Google Professional Data Engineer Interview Questions

By gathering, manipulating, and distributing data, the Google Professional Data Engineer certification aims to expand your knowledge of data-driven decision-making. You will be able to design, implement, operationalize, secure, and monitor data processing systems with a focus on security and compliance, scalability and efficiency, dependability and fidelity, and flexibility and portability after completing this certification.

Furthermore, the Professional Data Engineer exam measures your abilities to design, implement, and operationalize data processing systems, as well as operationalize machine learning models and assure solution quality. It is the most sought-after accreditation. We’ve compiled a list of questions to assist in your interview preparation.

Advanced Interview Questions

What is your experience with Big Data technologies such as Hadoop, Spark, and Hive?

I have extensive experience with Big Data technologies such as Hadoop, Spark, and Hive. I have been working with these technologies for several years and have been involved in several large-scale projects utilizing these technologies.

Hadoop is an open-source software framework that enables the processing and storage of large data sets. I have worked with Hadoop’s core components such as HDFS (Hadoop Distributed File System) and MapReduce, which allows for efficient data processing and storage.

Spark is an open-source data processing framework that provides an in-memory processing engine and supports batch processing, streaming, and machine learning. I have been using Spark for large-scale data processing and have worked on integrating Spark with Hadoop to provide a unified data processing platform.

Hive is an open-source data warehousing system that provides a SQL-like interface for querying and analyzing large data sets. I have been using Hive for data warehousing and have been involved in the design and implementation of Hive-based data warehousing solutions for several clients.

In addition to my experience with these technologies, I have also been involved in the development of custom applications utilizing these technologies to meet specific client requirements. I have also been involved in performance tuning and optimization of these technologies to ensure efficient and effective data processing.

Overall, my experience with Big Data technologies has provided me with the knowledge and skills necessary to effectively design, implement, and maintain large-scale data processing and storage solutions.

What is your experience with data warehousing and business intelligence?

I have worked on several projects that involved designing and implementing data warehousing solutions that could be used to store and process large amounts of data in real-time. These data warehouses were built using technologies like Google BigQuery, Amazon Redshift, and Snowflake, among others.

In addition to designing data warehouses, I have also worked on several business intelligence projects. These projects involved building data pipelines that could extract data from various sources, transform the data into a usable format, and then load the data into a data warehouse for analysis. I have also worked on building interactive dashboards and reports that can be used by business analysts to gain insights into their data. These dashboards were built using technologies like Tableau, Looker, and Google Data Studio, among others.

In my experience, data warehousing and business intelligence are crucial components in any organization’s data strategy. They provide the foundation for data-driven decision making and help organizations make informed decisions based on data. I have seen firsthand the impact that these solutions can have on a business and I am passionate about helping organizations leverage their data to achieve their goals.

Can you explain how you would optimize a large-scale data pipeline?

As a Google Professional Data Engineer, optimizing a large-scale data pipeline requires a combination of strategies and techniques to ensure efficient and effective processing of data. Here are the steps I would take to optimize a large-scale data pipeline:

1. Data collection: The first step in optimizing a data pipeline is to ensure that data is collected efficiently and accurately. This can be done by using parallel data collection techniques, such as multi-threading, to reduce data collection time and reduce the load on the data source.
2. Data pre-processing: Data pre-processing is critical to optimizing a data pipeline as it can help to remove irrelevant data, standardize data format, and resolve data quality issues. This step can be optimized by using tools such as Apache Spark to process data in parallel, reducing processing time.
3. Data storage: Data storage is an important factor in optimizing a data pipeline as it affects the speed of data retrieval and processing. To optimize data storage, I would use a data warehousing solution such as Google BigQuery, which provides fast and scalable data retrieval and processing capabilities.
4. Data processing: Data processing can be optimized by using tools such as Apache Spark, which can process data in parallel, reducing processing time. Additionally, I would use machine learning algorithms to identify patterns and trends in the data, reducing the amount of manual processing required.
5. Data delivery: The final step in optimizing a data pipeline is to ensure that data is delivered efficiently and effectively to its intended destination. This can be achieved by using a distributed data delivery solution, such as Apache Kafka, which provides fast and reliable data delivery.

In conclusion, optimizing a large-scale data pipeline requires a combination of techniques to ensure efficient data collection, pre-processing, storage, processing, and delivery. By leveraging tools such as Apache Spark, Google BigQuery, and Apache Kafka, I would be able to optimize a data pipeline and ensure that data is processed and delivered in a timely and effective manner.

How do you handle data quality issues, such as missing or corrupted data?

