machine Learning. When I work with model accuracy, I understand that the better the data is the better the outcome of the results will be. This data provides better predictions and insights that deliver more business value, and by optimizing model accuracy, it mitigates cost as well. I find this to be important at many levels of the organization because while there is a point of diminishing returns, the value of more accurate models corresponds to profit increases for the organization. When I use the Machine Learning model performance method, I start with a baseline model, and then I determine if the model skill is relative, and then assign a score to it. This method, in my opinion, is slightly better, but not by much. The biggest reasons are; mean outcome values for a regression and classification problem, and better input and output for forecasting."

Machine Learning as we know it is revolutionizing the way we buy, the way we live, and the way we work. Companies everywhere use machine learning models to improve the way they do business. It helps companies make more precise business decisions based on the data they have. When developing a machine learning model, one has to rely on data that can be used to develop new products, and provide insights into business operations and decision making processes. Interviewers ask this question because they are interested in a few different things from you. The first thing is your opinion on the importance of model performance and model accuracy. Your opinion should not heavily discount one over the other since they're interested to hear if you have worked with both, and why you have a preference over one or the other. Give an example and explanations of why you think one is better than the other, and detail the features, processes or techniques that you feel work better for you or the company. You could mention that model accuracy is used for identifying relationships and patterns between variables in a dataset. Model performance can also be mentioned as calculating current values based on a prediction and comparing the model's performance.

"Predictive models as a whole are exciting to work with since you're dealing with both model accuracy and model performance. In my career working with data, I find that a machine learning model is directly correlated with the quality of the data provided by the sources in a company. As mentioned earlier, I believe both methods provide value to an organization. Some of the data quality issues I've come across using model accuracy are missing or erroneous values in a data set, categorical values that don't work well with textual values, data dimensions that are required to be reduced due to size, and lastly issues with scaling values and features.""

Could you name a few data cleansing best practices that you follow, and how this has helped you become a better Data Consultant?

"In my eyes, data cleansing practices are an important part of my job duties and responsibilities. At the high level, I start with a data cleansing strategy, so I know what the goals and expectations are for the data cleansing initiative. Here is a list of best practices that I follow that has helped me become a better data consultant. 1. Develop a data quality plan - (Create data KPI's, know where data errors occur, determine where the data is coming from, and perform root cause analysis on data health). 2. Standardize customer contact data - (Check important data at the point of entry and create a standard operating procedure). 3. Validate and verify the accuracy of data - (Use manual, web, and email verification). 4. Identify duplicate entries - (Manual and automated systems). 5. Append data for accuracy - (Company name, contact first name and last name, title, phone, email, location, revenue, product lines, company stock, etc.)."

A lot of companies ask their technology employees about why data cleansing matter to them. The answer is simple. Good quality data will help a company identify potential customers, provide better customer service, run better quality sales and marketing campaigns, and know how much they are spending and tracking to determine ROI's. Data Consultants should have expertise with a variety of business intelligence tools like Informatica, Power BI, Cognos, and Microstrategy. All these tools help the company manage data so it can be used within the company for many purposes. What the hiring manager is seeking out of this question is best practices that you follow. Everyone seems to have a way of doing things their way, and because it's their way, it's not necessarily the best way. This is precisely why the hiring manager wants to hear about your best practices method. Has your method made you a better consultant? The best practices method that you share should convey a message of progressive learning and experience gained in the field while being given more responsibilities for larger and more complex projects. Don't forget to list your best practices and the data cleansing strategy that you use as well.

"Since data is so important to an organization, I make the extra effort to understand how data impacts every part of our organization. The fact is that data drives many aspects of a business's operation, and if you don't understand it, you won't know how to plan a strategy around data expectations. As part of my data cleansing duties, I like to sort out the information required so I can do a proper analysis. This is a very important part of the process because it allows me to find anomalies and perform process improvement techniques to remove repetitive information and data quality errors."

How do you define success?

"I define success by my ability to reach the goals that are set out for me. On a personal level, the things I wish to achieve in my life. On a work level, the targets that are set out for me as well as the professional development that I seek."

What does success mean to you? Tell the interviewer how you see success and be sure to tie your answer into the success that you plan to bring to this particular position, should you be offered the role.

"I consider a project a success only when I have produced work that I can be proud of."

