Monday, January 30, 2023

Data Quality Engineer Interview Questions

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Questions About Experience And Background In Quality Engineer

Data Engineer Interview Questions | Data Engineer Interview Preparation | Intellipaat

These questions allow interviewers to evaluate your previous work experience and education that makes you a preferable candidate for a quality engineering position within their company:

  • How do you believe your previous education has helped prepare you for a role in quality engineering?

  • What previous roles have you worked in that make you eligible for a quality engineering position?

  • Can you tell me about a time you disagreed with your company’s use of a particular raw material for a product? How did you approach the situation?

  • What is your greatest accomplishment in quality engineering or a related role so far?

  • What is your biggest regret working in quality engineering so far?

  • Have you ever written a quality report as part of your job duties? What should you include in one?

  • Have you ever hosted a meeting where you have to make a presentation and answer team questions?

  • In your previous role, did you gain experience working as a leader of a team or department? What skills did you learn that you can implement here?

  • What testing measures did you put into practice at your previous place of employment?

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How Would You Define What Data Engineering Is

Sample answer:

Data engineering is the process of making transformations to and cleansing data. It also involves profiling and aggregating data. In other words, data engineering is all about data collection and transforming raw data gathered from several sources into information that is ready to be used in the decision-making process.

What Is A Decorator

A decorator is a tool in Python which allows programmers to wrap another function around a function or a class to extend the behavior of the wrapped function without making any permanent modifications to it. Functions in Python are first-class objects, meaning functions can be passed or used as arguments. A function works as the argument for another function in a decorator, which you can call inside the wrapper function.

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Can You Explain What A Star Schema Is

Sample answer:

A star schema, also referred to as the star join schema, is a basic schema that is used in data warehousing.

Star schemas are called like this because the structure appears similar to a star that features tables, both fact and associated dimension ones. These schemas are ideal for vast quantities of data.

Amazon Data Engineer Interview Questions

Interview, Employee Testing and Selection, Assignment Help

Youre given an IP address as an input in the form of a string. How would you find out if it is a valid IP address or not?

It is the most common question asked during coding interviews, and the answer is simple. You are going to split the string on . and create multiple checks to determine the validity of the IP address.

def is_valid:    ip = ip.split    for i in ip:        if  >  3 or int <  0 or                          int >  255):            return False        if len >  1 and int == 0:            return False        if  >  1 and int != 0 and            i == '0'):            return False    return TrueA = "255.255.11.135"B = "255.050.11.5345"

The A IP is valid and it returns True, whereas B returns False as it has 4 digits after the dot.

print)> > >  Trueprint)> > >  False

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Should Qa’s Resolve Production Issues

You might have varying opinions on this one, but I’d advise you to answer “Yes”.

Its often good for the QA to be involved in solving production issues. They should, when possible, write test cases and try to find the issues. By getting involved, the QA is minimizing the number issues in the final product.

What Are The Roles And Responsibilities Of Data Engineer

Some of the roles and responsibilities of a data engineer are

  • Create and implement ETL data pipeline for a variety of clients in various sectors.

  • Generate accurate and useful data-driven solutions using data modeling and data warehousing techniques.

  • Interact with other teams and help them by delivering relevant datasets for analysis.

  • Build data pipelines for extraction and storage tasks by employing a range of big data engineering tools and various cloud service platforms.

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Explain The Difference Between Structured Data And Unstructured Data

Data engineers must turn unstructured data into structured data for data analysis using different methods for transformation. First, you can explain the difference between the two.

Structured data is made up of well-defined data types with patterns that make them easily searchable, whereas unstructured data is a bundle of files in various formats, such as videos, photos, texts, audio, and more.

Unstructured data exists in unmanaged file structures, so engineers collect, manage, and store it in database management systems turning it into structured data that is searchable. Unstructured data might be inputted through manual entry or batch processing with coding, so ELT is the tool used to transform and integrate data into a cloud-based data warehouse.

Second, you can share a situation in which you transformed data into a structured format, drawing from learning projects if youâre lacking professional experience.

What Do You Think Are Some Advantages Of Manual Testing

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Here are a few advantages of manual manual testing that you can talk about:

  • It can be less expensive compared to automated testing
  • For new teams or people new to QA, it can be easier to learn how to run manual test, so it can be rolled out faster
  • To a similar point, manual testing can be great on short-term projects when test scripts won’t be re-used many times
  • You can analyze the product from the point of view of the end user when doing manual testing
  • Testing the GUI can feel more intuitive and lead to more accurate results when doing a manual test the visual accessibility and preferences can be tricky to automate

Here’s an article where you can read more about the pros and cons of manual testing and automated testing.