I take data quality very seriously and understand that missing or corrupted data can have significant consequences for the accuracy and reliability of my analyses and insights. Therefore, I employ a multi-step approach to ensure that I am handling data quality issues in an effective and efficient manner.

1. Data Cleaning: Before working with any data set, I ensure that it is properly cleaned and formatted. This includes identifying and removing any duplicates, filling in missing values, and correcting any inconsistencies in the data. I use a combination of automated scripts and manual inspection to ensure that the data is clean and ready for analysis.
2. Data Validation: Once the data is cleaned, I validate the data to ensure that it is accurate and free of any errors. This includes checking for outliers, verifying that data is within a specific range, and checking for any correlations between data points. I use a variety of tools, such as data visualization and statistical analysis, to validate the data.
3. Data Backup: To ensure that my data is secure, I maintain multiple backups of my data sets, both in the cloud and on-premises. This enables me to quickly restore any data in case of an issue with the primary data set.
4. Data Monitoring: I continuously monitor the data to ensure that there are no new quality issues that arise. I use data monitoring tools to detect any anomalies in the data and take corrective actions as needed.
5. Documentation: I maintain detailed documentation of all data quality issues, the steps I took to address them, and any resulting insights or actions. This helps me keep track of the data quality issues that I encounter and allows me to easily identify any recurring issues.

In conclusion, handling data quality issues is a critical aspect of my role as a Google Professional Data Engineer. By employing a multi-step approach, including data cleaning, validation, backup, monitoring, and documentation, I ensure that my data is accurate and reliable, allowing me to deliver insights and solutions that are grounded in high-quality data.

Can you describe your experience with NoSQL databases such as Cassandra or MongoDB?

I have used both Cassandra and MongoDB in various data storage and retrieval projects, where the traditional RDBMS was not suitable due to the large volume of data and high concurrency requirements.

My experience with Cassandra began when I was working on a project that involved capturing and storing real-time data from multiple sources. Cassandra provided excellent performance and scalability, allowing me to easily handle a large volume of data and concurrently process millions of transactions. Additionally, Cassandra’s masterless architecture allowed me to scale out and distribute data across multiple nodes, providing high availability and resilience.

I have also worked with MongoDB in several projects, where I was required to store and process structured and unstructured data. MongoDB’s flexible data model and easy-to-use query language allowed me to quickly retrieve and aggregate data, making it an ideal solution for complex data-intensive projects. MongoDB’s scalability and robustness were also essential for ensuring the availability of data even in the case of node failures.

Overall, my experience with Cassandra and MongoDB has given me a deep understanding of NoSQL databases and the ability to work with them effectively. I have also developed expertise in data modeling, data ingestion, data processing, and data retrieval using these databases, making me well equipped to tackle complex data challenges.

How would you implement a real-time streaming data pipeline?

I would implement a real-time streaming data pipeline using the following steps:

1. Determine the source of data: The first step would be to determine the source of the data. This could be a website, a sensor, or a database.
2. Ingestion: Next, I would use a tool such as Apache Kafka, Apache Flume, or Google Cloud Dataflow to ingest the data into the pipeline. This would involve configuring the ingestion tool to collect data from the source, format it appropriately, and stream it into the pipeline.
3. Data Processing: The next step would be to process the data as it streams in. This would involve using tools such as Apache Spark Streaming, Google Cloud Dataflow, or Apache Flink to apply data transformations, such as filtering, aggregation, and machine learning algorithms, in real-time.
4. Data Storage: The processed data would then be stored in a data storage solution such as Google BigQuery, Apache Cassandra, or Amazon S3.
5. Data Analysis: Finally, the processed data would be analyzed using tools such as Google BigQuery, Google Data Studio, or Apache Superset to generate insights and reports in real-time.

In order to ensure that the pipeline is scalable, reliable, and secure, I would also implement best practices such as data partitioning, data sharding, and data encryption. Additionally, I would set up monitoring and alerting mechanisms to ensure that any issues with the pipeline are detected and addressed in a timely manner.

What is your experience with cloud platforms such as Google Cloud Platform or AWS?

I have been working with GCP for several years and have been involved in several large-scale data engineering projects on the platform.

GCP offers a wide range of data management and analytics services such as BigQuery, Cloud Storage, Cloud SQL, and Dataflow. These services are highly scalable and flexible, making them ideal for organizations looking to store, process, and analyze large amounts of data.

I have also worked with Amazon Web Services (AWS) on a few projects. AWS is another popular cloud platform that offers similar data management and analytics services to GCP.