What is a false positive and false negative alert in the case of Intrusion Detection Systems?

"While we try to protect any sort of intrusion or hacking of our systems, we do get the occasional attempted intrusion alerts that tell us where the intrusion is coming from and how they are able to get through a first level of security. This is where we implement a false positive and false negative to give them the impression that they are penetrating our system, but in reality we are watching them to monitor which areas of our network they are attempting to attack, so that we can strengthen that area, and other areas of attempted attacks."

Every company that has a intrusion detection system will likely come across a false positive and/or false negative. If you've ever did test cases for your company, and the results gave you a false positive or false negative, could be because of a bug in the software, a failure in the hardware, or perhaps the functionality is not working properly. In any case, the hiring manager will ask this question to gauge if this problem is caused by manual intervention, or a failure of the systems. It's important to highlight your knowledge and understanding of this topic by talking about the steps you take to monitor the reliability of the hardware and software and use corrective actions to prevent future alerts of this kind. For example, did you have to do further testing or change the code or functionality in the software to correct this problem?

"I've worked with intrusion detection software, anti-virus, and malware software for many years, and have found that even when you test your system, you might get a false positive or false negative. It's not uncommon for this to happen, and I've always proactively planned for it since it's very likely to happen. My approach is to explore why it happens, or what might have caused it to happen, and work towards a preemptive strike to prevent it from happening again. Since both are damaging, and they create a false sense of security, it makes it even more important to me to address it very early in the process. One of the areas I look closely at is test cases. I tend to break them down to the granular level and analyze every detail to get to the core of the problem. I do this by using different test data, metrics and analysis to review test cases, and I do this process manually and also use automation scanning tools as well."

Tell me about your greatest work related accomplishment.

"The greatest accomplishment in my career was graduating University as an honors student while still working full time in a related field. I was top of my class, and working full time. This accomplishment showed me that I could dedicate myself to my career, and reach the goals that I set for myself. It felt great to accomplish so much and be recognized for my dedication."

Talking about your most significant accomplishment will give the interviewer a firm idea of where you place your values. It will also show the interviewer more about your personality, how you like to be motivated, and how to coach you in the future. It is okay to brag a little bit when answering this question. Show that you are proud of yourself and your career accomplishments!

"My greatest work-related accomplishment was the management promotion I received at Company X after just one year. I worked very hard for that promotion and was so happy when my work and dedication was rewarded. It kick-started my career."

Tell me about a time that you were assigned to manage a data analytics project. Walk me through the step-by-step process that you used to kick-off this project?

"Managing a project can come with challenges if you don't take the right steps to prepare and execute properly. There are certain steps I take to ensure that the preparation is done even before kick-off is underway. The pre-prep of a project is a very important step before the actual kick-off. The main elements of the project pre-prep are having the right team members in place with the appropriate skills, and communicating tasks, deadlines, milestones, and clear objectives. Once that is done, I proceed to the data analytics modeling steps. Here's how I prepare, and the steps I take to ensure a successful project kick-off. 1. Define expectations and results - (Requirements gathering sessions with identification of expected results). 2. Team assignments and responsibilities - (Clear vision and outcome of project, and assessment of team capabilities). 3. Project Management - (Monitor and manage project with clear milestones). 4. Organizational Management - (Establish processes, activities, change requests and deliverables). 5. Data Governance - (Management of data quality, security, performance, team, integrity, and priorities)."

Everyone has their own unique style of managing and kicking off a project. What we'll examine here is a commonly used step-by-step process that most people use as part of their preparation to get a project started. The main purpose of an interviewer asking these questions is to understand better your approach to a data problem and what your process is to ensure the success of the project. A good way to get this started is to understand the objective and goal of the project. Before you start the data modeling process, you'll need to have a complete and accurate dataset and make sure that the entire team has specific tasks for what they will be doing throughout the data modeling exercise. During the interview, you will likely be asked what tools you will use as part of the step-by-step process, how you will validate the data, and the method you will use to track the output results.

"There's something special about kicking off a project. Being able to oversee and deliver a successful project is very rewarding. It's not only seeing the project through to its completion but managing it after it's kicked-off. Yes, I do have a process that I follow, and I do monitor the project from the infancy of the concept phase to the realization of what the project will look like when it is delivered to the business unit. I feel it's important to see the vision and realization of how the project will come together even before it gets started. I've had this unique skill for a long time, and it's helped me start and complete a number of projects with data at the center of them."