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What Is A Memorable Data Pipeline Performance Issue That You Solved

This question will give you insight into the candidates past experiences with data pipeline implementation and how they were able to improve performance. Performance issues in a data pipeline can not only slow down the gathering of data, but can disrupt and slow down data analysis. This can have a direct impact on business decisions.

Here are some examples of experiences candidates could discuss:

  • how they improved the performance of a specific SQL query
  • how they upgraded a database from one type to another
  • how they reduced the time it took to run a set of queries
  • how they improved performance of importing or exporting of data
  • how they improved retrieval of data from a backup system

If you want to know if the candidate has ideas on how to improve the performance of your data pipeline, also ask this as a question!

You can also ask a candidate how they have solved issues with malformed data and incorrect taxonomies.

Check out our entire set of software development interview questions to choose and practice those which fit your job search situation best:

Youre Tasked With Building A Notification System For A Reddit

Many case study questions for data engineers are similar to database design questions. With a question like this, start with clarifying questions. You might want to know goals for the notification system, user information, and the types of notifications that are being sent.

Then, youll want to make assumptions. A basic solution might could start with the notifications:

  • Trigger-based notifications – This might be something like an email notification for comment replies on a submitted post.
  • Scheduled notifications – This might be a targeted push notification for new content. These are notifications designed to drive engagement.

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What Are The Key Components Of A Quality Plan

Aside from technical works, you should be familiar with documenting tasks.

Tip #1: Be specific

Tip #2: Show confidence

Sample Answer:

Some of the things that should be included are the team members and the responsibilities assigned. The quality standards of the product or service should also be included clearly. The other things include the testing and quality assurance, the risks, design control, as well as control development.

Data Quality Engineer Interview Questions And Answers

Industrial Engineering and Production Management Questions and Answers ...

Learn what skills and qualities interviewers are looking for from a data quality engineer, what questions you can expect, and how you should go about answering them.

As data becomes an increasingly important commodity in business, more and more companies are looking to hire data quality engineers. These professionals are responsible for ensuring the accuracy and completeness of data sets, as well as developing and implementing strategies for data cleansing and data governance.

If youre looking to interview for a data quality engineer position, its important to be prepared for questions that will test your knowledge of data management concepts and your ability to think critically about data-related problems. In this article, well provide you with a list of sample questions and answers that will help you shine in your interview.

Are you familiar with the term data cleansing? What is its purpose?

This question is an opportunity to show your interviewer that you understand the basic terminology of data quality engineering. Use this question as a chance to demonstrate your knowledge and understanding of the field by defining the term and explaining its purpose.

What are some of the most common types of data errors you have encountered in your previous roles as a data quality engineer?

How would you rate your technical writing skills as a data quality engineer? What examples can you provide?

What is your process for verifying the accuracy of data?

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Do You Have Any Experience With Data Modeling

Unless you are interviewing for an entry-level role, you will likely be asked this question at some point during your interview. Start with a simple yes or no. Even if you dont have experience with data modeling, youll want to be at least able to define it: the act of transforming and processing fetched data and then sending it to the right individual. If you are experienced, you can go into detail about what youve done specifically. Perhaps you used tools like Talend, Pentaho, or Informatica. If so, say it. If not, simply being aware of the relevant industry tools and what they do would be helpful.

What Are The Key Differences Between Namenode And Datanode In Hadoop

Following is the list of key differences between NameNode and DataNode in Hadoop:

NameNode DataNode
NameNodes are the centerpiece of HDFS. They are used to control and manage the HDFC. They are known as the Master in the Hadoop cluster. DataNodes are used to store the actual business data in HDFS. They are also known as the Slave in the Hadoop cluster.
NameNode only stores the metadata of actual data. It acts as the directory tree of all files in the file system and tracks them across the cluster. For example, filename, path, no. of data blocks, block IDs, block location, no. of replicas, slave-related configuration, etc. DataNode acts as the actual worker node where Read/Write/Data processing is handled.
NameNode is responsible for constructing the file from blocks as it knows the list of the Blocks and their location for any given file in HDFS. DataNode makes a constant communication with NameNode to do the job.
NameNode plays a critical role in HDFS when the NameNode is down, the HDFS/Hadoop cluster cannot be accessed and is considered down. DataNode is not so important as when it is down. It does not affect the availability of the data or the cluster. NameNode will arrange replication for the blocks managed by the DataNode that is not available.
NameNode is generally configured with a lot of memory because the block locations are held in the main memory. DataNode is generally configured with a lot of hard disk space because the actual data is stored in the DataNode.

