Saturday, June 6, 2020

Data Analyst Interview Question and Answers

1. What Are Some Issues That Data Analysts Typically Come Across?

All jobs have their challenges, and your interviewer not only wants to test your knowledge on these common issues but also know that you can easily find the right solutions when available. In your answer, you can address some common issues, such as having a data file that’s poorly formatted or having incomplete data. 

2. What Are the Main Responsibilities of a Data Analyst?

It is important to be able to define the role you’re interviewing for clearly. Some of the different responsibilities of a data analyst you can use in your response include: analyzing all information related to data, creating business reports with data, and identifying areas that need improvement.

3. When Analyzing Data, What Are Some of the Statistical Methods Used?

There are quite a few answers you can give to this question, so be prepared to answer without much hesitation. Some of the examples you should give to your interviewer include the simplex algorithm, Markov process, and bayesian method. 

4. What Does the Standard Data Analysis Process Look Like?

If you’re interviewing for a data analyst job, you'll likely be asked this question and its one that your interviewer will expect that you can quickly answer, so be prepared. Be sure to go into detail and list and describe the different steps of a typical data analyst process. These steps include data exploration, data preparation, data modeling, validation, and implementation of the model and tracking.  

5. How Does Data Analysis Differ from Data Mining?

As a professional data analyst, you should be able to identify what sets data mining apart from data analysis quickly. Use a few key examples in your answer: for instance, you can explain that data analysts must create their equations based on a hypothesis, but when it comes to data mining, algorithms automatically develop these equations. You may also want to mention that the data analysis process begins with a hypothesis, but data mining does not.

6. What Two Steps Are Performed During the Data Validation Process?

You should easily be able to demonstrate to your interviewer that you know and understand these steps, so be prepared for this question if you are asked. Be sure to not only answer with the two different steps—data validation and data verification—but also how they are performed. 

7. What is the Interquartile Range?

Shown in a box plot, the interquartile range is the difference between the lower and upper quartile, and is a measure of the dispersion of data. If you’re interviewing for a data analyst job, it’s important to be prepared with a similar answer and to answer confidently. 

8. What is an Outlier?

Another must-know term for any data analyst, the outlier (whether multivariate or univariate), refers to a distant value that deviates from a sample’s pattern. 

9. Why Do You Want to Be a Data Analyst?

If you already have experience as a data analyst, this can be easier to answer: explain why you love working as a data analyst and why you want to continue. As a new data analyst, this question can catch you off-guard, but be prepared with an honest answer as to why you want to work in this industry. For example, you can say that you enjoy working with data, and it has always fascinated you. 