What are the benefits of leasing a private data center vs. building one from scratch?

"I believe there are benefits to each, and the choice would depend greatly on budget and how much control a company would like to have. If leasing, you can reduce expenditure significantly. If building one from scratch, you would have full control. In my opinion, there are pros and cons to each side."

There are many benefits to either leasing a data center as opposed to building one and vice versa. The interviewer would like to see that you understand the expenditure and risk that comes with either option. When it comes to leasing a private data center the benefits include: - Fewer upfront costs - More predictable expenditure - Ability to scale more easily - Option to contract the experts as opposed to hiring them full time If a company built their own data center the benefits could be: - Ownership and complete control of all operations - More control over your location - Ability to lease out extra space to other businesses Discuss with the interviewer the benefits of each, without swaying too far when it comes to your opinion.

"I have worked for data centers that lease their space, and data centers who own their space. From the leasing side, it is more challenging to scale; however, the costs are much lower. From the ownership side, there are more headaches when it comes to IT staffing; however, there is more control over space, location, and operations."

How would you convince a company that is still undecided about whether or not they should continue managing their own data center or move to a hosted cloud?

"There are several benefits to moving to a hosted cloud. The key benefit is cost, based on the ability to only providing the resources you need. You can scale up the resources during peak business periods and then de-provision them when they are no longer needed. This is difficult in a self-managed data center environment. Other advantages include more secure data and backup, built-in disaster recovery, more flexible software licensing by using Software as a Service (SaaS) and it is platform and OS agnostic. These features provide the organization with greater flexibility. The final benefit is increased productivity. The company can focus on its business rather than running an IT department."

Being able to convince potential clients to join Hitachi Vantara's cloud services is a key skill that hiring managers are looking for. By asking this question, the interviewer is attempting to determine your knowledge of a hosted cloud and your opinion of the benefits.

"Hosted Cloud service providers provide an organization like yours several benefits. The key benefit is cost savings. Moving to a hosted environment allows you to 'rent' your computer resources and only use what you need. This includes compute, storage and even software, using the SaaS model. You pay for what you need when you need it, then put it back on the shelf and don't pay when you're not using it. Other advantages include focusing on your core business vs. running an IT department, being platform and OS agnostic, and being able to use the most productive or popular technologies. Finally, you will be operating in a secure computing environment with SLAs, backups and disaster recovery included with the services."

How do you prevent data leakage?

"Data leakage can be prevented through employee education, training, and then the reinforcement of rules when it comes to security policies. I recently read that research from Cisco showed over 60% of employees use a work computer for personal use. As simple as it is, this practice alone can create a security issue. I believe that data security issues often start with unclear employee practices."

Data leakage occurs when an organizations' data is leaked out in a way they did not authorize. This situation can happen through leakage of printed materials, emails, or lost company laptops or physical files. Hitachi Vantara will have rules in place when it comes to the use of their sensitive or confidential information, and the interviewer wants to know that you understand which steps to take to prevent a data vulnerability. A few ways that a company can prevent data leakage include: - Having a clear security policy for all employees to abide by - Possessing the best software and technology related to security - Ensuring that employees keep business and personal passwords separate - Educating employees on potential vulnerabilities and possible data and cyber attacks

"There are many ways to prevent data leakage; however, in my opinion, it all begins with having the right software and technology. Every company should have a data protection solution such as an ERM that goes far beyond a firewall or simple network monitoring."

Explain what the difference is between Data Mining and Data Analytics, and tell me how you have used both in your recent projects?

"Data Mining and Data Analytics data is compiled by a number of different sources throughout a company. This data comes into the organization in many different forms from different formats, platforms, media, etc. It comes in different shapes, sizes, and venues like social media, social activity reports, customer surveys, emails, weblogs, sensors and bots related to the Internet of Things. I take this data and put it in the correct silos of a data warehouse, then break it down into data points that have relevant and usable data that the business can understand and use it to make business decisions. I was responsible for a data mining project that needed data to calculate operational expenses within the marketing group. They needed to know what it cost the Marketing department to run lead generation campaigns, and what the customer acquisition costs were."