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Are You Comfortable With Calling Together Meetings To Discuss Specific Product Quality Issues As Necessary

In the event of an emergency, a quality engineer may need to speak with the rest of the team regarding certain quality control issues. In some environments, this is not even done in an emergency. A good candidate should be comfortable with public speaking and collaborating, especially as a part of a weekly or monthly report. Knowing that the potential candidate has experience in this field can be invaluable.

What to look for in an answer:

  • Experience with public speaking
  • Experience addressing quality control issues in the past
  • Ability to adhere to the companys meeting etiquette

Example:

I have experience with leading meetings around quality control issues at my previous employment, and my meetings have resulted in immediate material rollbacks and improvements.

What Did You Do In Your Last Project

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There are no clear answers, only guidelines, for this one. It’s common for interviewers to ask about your career trajectory and previous projects, so make a quick list of points in advance so you can speak to the projects that you think best represent your work.

The biggest piece of advice I can give is to answer as honestly as possible. Dont exaggerate or undervalue your contribution in previous teams. Tell them what your day-to-day role was, what tools you used and how the QA testing went.

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What Is Your Approach To Developing A New Analytical Product As A Data Engineer

The hiring managers want to know your role as a data engineer in developing a new product and evaluate your understanding of the product development cycle. As a data engineer, you control the outcome of the final product as you are responsible for building algorithms or metrics with the correct data.

Your first step would be to understand the outline of the entire product to comprehend the complete requirements and scope. Your second step would be looking into the details and reasons for each metric. Think about as many issues that could occur, and it helps you to create a more robust system with a suitable level of granularity.

What Is Data Modelling Do You Understand Different Data Models

Data Modelling is the initial step towards data analysis and database design phase. Interviewers want to understand your knowledge. You can explain that is the diagrammatic representation to show the relation between entities. First, the conceptual model is created, followed by the logical model and, finally, the physical model. The level of complexity also increases in this pattern.

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What Are The Design Schemas Of Data Modeling

Design schemas are fundamental to data engineering, so try to be accurate while explaining the concepts in everyday language. There are two schemas: star schema and snowflake schema.

Star schema has a fact table that has several associated dimension tables, so it looks like a star and is the simplest type of data warehouse schema. Snowflake schema is an extension of a star schema and adds additional dimension tables that split the data up, flowing out like a snowflakeâs spokes.

Why Are You Interested In This Job And Why Should We Hire You

4 Red Herring Question Examples to Improve Research Quality

It is a fundamental data engineer interview question, but your answer can set you apart from the rest. To demonstrate your interest in the job, identify a few exciting features of the job, which makes it an excellent fit for you and then mention why you love the company.

For the second part of the question, link your skills, education, personality, and professional experience to the job and company culture. You can back your answers with examples from previous experience. As you justify your compatibility with the job and company, be sure to depict yourself as energetic, confident, motivated, and culturally fit for the company.

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Mention Some Advantages Of Using Numpy Arrays Over Python Lists

  • NumPy arrays take up less space in memory than lists.

  • NumPy arrays are faster than lists.

  • NumPy arrays have built-in functions optimized for various techniques such as linear algebra, vector, and matrix operations.

  • Lists in Python do not allow element-wise operations, but NumPy arrays can perform element-wise operations.

Skipping The Mock Interview

Are you so deep into your interview preparation process that youve cut all ties with the outside world? Big mistake! Snap out of it now, call a fellow data engineer and ask them to do a mock interview with you. Every interview has a performance side to it, and just imagining how youre going to act or sound wouldnt give you a realistic idea. So, while youre doing the mock interview, pay special attention to your body language and mannerisms, as well as to your tone of voice and pace of speech. Youll be amazed by the insight youre going to get!

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Differentiate Between In And Between Operators

The BETWEEN operator in SQL tests if a particular expression lies between a range of values. The values can be in the form of text, dates, or numbers. You can use the BETWEEN operator with SELECT, INSERT, UPDATE, and DELETE statements. In a query, the BETWEEN condition helps to return all values that lie within the range. The range is inclusive. The syntax is of BETWEEN is as follows:

SELECT column_name

FROM table_name

WHERE column_name BETWEEN value1 AND value2

The IN operator tests whether an expression matches the values specified in a list of values. It helps to eliminate the need of using multiple OR conditions. NOT IN operator may exclude certain rows from the query return. IN operator may also be used with SELECT, INSERT, UPDATE, and DELETE statements. The syntax is:

SELECT column_name

WHERE column_name IN

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