10. In Your Opinion, What Skills and Qualities Should a Successful Data Analyst Have?

There is no right or wrong answer to this question necessarily, but it’s good to be prepared for the possibility of this question coming up. Being an analytical thinker and good problem solver is two examples of answers you could use for this type of question.
Few more questions an answers
1) Mention what is the responsibility of a Data analyst?
Responsibility of a Data analyst include,
  • Provide support to all data analysis and coordinate with customers and staffs
  • Resolve business associated issues for clients and performing audit on data
  • Analyze results and interpret data using statistical techniques and provide ongoing reports
  • Prioritize business needs and work closely with management and information needs
  • Identify new process or areas for improvement opportunities
  • Analyze, identify and interpret trends or patterns in complex data sets
  • Acquire data from primary or secondary data sources and maintain databases/data systems
  • Filter and “clean” data, and review computer reports
  • Determine performance indicators to locate and correct code problems
  • Securing database by developing access system by determining user level of access
2) What is required to become a data analyst?
To become a data analyst,
  • Robust knowledge on reporting packages (Business Objects), programming language (XML, Javascript, or ETL frameworks), databases (SQL, SQLite, etc.)
  • Strong skills with the ability to analyze, organize, collect and disseminate big data with accuracy
  • Technical knowledge in database design, data models, data mining and segmentation techniques
  • Strong knowledge on statistical packages for analyzing large datasets (SAS, Excel, SPSS, etc.)
3) Mention what are the various steps in an analytics project?
Various steps in an analytics project include
  • Problem definition
  • Data exploration
  • Data preparation
  • Modelling
  • Validation of data
  • Implementation and tracking
4) Mention what is data cleansing?
Data cleaning also referred as data cleansing, deals with identifying and removing errors and inconsistencies from data in order to enhance the quality of data.
5) List out some of the best practices for data cleaning?
Some of the best practices for data cleaning includes,
  • Sort data by different attributes
  • For large datasets cleanse it stepwise and improve the data with each step until you achieve a good data quality
  • For large datasets, break them into small data. Working with less data will increase your iteration speed
  • To handle common cleansing task create a set of utility functions/tools/scripts. It might include, remapping values based on a CSV file or SQL database or, regex search-and-replace, blanking out all values that don’t match a regex
  • If you have an issue with data cleanliness, arrange them by estimated frequency and attack the most common problems
  • Analyze the summary statistics for each column ( standard deviation, mean, number of missing values,)
  • Keep track of every date cleaning operation, so you can alter changes or remove operations if required
Logistic regression is a statistical method for examining a dataset in which there are one or more independent variables that defines an outcome.
7) List of some best tools that can be useful for data-analysis?
  • Tableau
  • RapidMiner
  • OpenRefine
  • KNIME
  • Google Search Operators
  • Solver
  • NodeXL
  • io
  • Wolfram Alpha’s
  • Google Fusion tables
8) Mention what is the difference between data mining and data profiling?
The difference between data mining and data profiling is that
Data profiling: It targets on the instance analysis of individual attributes. It gives information on various attributes like value range, discrete value and their frequency, occurrence of null values, data type, length, etc.
Data mining: It focuses on cluster analysis, detection of unusual records, dependencies, sequence discovery, relation holding between several attributes, etc.
9) List out some common problems faced by data analyst?
Some of the common problems faced by data analyst are
  • Common misspelling
  • Duplicate entries
  • Missing values
  • Illegal values
  • Varying value representations
  • Identifying overlapping data
10) Mention the name of the framework developed by Apache for processing large data set for an application in a distributed computing environment?
Hadoop and MapReduce is the programming framework developed by Apache for processing large data set for an application in a distributed computing environment.
11) Mention what are the missing patterns that are generally observed?
The missing patterns that are generally observed are
  • Missing completely at random
  • Missing at random
  • Missing that depends on the missing value itself
  • Missing that depends on unobserved input variable
12) Explain what is KNN imputation method?
In KNN imputation, the missing attribute values are imputed by using the attributes value that are most similar to the attribute whose values are missing. By using a distance function, the similarity of two attributes is determined.
13) Mention what are the data validation methods used by data analyst?
Usually, methods used by data analyst for data validation are
  • Data screening
  • Data verification
14) Explain what should be done with suspected or missing data?
  • Prepare a validation report that gives information of all suspected data. It should give information like validation criteria that it failed and the date and time of occurrence
  • Experience personnel should examine the suspicious data to determine their acceptability
  • Invalid data should be assigned and replaced with a validation code
  • To work on missing data use the best analysis strategy like deletion method, single imputation methods, model based methods, etc.
15) Mention how to deal the multi-source problems?
To deal the multi-source problems,
  • Restructuring of schemas to accomplish a schema integration
  • Identify similar records and merge them into single record containing all relevant attributes without redundancy
16) Explain what is an Outlier?
The outlier is a commonly used terms by analysts referred for a value that appears far away and diverges from an overall pattern in a sample. There are two types of Outliers
  • Univariate
  • Multivariate
17) Explain what is Hierarchical Clustering Algorithm?
Hierarchical clustering algorithm combines and divides existing groups, creating a hierarchical structure that showcase the order in which groups are divided or merged.
18) Explain what is K-mean Algorithm?
K mean is a famous partitioning method.  Objects are classified as belonging to one of K groups, k chosen a priori.
In K-mean algorithm,
  • The clusters are spherical: the data points in a cluster are centered around that cluster
  • The variance/spread of the clusters is similar: Each data point belongs to the closest cluster
19) Mention what are the key skills required for Data Analyst?
A data scientist must have the following skills
  • Database knowledge
  • Database management
  • Data blending
  • Querying
  • Data manipulation
  • Predictive Analytics
  • Basic descriptive statistics
  • Predictive modeling
  • Advanced analytics
  • Big Data Knowledge
  • Big data analytics
  • Unstructured data analysis
  • Machine learning
  • Presentation skill
  • Data visualization
  • Insight presentation
  • Report design
20) Explain what is collaborative filtering?
Collaborative filtering is a simple algorithm to create a recommendation system based on user behavioral data. The most important components of collaborative filtering are users- items- interest.
A good example of collaborative filtering is when you see a statement like “recommended for you” on online shopping sites that’s pops out based on your browsing history.
21) Explain what are the tools used in Big Data?
Tools used in Big Data includes
  • Hadoop
  • Hive
  • Pig
  • Flume
  • Mahout
  • Sqoop
22) Explain what is KPI, design of experiments and 80/20 rule?
KPI: It stands for Key Performance Indicator, it is a metric that consists of any combination of spreadsheets, reports or charts about business process
Design of experiments: It is the initial process used to split your data, sample and set up of a data for statistical analysis
80/20 rules: It means that 80 percent of your income comes from 20 percent of your clients
23) Explain what is Map Reduce?
Map-reduce is a framework to process large data sets, splitting them into subsets, processing each subset on a different server and then blending results obtained on each.
24) Explain what is ClusteringWhat are the properties for clustering algorithms?
Clustering is a classification method that is applied to data. Clustering algorithm divides a data set into natural groups or clusters.
Properties for clustering algorithm are
  • Hierarchical or flat
  • Iterative
  • Hard and soft
  • Disjunctive
25) What are some of the statistical methods that are useful for data-analyst?
Statistical methods that are useful for data scientist are
  • Bayesian method
  • Markov process
  • Spatial and cluster processes
  • Rank statistics, percentile, outliers detection
  • Imputation techniques, etc.
  • Simplex algorithm
  • Mathematical optimization
26) What is time series analysis?
Time series analysis can be done in two domains, frequency domain and the time domain.  In Time series analysis the output of a particular process can be forecast by analyzing the previous data by the help of various methods like exponential smoothening, log-linear regression method, etc.
27) Explain what is correlogram analysis?
A correlogram analysis is the common form of spatial analysis in geography. It consists of a series of estimated autocorrelation coefficients calculated for a different spatial relationship.  It can be used to construct a correlogram for distance-based data, when the raw data is expressed as distance rather than values at individual points.
28) What is a hash table?
In computing, a hash table is a map of keys to values. It is a data structure used to implement an associative array. It uses a hash function to compute an index into an array of slots, from which desired value can be fetched.
29) What are hash table collisions? How is it avoided?
A hash table collision happens when two different keys hash to the same value.  Two data cannot be stored in the same slot in array.
To avoid hash table collision there are many techniques, here we list out two
  • Separate Chaining:
It uses the data structure to store multiple items that hash to the same slot.
  • Open addressing:
It searches for other slots using a second function and store item in first empty slot that is found
29) Explain what is imputation? List out different types of imputation techniques?
During imputation we replace missing data with substituted values.  The types of imputation techniques involve are
  • Single Imputation
  • Hot-deck imputation: A missing value is imputed from a randomly selected similar record by the help of punch card
  • Cold deck imputation: It works same as hot deck imputation, but it is more advanced and selects donors from another datasets
  • Mean imputation: It involves replacing missing value with the mean of that variable for all other cases
  • Regression imputation: It involves replacing missing value with the predicted values of a variable based on other variables
  • Stochastic regression: It is same as regression imputation, but it adds the average regression variance to regression imputation
  • Multiple Imputation
  • Unlike single imputation, multiple imputation estimates the values multiple times
30) Which imputation method is more favorable?
Although single imputation is widely used, it does not reflect the uncertainty created by missing data at random.  So, multiple imputation is more favorable then single imputation in case of data missing at random.
31) Explain what is n-gram?
N-gram:
An n-gram is a contiguous sequence of n items from a given sequence of text or speech. It is a type of probabilistic language model for predicting the next item in such a sequence in the form of a (n-1).
32) Explain what is the criteria for a good data model?
Criteria for a good data model includes
  • It can be easily consumed
  • Large data changes in a good model should be scalable
  • It should provide predictable performance
  • A good model can adapt to changes in requirements