Data Mining and Data Analytics go hand In hand in the world of big data. Data Mining is used to find patterns among large datasets, while Data Analytics is used to test hypothetical models on particular datasets. Over a period of time, you acquire skills that companies see as valuable to their organization. It's these skills that set you apart in the market. As you hone these skills over time, you become good at many different soft skills that accompany the technical skills. Here's where you can use a combination of these skills to articulate a compelling message that explains the differences between Data Mining and Data Analytics. A very important question that might come from an interviewer is how much data do you analyze, and how do you manage that data without risking a security breach. Large scale big data projects use tools like Hadoop as a repository for this data. The reason I mention it is the hiring manger might ask what repository you use to store it, and what your level of confidence using this tool is.

"To me, comparing and contrasting two things like Data Mining and Data Analytics is an exercise that I enjoy a lot. I'm very passionate about data in general. Let me give you an example of these two, and how I used them in my current and past projects. Let's examine the difference between the two, and my experience using both. The Data Analytics process goes into the following steps: Project Definition, Data Collection, Data Analysis, Statistical Analysis, Data Modeling, and Deployment. The Data Mining process is a little different. The steps are Problem Definition, Data Collection, Data Analysis, Statistical Analysis, Data Modeling, Verification and Validation, and Insights. Now for my experience: In a recent project, I collected data from our websites to look at patterns in user behavior and website traffic for all our pages. This data was helpful to the Search Engine Optimization team so they could focus on redeveloping pages that were getting inquires and orders."

As I understand it, the network is the most vulnerable part of a company's IT infrastructure. What is your opinion of this and how would you define the threat and risk to a company in terms of network security?

"The network infrastructure is the most vulnerable part of the IT system because it is the easiest part to access. Since all the company's data is transmitted via the network, hackers focus on this to get the data for malevolent uses. This is the threat to the system. The risk is the company's responsibility to secure their users' and customers' information or be subject to fines, injunctions or even shut down. Measures need to be taken to minimize both the threats and the risks related to network security. I am well versed in these and can expound on them if you would like me to."

This is a 'set the table' question which provides the interviewer with an overview of your knowledge of and perspective on network security.

"I agree with you that the greatest threat to a company's IT infrastructure is the network. Hackers target the network because it is the easiest part of the system to access and because all the data is transmitted over the network. It's the data that hackers want and can cause the most disruption with. The risk to the company if the data is compromised is the loss of business, fines and injunctions and the possibility of being shut down. While the threat associated with network security will always remain, the risk can be minimized through prudent network security practices and procedures."

According to the American National Standards Institute (ANSI) and Telecommunications Industry Association (TIA), how many tiers of data centers are there?

"As a data center engineer, I understand there to be four tiers of data centers, all with a range of capacity and capabilities. The bulk of my experience is with Tier 4 data centers where there is 99.995% availability, two independent utility paths, and significant power outage sustainability."

Not all data centers are created equal. The interviewer would like to see that you have a solid understanding of data centers and the differences between them. - Tier 1 which is usually for small business, guaranteeing 99.671% availability. - Tier 2 is best for medium-sized business, with 99.749% availability. - Tier 3 offers 99.982% availability, making it an excellent option for large companies. These data centers can also sustain up to 72 hours of power outage. - Tier 4 has sustainability up to 96 hours of a power outage, offers 99.995% availability, making it the best choice for enterprise businesses. Explain to the interviewer how many data center tiers there are, and how they differ. Depending on the position for which you are interviewing, you may want to dig deeper into the technical differences between each.

"There are four tiers of data centers, all offering different levels of service depending on the size and needs of a company. A tier one center would be best for small business, while tier four is best for multi-million dollar companies."

Big Data can be an efficient tool to monitor and grow a business, but can have challenges if not properly implemented. What challenges have you encountered while working with big data?

"I have used Business Intelligence tools like ETL, Informatica, Tableau, QLIK, and Power BI. These tools have helped me shape my knowledge base and career path over the years. I enjoy working with data because it's fun to work with, and I get enjoyment out of it. It has been my experience that big data doesn't always work as advertised. I did have some set-backs on a couple of projects that I managed, and the way I was able to resolve some of the impending issues was I had to re-assess the overall situation, and after doing that, was able to figure out that there was miscommunication between team members and the understanding of the final delivery of the implementation. The issue was the data wasn't being analyzed thoroughly enough to use it as accurate data for the business."