In my experience, both GCP and AWS are powerful and versatile platforms that offer a range of services and tools to help organizations manage and process data at scale. However, I have found that GCP’s user interface and documentation are more user-friendly and easier to navigate compared to AWS. Additionally, GCP also offers a more seamless integration with other Google services such as Google Analytics and Google AdWords.

In conclusion, my experience with cloud platforms such as GCP and AWS has been extremely positive and has provided me with the necessary skills and knowledge to help organizations build and maintain large-scale data infrastructure.

How do you handle data security and privacy concerns?

The following are the steps I take to ensure the security and privacy of data:

1. Data Encryption: I ensure that all sensitive data is encrypted both at rest and in transit. This protects the data from unauthorized access and reduces the risk of data breaches.
2. Access Management: I implement robust access management systems that control who has access to the data and what they can do with it. I use unique user IDs, passwords, and permissions to prevent unauthorized access to sensitive data.
3. Data Backup: I implement regular data backups to ensure that data is not lost in the event of a system failure. Backups are encrypted and stored in secure, off-site locations.
4. Data Retention Policy: I establish a data retention policy to ensure that data is not kept for longer than necessary. Old data that is no longer needed is securely deleted.
5. Network Security: I implement firewalls, intrusion detection systems, and other security measures to protect the network from cyber threats.
6. Compliance: I ensure that the data management practices meet all relevant data privacy and security regulations, such as the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA).
7. Monitoring: I regularly monitor the systems to identify any security threats or vulnerabilities and take immediate action to address them.

In conclusion, as a Google Professional Data Engineer, I understand the importance of data security and privacy, and I take all necessary measures to protect sensitive data.

Can you walk us through a data project you have worked on and the technologies you used?

As a Google Professional Data Engineer, I have worked on several data projects throughout my career. One project that stands out was a data analysis and visualization project for a large e-commerce company. The goal of this project was to help the company understand their customer behavior and identify key trends in customer behavior to improve customer retention and increase sales.

The first step in this project was to gather the required data from various sources, including the company’s database, customer surveys, and customer feedback. The data was then cleaned, pre-processed and transformed into a structured format to be used for analysis and visualization.

To store the large amount of data, I used Google Cloud Storage, which provided a scalable, secure, and cost-effective solution for storing data. The data was then processed using Apache Beam, which is an open-source, distributed, data processing framework that provides a unified programming model for both batch and streaming data.

For analysis, I used Google BigQuery, which is a web service from Google that is used for handling or analyzing big data. BigQuery provides real-time insights by performing quick and complex queries on petabyte-scale datasets. The data was then visualized using Google Data Studio, which is a data visualization tool that allows you to create interactive reports and dashboards from various data sources.

Finally, the insights from the analysis and visualization were shared with the company’s stakeholders through a custom dashboard that was created in Data Studio. This dashboard provided key metrics and insights into customer behavior, such as customer purchase history, product usage, and customer feedback.

In conclusion, the technologies used in this project included Google Cloud Storage, Apache Beam, Google BigQuery, and Google Data Studio. These tools provided a powerful, scalable, and cost-effective solution for data analysis and visualization, enabling the company to gain valuable insights into customer behavior and improve customer retention and sales.

Can you explain how you would handle data migration from one platform to another?

As a Google Professional Data Engineer, I would handle data migration from one platform to another by following the following steps:

1. Assessment: The first step would be to assess the source and target platforms, their data structures, and the volume of data to be migrated. This would help me determine the complexity of the migration and the resources required.
2. Plan: Based on the assessment, I would create a plan that would outline the steps required to migrate the data, including data extraction from the source, data transformation, and data loading into the target platform. The plan should include a timeline, a list of dependencies, and the roles and responsibilities of the different stakeholders involved in the migration.
3. Data Extraction: The next step would be to extract the data from the source platform. This would involve creating data extraction scripts and configuring the data extraction process. The data should be extracted in a format that can be easily transformed and loaded into the target platform.
4. Data Transformation: The extracted data would then be transformed to match the data structure of the target platform. This may involve cleaning the data, removing duplicates, and transforming the data into the appropriate format. The data transformation process should be automated to reduce the risk of human error and to increase the speed and efficiency of the migration.
5. Data Loading: The transformed data would then be loaded into the target platform. This may involve configuring the data loading process and testing the data to ensure that it has been loaded correctly.
6. Verification: The final step would be to verify the data migration. This would involve comparing the data in the source and target platforms to ensure that all data has been successfully migrated. If any errors are detected, they should be corrected and the migration process repeated until the data migration is complete.

In conclusion, data migration from one platform to another requires a well-planned and executed process to ensure the data is migrated accurately and efficiently. As a Google Professional Data Engineer, I would use my technical skills and experience to ensure the data migration process is successful.