BI analyst interview questions

Why you should prepare for BI analyst interview questions?
We know that preparing for a BI analyst interview can be a bit overwhelming, especially if you’re just starting your career. So, to help you find your way through the BI interview maze, we created this article. Here’s what you’ll discover:
  • How to prepare for the BI analyst interview;
  • What questions you can expect to be asked (we’ve listed some real BI analyst interview questions and answers).
  • How the BI interview process goes in 3 real major companies.
Finally, we’ll share some tips on how to make your BI analyst interview performance mistake-free.

What skills do you need to ace the BI Analyst Interview?

To successfully answer BI analyst interview questions, you need relevant knowledge and skills. And that means acquiring the following:
  • Advanced SQL, Python, and/or R skills;
  • Experience with Power BI;
  • Advanced Tableau Desktop and Server abilities;
  • Advanced Excel skills.
What BI Analyst Interview Questions You Should Prepare For?

General BI Analyst Interview Questions

1. Tell me about your educational background and the business intelligence analysis field you’re experienced in.

How to Answer
A business intelligence analyst can concentrate on various industries, such as finance, economics, IT, statistics, manufacturing, and more. Share with the interviewer which area you specialized in while obtaining your university degree, and briefly outline where your career journey has taken you so far. Make sure to demonstrate a keen interest in the company’s industry or business sphere.
Answer Example
“I’m a Finance Graduate specialized in Business Administration. My education has helped me greatly on my business intelligence career path, as my interest and expertise evolved in fields such as business law, microeconomics, and financial accounting.”