Any implementation if not monitored and managed correctly can turn into a complete disaster. This is especially true of Big Data. When you're dealing with large volumes of sales data, customer data, even confidential data, it makes it that much more important to ensure that you mitigate risk at every level of the implementation process. Having prior experience with successful implementations in the big data space will give you a distinct advantage. A hiring manager wants to hear about the challenges you had with past big data implementations. Managers know that implementations don't always go as planned, so they will be listening carefully to see if you own up to your mistakes or blame others for things that go wrong. It goes without saying, but you never badmouth your company, subordinates or colleagues. A good way to turn this question around is to give an example of a big data implementation that didn't go as planned, but that you were able to get it back on track by re-evaluating the process, the requirements and your team's ability and experience to get this implementation completed successfully.

"A lot of technologies are used simultaneously in the implementation process of a big data project. One area that can't be overlooked is storage. My experience with implementations is that if you have the right infrastructure in place like software-defined storage, compression, duplication, and tiering; it can reduce the amount of space and costs associated with big data implementations. If you don't have these in place before you start the implementation, then you're setting yourself up for failure. I was involved in a project that was delayed for a few reasons. One was that the data wasn't validated, another reason was we were working with disparate data sources, and lastly, we came across organizational resistance (Insufficient alignment and lack of middle management understanding)."

Your customer wants you to explain the benefits of the Big Data model you developed, how do you communicate the insights they can use for their business?

"My expertise has been in Data Analysis, so Big Data to me was a pretty flat and easy learning curve. When you're working with large volumes of data, you need to make sure that the data is scrubbed (clean) so that the information can be interpreted by the organization so they can use that data to make better business decisions. I was able to do this by using data warehouse applications that broke down the data in smaller manageable groups that had relevant information about each customer or prospect. Based on my knowledge, there are a lot of benefits that a company can get out of big data. Here's a short list that I have compiled based on my experience:1. Ability to acquire, extract, modify, analyze, and blend the data with various business intelligence tools in order to get the information you need to run your business. 2. You can evaluate risk by a portfolio of products or services. 3. Be able to customize customer experiences. 4. You can identify important information or performance data to improve decision making. 5. Real-time forecasting and monitoring across company-wide locations, divisions, and business units. 6. You can monitor sales and marketing campaigns, and track customer purchasing habits."

Everywhere you look in corporate America; you will find Big Data. It's fast becoming one of the most important facets of a business's operation. Big Data is used for data analytics and data management. If you want to know if your company is profitable or losing money, big data analytics can provide that. If you want to see what product sold best in a specific region of the country or world, big data analytics can provide that information. It's like having a dashboard with multiple screens in one that shows the performance of your company, people, processes, etc. Everyone talks about this concept, but very few can articulate the benefits of big data and how it can help a business. This is especially true for data consultants who are new to big data. Depending on the level of experience that you have, one of the most important areas that you should focus your response on is highlighting the insights that the customer is going to realize when they implement a big data solution. The interviewer is interested in hearing about how you present benefits to the customer in a way that connects with the customer's pain and adds value to the company. Effective communication is key here. They might ask for a hypothetical scenario where you clearly presented a case for big data and it's benefits. It would help to mention that they would be able to calculate metrics for data gathered from multiple sources like social media, customer profile data, company website, etc.

"In years past when I worked with data management tools, it was systems like ETL and Data Warehouses that I used to compile and structure data that the business could use. These were typical Business Intelligence solutions at the time, but they weren't as robust as they are now. My responsibility was to extract and cleanse data so I could present it in a way that was easy to understand to a non-technical person. Having a strong analytical background helped me to analyze different forms of data so I could optimize and index data structures in order to present the many benefits that big data has to offer."