2. What’s your experience in SDLC and UAT?

How to Answer
A seasoned BI analyst will have exposure to systems development life cycle (SDLC) and user acceptance testing (UAT). When a company introduces a new software or application to their business, the transition must be well thought out, carefully tested, and then effectively deployed within the organization. An experienced business intelligence analyst can facilitate this process, saving the company time and financial resources. Talk about your exposure to SDLC and UAT. In case you lack the experience, emphasize your interest in becoming familiar with these activities and learning.
Answer Example
“Although I have limited exposure to SDLC, I’ve been involved in the UAT phase of some projects. I enjoy analyzing which aspects of a new software program or application are the hardest to implement, which are the easiest to accommodate, and how to proceed from there.”

3. Do you plan on continuing your education with an MBA?

How to Answer
A lot of accomplished BI analysts have a Bachelor’s degree, while others hold an MBA. With this question, the hiring manager wants to assess your interest in further development that would result in greater career opportunities. As a Master of Business Administration, you’ll have an in-depth understanding of enterprise business, the economy, and how various economic and social factors affect the business environment. That said, an MBA isn’t a must-have for BI analysts. However, showing an interest could give you some competitive edge.
Answer Example
“I have certainly thought about earning an MBA parallel to advancing in my career. As a Business Intelligence analyst, I believe that having an MBA will undoubtedly expand my knowledge of business economics. And that will definitely be beneficial to my future employer and their clients.”

4. What is your opinion about Agile software development for BI projects? Do you support employing Agile methodologies with your company’s clients?

How to Answer
Agile software development has received a warm welcome from companies worldwide since its onset. Agile stimulates collaboration with a team’s clients and the end-users, enabling more cross-functional projects to run smoothly. However, there are still some who strongly prefer the structured development methodology of Waterfall, for example. So, before sharing your thoughts on Agile with the interviewer, make sure you know where the company stands on Agile.
Answer Example
“As far as I know, Agile software development is much more collaborative in comparison to other software development models. I believe Agile can be the best solution in many projects. However, maybe that’s not always the case. That said, I’d love to get familiar with the methodologies employed here. At the end of the day, it’s the end results that matter most, and not the methodologies behind the projects.”

Technical BI Analyst Interview Questions

5. Which data modeling software do you prefer to use?

How to Answer
BI analysts mostly use Microsoft Excel or Power BI for their data modeling needs. The required or preferred tools will be most probably listed in the company’s job description, so it would be best to refer to those. If you have relevant experience, share your level of expertise with the interviewer. In case you lack exposure to their preferred software or programs, explain how you can incorporate your skills into their systems.
Answer Example
“I do most of my data modeling in Excel, as I find it most convenient for data mapping. I have some exposure to Power BI, as well. However, I believe I can benefit from sharpening my skills in that program. That’s why I’m currently taking a Power BI online training.”

6. What specific technical skills do you have as a BI analyst?

How to Answer
Your BI analyst experience and skillset are closely related to the focus of your career. Depending on whether you are a data BI analyst, an IT BI analyst, or a strategic BI analyst, your answer to this question will be different.
If you’re applying for a data-focused role, your technical skillset may include proficiency in data analysis software and visual presentation tools, such as Power BI. A BI analyst in the IT field would probably have exposure to some software development programs. While a strategic BI analyst would be well-familiar with business case analysis software and applications. Taking that into consideration, share with the interviewer the technical skills you will bring to the company.
Answer Example
“As a data BI analyst, I’ve been exposed to data mining and big data software, such as LIONsolver and Oracle. I’m highly skilled in Microsoft Excel which I use for data modeling and Power BI where I create rich visuals and client presentations.”

7. Specify two important chart types in your BI analyst arsenal. Why do you find them important?

How to Answer
The hiring authority wants to see that you have basic knowledge when it comes to the diagrams and charts that you will be using during your business analyst career. Some examples include:
The interviewer wants to check your basic knowledge of charts you’ll be using in your BI analyst’s tasks. Some examples include:
  • area charts;
  • bar charts;
  • clustered column charts;
  • combo charts;
  • doughnut charts;
  • funnel charts;
  • gauge charts;
  • line charts;
  • pie charts;
  • scatter plots;
  • waterfall charts.
You are probably familiar with most of these, so just choose two options which you have experience with and can easily talk about.
Answer Example
“The two types of charts I use most often are area charts and bar charts. In my role as BI analyst, area charts have helped me display where a specific trend is headed in the future, which, in turn, makes planning easier. Bar charts, on the other hand, can show clearly which products are most popular among customers or display the number of unique visitors on a landing page based on various criteria.”
8. How would you define benchmarking and why do you consider it important?
How to Answer
Benchmarking is the practice of evaluating and comparing the business processes in a company with the best competitors’ practices and use these insights to set standards and make improvements to your company’s business performance. When a BI analyst is benchmarking, they study various metrics and processes, such as product development, manufacturing procedures, and more. Discuss with the interviewer how you use benchmarking to help your company achieve its goals.
Answer Example
“Benchmarking is an important practice of comparing your business against other businesses that are already very successful. It’s like a smart, analytical comparison. I believe it’s essential to benchmark when a company is looking at making a significant change, are seeing a loss of revenue, are anticipating the launch of a new product, or need to recalibrate their business operations in one way or another.”

9. How do you differentiate between a risk and an issue?

How to Answer
If you’re an experienced BI analyst, you know for sure there’s a tremendous difference between real risk and an issue. The interviewer wants to check if you can be mindful of probability, while, at the same time, stay focused and hands-on when it comes to current issues.
Answer Example
“In my role as a business intelligence analyst, my focus is more on risk than issues. I view risk as a predicted problem that could come up in the future, so it’s up to me to assess this risk and help my clients prevent it. An issue, on the other hand, is a risk that has already happened. In such cases, I can advise my clients on how to do damage control. But I’d strongly prefer helping them avoid the issue altogether.”

10. What’s your preferred decision-making technique?

How to Answer
The interviewer wants to see what you know about decision-making and what techniques you use to arrive at reliable conclusions in your projects. Some of the common decision-making techniques are T-Chart Analysis, Pareto Analysis, a.k.a. the 80/20 rule, etc. Discuss the techniques you utilize with the interviewer and the reasons for your preferences.
Answer Example
“I don’t limit myself to one technique only. In decision-making, my choice depends largely on the stage of the project. That said, sometimes, I use a variety of techniques within the same project, such as Pareto Analysis, T-Chart Analysis, SWOT Analysis, or decision trees. All of these help me resolve certain issues and come to a decision.”

11. Explain Selection Bias.

Selection bias is the bias introduced by the selection of individuals, groups or data during the sampling process when randomization is not achieved. This means that the sample we created does not represent the general population properly. It is called ‘selection’ because it refers to the ‘sample selection’.
Selection bias is a very broad term that encompasses many different biases. Here are some examples:
Sampling bias
Also known as ‘sample selection bias’ occurs when not everyone in the general population has an equal chance to be in the sample. Let’s say that we want to make a survey about students in one university. We can go to the university, enter random classrooms and ask all of them to participate in our survey. Great, right? Well, not exactly. There are two main issues:
  • We assumed that everyone who is a student at the university will be present at the chosen time and date. And that’s never the case, since students don’t have lectures every day, work part-time, get sick or even go on holiday.
  • We also expected that everyone who is present will want to answer the survey, which is a very optimistic assumption. Students can be forced to, but in that case, one shouldn’t expect a great quality of answers
Length time bias
It occurs when different observations in our sample have different development cycles. The most common case is when we are dealing with diseases like cancer. Some types of cancer develop faster than others, so examining 6 months of disease development for 2 different individuals may result in one having no change, while the other passing away. The reason is that they may be in different stages of cancer or different organisms react differently to the disease so time is extremely problematic in general for our sample.
Exposure bias
This one is very common and problematic. Imagine you’ve got funding to explore everything about a group of your customers, for instance, female customers. You complete your study and everything is great. However, you are then asked to conduct another study – about the shopping habits of your customers overall. Problem is – you don’t have data on the male customers. Using only the female data would lead you to some results, but they would definitely be problematic. If you are not provided with male data and are forced to complete the study, you will experience exposure bias. This is not uncommon when resources are limited – sampling has been done once and nobody wants to pay for another sampling process.
Data bias
There are several popular examples, but one such case is removing outliers with correct data. Usually, we remove the outliers to get better results, but sometimes some important patterns may be contained there.
Studies bias
It occurs when we use only the studies, where we have reached a good result. Often, we have formed a hypothesis and we look for studies that support it. In this effort, one could be misled to reference only papers that support their claim and thus introduce a bias. Note that in general academia is extremely biased in that regard. There is research that suggests that papers that reach results are 4 times more likely to be published than papers that have non-satisfactory results. This is very problematic as we know that determining there is ‘no effect’ is still a valid result.
Attrition bias
it is related to survivorship bias. The most common example that everyone uses is businesses (e.g. startups). All companies that could be studied are successful (profitable ones). Those that are non-successful seize to exist and cannot be studied.
Observer bias
This is the tendency to see what we expect to see – meaning we have already “decided” what outcome we want and we strive for them to be right. As you can sense this one is related to the ‘studies bias’ from above.
Okay. So, there are a lot of problems around selection bias, right?
The best way to get rid of selection bias… is to not introduce our sample to it. No joke, right? What we mean by that is that selection bias is formed in the sampling process. If we are not careful when we collect our data, our analysis will definitely be flawed.