Hitachi Vantara Interview Questions

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1. Give me your opinion on Blockchain technology, and how do you think it will revolutionize cyber security?
2. What are your salary expectations?
3. How will you detect an incident or data breach, and what steps do you take to prevent it from happening?
4. There are different levels of data classification, how are they structured, and why are they required?
5. Explain to me the concept of hyper-converged infrastructure.
6. What is your knowledge of imputation? Would you kindly list different types of imputation techniques, and which method you find to be most favorable for your environment?
7. Can you describe the difference between a Black Hat, White Hat and Grey Hat hacker?
8. If a client is considering moving from an internal data center environment to our platform. What are the key issues you would recommend them addressing before making this move?
9. How would you explain SSL Encryption to someone who does not have a background in tech?
10. When dealing with data, in your opinion, is it better to have too many false positives, or too many false negatives? Please explain.
11. Although you are not an experienced network administrator, you should be fluent in the terminology used in our industry. Can you explain to me the difference between symmetric and asymmetric encryption and the benefits of each one?
12. When designing a machine learning model, what in your opinion is more important, model performance or model accuracy, and why?
13. Could you name a few data cleansing best practices that you follow, and how this has helped you become a better Data Consultant?
14. How do you define success?
15. What is a false positive and false negative alert in the case of Intrusion Detection Systems?
16. Tell me about your greatest work related accomplishment.
17. Tell me about a time that you were assigned to manage a data analytics project. Walk me through the step-by-step process that you used to kick-off this project?
18. What are the benefits of leasing a private data center vs. building one from scratch?
19. How would you convince a company that is still undecided about whether or not they should continue managing their own data center or move to a hosted cloud?
20. How do you prevent data leakage?
21. Explain what the difference is between Data Mining and Data Analytics, and tell me how you have used both in your recent projects?
22. As I understand it, the network is the most vulnerable part of a company's IT infrastructure. What is your opinion of this and how would you define the threat and risk to a company in terms of network security?
23. According to the American National Standards Institute (ANSI) and Telecommunications Industry Association (TIA), how many tiers of data centers are there?
24. Big Data can be an efficient tool to monitor and grow a business, but can have challenges if not properly implemented. What challenges have you encountered while working with big data?
25. Your customer wants you to explain the benefits of the Big Data model you developed, how do you communicate the insights they can use for their business?

Hitachi Vantara Interview Questions

1. Give me your opinion on Blockchain technology, and how do you think it will revolutionize cyber security?

2. What are your salary expectations?

3. How will you detect an incident or data breach, and what steps do you take to prevent it from happening?

4. There are different levels of data classification, how are they structured, and why are they required?

5. Explain to me the concept of hyper-converged infrastructure.

6. What is your knowledge of imputation? Would you kindly list different types of imputation techniques, and which method you find to be most favorable for your environment?

7. Can you describe the difference between a Black Hat, White Hat and Grey Hat hacker?

8. If a client is considering moving from an internal data center environment to our platform. What are the key issues you would recommend them addressing before making this move?

9. How would you explain SSL Encryption to someone who does not have a background in tech?

10. When dealing with data, in your opinion, is it better to have too many false positives, or too many false negatives? Please explain.

11. Although you are not an experienced network administrator, you should be fluent in the terminology used in our industry. Can you explain to me the difference between symmetric and asymmetric encryption and the benefits of each one?

12. When designing a machine learning model, what in your opinion is more important, model performance or model accuracy, and why?

13. Could you name a few data cleansing best practices that you follow, and how this has helped you become a better Data Consultant?

14. How do you define success?

15. What is a false positive and false negative alert in the case of Intrusion Detection Systems?

16. Tell me about your greatest work related accomplishment.

17. Tell me about a time that you were assigned to manage a data analytics project. Walk me through the step-by-step process that you used to kick-off this project?

18. What are the benefits of leasing a private data center vs. building one from scratch?

19. How would you convince a company that is still undecided about whether or not they should continue managing their own data center or move to a hosted cloud?

20. How do you prevent data leakage?

21. Explain what the difference is between Data Mining and Data Analytics, and tell me how you have used both in your recent projects?

22. As I understand it, the network is the most vulnerable part of a company's IT infrastructure. What is your opinion of this and how would you define the threat and risk to a company in terms of network security?

23. According to the American National Standards Institute (ANSI) and Telecommunications Industry Association (TIA), how many tiers of data centers are there?

24. Big Data can be an efficient tool to monitor and grow a business, but can have challenges if not properly implemented. What challenges have you encountered while working with big data?

25. Your customer wants you to explain the benefits of the Big Data model you developed, how do you communicate the insights they can use for their business?