12. What is Kano Model Analysis and why is it important?

How to Answer
The Kano Model Analysis taps into customer’s emotions and needs to improve product development.
It helps a company add certain features to their product that would increase customer satisfaction without costing a fortune. According to the Kano Model Analysis, there are 3 types of attributes to products:
  • Basic attributes;
  • Performance attributes;
  • Excitement attributes, a.k.a. Delighters.
When answering this question, demonstrate that you’re not only familiar with the three points of satisfaction but you also know how they act together to help customer satisfaction analysis.
Answer Example
“Kano Analysis is of major importance to developing new products and services. It helps companies understand customer needs and make sure they have a competitive edge before launching them on the market. The threshold attributes are the basic features a customer expects from the product. The performance attributes also called “satisfiers”, are additional features that increase customer satisfaction. And “delighters” are the elements of surprise that can really increase the product’s competitive edge.”

13. What are the most important SDLC models?

How to Answer
SDLC denotes Software Development Life Cycle. It’s a concept in IT that is often employed by BI analysts in the field. In its essence, SDLC is a process that starts with the decision to launch a product and ends with the full removal of the software product from exploitation. There are various types of SDLC models, each predetermined by the software product type in development. The most popular are:
  • Waterfall model;
  • Iterative model;
  • Spiral model;
  • V-shaped model;
  • Agile model.
Even if you have no experience in the field, show the interviewer that you understand the differences between the models by briefly outlining them.
Answer Example
“Although I don’t have practical experience with ADLC models, I learned in college that there are 5 primary SDLC model types: Waterfall, Iterative, Spiral, V-shaped, and Agile. The Agile model is related to flexibility and adapting to change. The Iterative model refers to the “incremental build” approach in large development efforts. I’m less familiar with the rest but I would enjoy diving deeper and learning more.”

Behavioral BI Analyst Interview Questions

14. How do you demonstrate to your clients the importance of dialogue during a project?

How to Answer
When it comes to clear communication through every stage of a project, leading by example is key. As a business intelligence analyst, it is your job to establish the tone of the dialogue and the means of communication. Show the interviewer that you know how to set the foundations of proper communication with your clients and their teams. If possible, give examples of projects you’ve worked on.
Answer Example
“As a business intelligence analyst, I like to keep everyone in the loop about the development of a project. I often promote the use of project management apps that make collaboration easier and gives access to every detail of the project at any stage.”

15. As a business analyst, when do you regard a project as complete?

How to Answer
A great business analyst knows that when a client signs off a project, it doesn’t mean it’s successful (or finished) yet. So, make sure you explain to the interviewer that you remain available to your clients and you support them until you’re sure their expectations are met and they are happy with the results.
Answer Example
“As a BI analyst, I always make sure there are no unresolved issues when the client signs off a project. Nevertheless, I’m available in case their expectations aren’t fully met and I need to make adjustments to deliver what has been promised. However, this rarely happens once there are no outstanding invoices and documentation is archived.”

16.  How often do you brainstorm new ideas with your coworkers?

How to Answer
Having regular discussions with other team members is of great importance when it comes to project plans and aligning ideas. Let the interviewer know that you’re a team player who is open to others’ views and opinions.
Answer Example
“I believe learning from each other’s working styles and approaches is invaluable for any project. I support the collaborative spirit in my team and I’m sure we always come up with better ideas together rather than individually.”

17. Is there a case in your experience when you broke a confidentiality agreement?

How to Answer
Confidentiality agreements ensure the protection of company trade secrets. This question gives you a chance to present yourself as a trustworthy individual that abides by their company’s policy and respects their clients.
Answer Example
“I have signed NDAs on countless occasions in my career as a business intelligence analyst. When working on a project, confidentiality is one of my team’s top priorities. None of us has broken the trust of our company and clients.”

18. How do you respond when you’re unhappy with the end result of a project?

How to Answer
Even the best BI analysts experience failure at times. Not all projects are perfect, and not all clients can be satisfied. What the interviewer would like to know is if you’re capable of accepting disappointment and responding in a mature and productive way.
Answer Example
“I think business intelligence requires perfectionism at all times. When I’m not happy with my performance, or I make a mistake, I take a step back and take my time to fine-tune my work before submitting it.”

19. How do you plan to improve yourself professionally this year?

How to Answer
Employers are seeking BI analysts who are constantly upgrading their skills and strive to stay relevant. You can set career development goals and accomplish them by attending conferences, earning online certificates, listening to podcasts, or even joining a mentoring program. When you mention some of these examples and the goals you’ve set for yourself this year, make sure you bridge the knowledge you’ll gain with the benefits you’ll bring to the company.
Answer Example
“This year, I’ve enrolled in a Power BI online course to refresh my expertise, and I’ve also signed up for a few TDWI seminars in Predictive Analytics and Data Modeling. I can’t wait to take my skills to another level and, hopefully, apply what I’ve learned as a BI analyst in your company.”

Brainteasers

20. You have 100 balls (50 red balls and 50 blue balls) and 2 buckets. You can choose how to divide the balls into the two buckets so as to maximize the probability of selecting a blue ball if 1 ball is chosen from 1 of the buckets at random.

Put 1 blue ball in one of the buckets and put the rest of the balls in the other bucket. This way you will have 50% chance of selecting the bucket with only 1 ball and then, even if it is not selected and you have to draw a ball from the other bucket you would have almost 50% chance of selecting a blue ball (49 blue balls versus 50 red balls). The joint probability of the two events would equal almost 75%.

Guesstimate

21. How would you estimate the weight of the Chrysler building?

This is a process guesstimate – the interviewer wants to know if you know what questions to ask. First, you would find out the dimensions of the building (height, weight, depth). This will allow you to determine the volume of the building. Does it taper at the top? (Yes.) Then, you need to estimate the composition of the Chrysler building. Is it mostly steel? Concrete? How much would those components weigh per square inch? Remember the extra step – find out whether you’re considering the building totally empty or with office furniture, people, etc? (If you’re including the contents, you might have to add 20 percent or so to the building’s weight.)

What is the BI analyst interview process like?

Apple

Typically, you’ll get a phone screen call from a recruiter first, followed by a few technical phone interviews with the BI team members. Prior to the onsite interviews, the recruiter will give you an overview of the BI analyst interview process. What comes next are 6 to 8 interviews with members of the BI team (plus some important employees your team works with). Usually, there are 1-on-1 and 2-on-1 interviews.

Be prepared for some whiteboard coding tasks and a lunch interview with your potential manager.

Similar to other companies, the BI analyst interviewers’ questions are centered around different areas and sharing feedback isn’t a common practice. Once that part of the process is over, your interviewers will compare notes. Then, only if they agree that you’re a suitable candidate, you’ll have interviews with the director and the VP of the company. Of course, the latter can reject any candidate at their own discretion. Ultimately, if you’ve made it, you’ll hear from a recruiter a few days later.
However, if it takes longer to receive an answer, a polite nudge for updates won’t harm. As a final note, being familiar with Apple application and operating systems definitely helps (yep, all employees are huge Apple fans). And one more thing – consider presenting yourself as someone who is eager to adapt and learn new things. Why? Here’s what a BI Analyst working for Apple says: “Apple is looking for people who continually show they are willing to challenge themselves and take risks throughout their career.” So keep that in mind.

Facebook

Usually, the BI analyst interviewing process starts with an email or a phone call with a recruiter, followed by a phone screen or an in-person interview. The screening interview is conducted by a coworker and takes about 45 minutes. It consists mostly of coding tasks you must solve using a collaborative editor. Of course, you’ll also answer BI analyst interview questions related to your resume, skills, interests, and motivation. If those go well, you’ll be invited to a longer series of BI analyst interviews at Facebook’s offices.

What’s typical for Facebook BI analyst interviews is that many questions are focused on a deep understanding of their product, so make sure you demonstrate both knowledge and genuine interest in the job.

You’ll be asked questions about issues that the company is facing and how you can help solve them. So, think about metrics when preparing for the Facebook BI analyst interview. Once the interviews are over, everyone you’ve interviewed gets together to decide if you’ll be successful in the BI analyst role. Then all left to do is wait for your recruiter to contact you with feedback from the BI interview.

Amazon

Here’s how the BI analyst hiring process goes at Amazon. It starts with 1 or 2 technical phone screens with a BI team member or, quite surprisingly, a hiring manager (mainly SQL, SAS, and econometric questions, plus some behavioral questions).
If everything goes well, you can expect to have 4 – 5  hour-long onsite interviews, with 1 or 2 teams. But be patient – sometimes the onsite interviews are scheduled a month after you’ve passed the phone screen. Each team focuses on BI analyst interview questions in different areas. Some of them involve statistical modeling and data sets. So, some experience with those will be definitely helpful.
Each BI analyst interviewer can see the others’ evaluations but only after they’ve submitted their own feedback first. Then there’s a meeting where the BI interviewers discuss the candidate’s performance and make the final hiring decision.

Is there anything we’ve skipped mentioning? Yes – the Bar Raiser.

Bar Raisers have rich interviewing experience and hold the supreme veto power in the hiring process. The bar raiser’s final decision can’t be overruled even by the hiring manager. But what exactly does the Bar Raiser assess? Amazon VP of Worldwide People Operations Ardine Williams says one of Amazon’s hiring principles is that anyone they bring in should raise the bar on the company’s internal performance, which means that Bar Raisers are looking for someone who’s better than half of the people currently working there at that level.
That said, if you’re considered one of the top 50%, Amazon’s recruiters should follow up promptly. However, you don’t have to act coy – if you’re still waiting for an answer after a week, a friendly status-update request won’t hurt.

A Final Note on the BI Analyst Interview Questions

Acing the BI analyst interview is not just about being qualified and practicing the BI analyst interview questions in advance. So, as a final note, we’ll share 5 common mistakes BI analyst candidates make (so that you’ll know better and avoid them at your own BI analyst interview):

Memorizing solutions

Cramming is not helpful when it comes to business intelligence interview questions. Instead, focus on quality. Don’t just go through the solutions. Try to learn the logic behind the answer and use it as a stepping stone to improve your approach to new problems.

Too much talking

When asked BI analyst interview questions, try to be as specific and to-the-point as possible. There’s nothing worse than rambling about a topic (especially if it happens to be a BI topic you’re not an expert on). So, try breaking down your reply into meaningful parts and say a few sentences about each. If the interviewer needs more details, they’ll certainly continue with a follow-up question.

Not uttering a word

Nobody can read your thoughts, including job interviewers. So, unless you want to appear as if you’re stuck beyond repair, speak up often. Make sure you guide the interviewer through your thought process when solving a BI analyst problem. This way, even if your explanation isn’t perfect, they will be able to give you a hint and you will arrive at the solution faster. What better way to show good communication skills and willingness to collaborate?

Rushing in coding tasks

Hurry slowly. Trying to solve a coding problem as fast as possible makes you appear nervous and sloppy! So, take your time, approach the task methodically, and test often (unless you’re doing whiteboard coding). This will help you finish the problem quicker. Not to mention you’ll avoid making hasty mistakes you’ll later regret.

In Conclusion

Hope you’ll find this article useful in the preparation for your future BI analyst interviews. Last piece of advice – be persistent. Put in the necessary work, stay enthusiastic in the process, and sooner or later, you’ll reap the rewards.

“What are you going to do with your life?”
People (as in job interviewers and prospective mates) admire direction. Though you may not have Carlton Banks’s master plan, you do want to have some kind of trajectory. If you don’t have one at all, it may be time to head back to the drawing board, or consult a book like What Color is Your Parachute (not a dig, it’s a useful resource).  
“How have you dealt with difficult people?”
This one gives you the chance to do two things: show off your soft skills, and demonstrate that you can do collaboration on a personal, as well as a technical level. The soft skill is important, much as it is in dating. Were you polite to the waiter? Did you tip at the right level? Did you offer to pay, or split, or whatever’s considered financially appropriate? That can make or break you.
As for collaboration, though that word usually connotes the ability to share or annotate something in a BI application, there’s human collaboration that you want to show off, too. The ability to collaborate with the business people (if you’re on the tech side of things, or vice versa) is key. If the IT folks and the business folks don’t know what the others want and need, all the functionalities and tools in the world won’t help you.
“What does our brand mean to you?”
If your date looks into your eyes and says “what makes me attractive,” you wouldn’t say “Any port in a storm, my adorable little Fort Lauderdale!” Likewise, don’t tell the interviewer their brand means a steady paycheck. Know the company, know the brand, and know why you (should) like and want to work for them. Do your research, in the same way you probably checked out that person on Facebook and Twitter (everyone does it, don’t judge).
Just because Winston Zedmore pulled it off doesn’t mean you can. Side note: this role was written for Eddie Murphy.
“What do you want from this position? More generally, from your career?”
Handling this one’s a little like the first question. Again, have a trajectory, even if that trajectory is “I love doing x, and I love the idea of doing it for you.” This is also a place where you can expand on how much you know about the field. Talk about why you love doing x in a way that syncs up with the company’s goals. Do you love approaching a problem from different vantage points, or doing data-driven detective work? Get into it. Say why you like it in a way that lets you show what you know.
“What would you change from your past, if you could change anything?”
Tread lightly, and don’t go for answers like “I wouldn’t care as deeply as I do,” or any glaringly obvious attempts at self-aggrandizement. Like that special someone on the third date, the company interviewing you has pieced enough together by now to know when you’re being fake. Also, try to avoid answers like “I would have definitely gotten that manslaughter/regicide/arson conviction vacated. Boy howdy, am I ever a klutz! Good times, though…”
“What’s an example of a time you went above and beyond in your job?”
Though I cautioned against self-aggrandizement earlier, I wouldn’t rule out a little judicious bragging. If I were being very idealistic, I wouldn’t even call it bragging…at least, if your above-and-beyond was genuine. It’s one thing to talk about how you helped find tens of thousands of dollars by drilling down into several demographics, it’s another entirely to talk about how, I don’t know, Richard Branson poached his style from you.
If you’re in the interview, they’re impressed enough to take a chance on you, again, like the third date. If you’re there, they probably like you, so talking about your accomplishments, in the right way, isn’t just peacocking. Do it tactfully, and with an eye towards how those accomplishments can help you can grow with the other person (or the company), and you’re being genuine.