Basic Interview Questions

1. Explain the concept of data engineering.

In the field of big data, the term “data engineering” refers to the process of collecting and analyzing data. This involves transforming raw data into valuable insights that can be used to make informed decisions.

2. What is the concept of data modeling?

Data modeling is a visual technique used to represent complex software designs in a way that is easily understood. It involves creating a conceptual representation of data objects and their relationships to other data objects.

3. When Block Scanner detects a compromised data block, what happens next?

When the Block Scanner detects a compromised data block, it notifies the NameNode, which then initiates the process of constructing a new replica from a non-corrupted block replica. If the replication count matches the replication factor, the compromised data block will not be removed.

4. How do you go about deploying a big data solution?

To deploy a big data solution, one should first collect data from various sources and store it in a NoSQL database or HDFS. Computing frameworks such as Pig, Spark, and MapReduce can then be used to analyze the data and derive insights.

5. When it comes to Data Modeling, what are some of the architecture schemas that are used?

When creating data models, two commonly used schema types are the Star schema and the Snowflake schema. These schemas help to organize and structure data in a way that is efficient and easily accessible.

6. What makes structured data different from unstructured data?

Parameters	Structured Data	Unstructured Data
Storage Method	DBMS	Most of it unmanaged
Protocol Standards	ODBC, SQL, and ADO.NET	XML, CSV, SMSM, and SMTP
Scaling	Schema scaling is difficult	Schema scaling is very easy
Example	An ordered text dataset file	Images, videos, etc.

7. In a nutshell, what is Star Schema?

Star Schema is a basic schema in Data Warehousing that has a star-shaped structure with fact tables and related dimension tables, making it ideal for managing large amounts of data.

8. What is Snowflake Schema, in brief?

Snowflake Schema is an extension of the star schema that becomes more complex as more dimensions are added. It is named after its snowflake-like shape and involves normalizing data into more tables.

9. What are some of the methods of Reducer()?

The three main methods of reducer:

• setup(): This primarily configures input data parameters and cache protocols.
• cleanup(): This method removes the temporary files stored.
• reduce(): The method is called one time for every key, and it happens to be the single most important aspect of the reducer on the whole.

10. What do you think a Data Engineer’s main responsibilities are?

A Data Engineer is in charge of a variety of tasks. Here are a few of the most important:

• Pipelines for data inflow and processing
• Keeping data staging areas up to date
• ETL data transformation activities are my responsibility.
• Doing data cleansing and redundancy elimination
• Creating native data extraction methods and ad-hoc query construction operations

11. What are some of the technologies and skills required of a Data Engineer?

The following are the main technologies that a Data Engineer should be familiar with:

• Mathematics (probability and linear algebra)
• Summary statistics
• Machine Learning
• R and SAS programming languages
• Python
• SQL and HiveQL

12. What does Rack Knowledge imply?

Rack Knowledge is a technique used by NameNode to optimize network traffic by reading or writing data from the file closest to the rack from where the request was made.

13. What is Metastore’s purpose in Hive?

Metastore is used to store schema and Hive tables as well as metadata like descriptions and mappings for later retrieval from an RDMS.

14. What are the different components in the Hive data model?

Following are some of the components in Hive:

• Buckets
• Tables
• Partitions

15. Is it possible to make more than one table for a single data file?

Yes, a single data file can have multiple tables as Hive’s metastore contains schemas that make it easy to retrieve data for corresponding tables.

16. List various complex data types/collection supported by Hive

Hive supports the following complex data types:

• Map
• Struct
• Array
• Union

17. In Hive, what is SerDe?

• Serialization and Deserialization in Hive are referred to as SerDe. It is the operation that occurs as records and then passes through Hive tables.
• The Deserializer takes a record and transforms it into a Hive-compatible Java object.
• The Serializer now takes this Java object and transforms it into an HDFS-compatible format. HDFS takes over the storage role later.

18. Explain how .hiverc file in Hive is used?

In Hive, the .hiverc file is used as an initialization file that is loaded first when we start Hive’s Command Line Interface (CLI). It allows us to set the initial values of parameters.

19. Is it possible to create more than one table in Hive for a single data file?

Yes, we can have many table schemas for the same data file. In Hive Metastore, Hive saves schema. We may get different outputs from the same data using this design.

20. Explain different SerDe implementations available in Hive

In Hive, there are several SerDe implementations. You may even create your own SerDe implementation from scratch. Here are a few well-known SerDe implementations:

• OpenCSVSerde
• RegexSerDe
• DelimitedJSONSerDe
• ByteStreamTypedSerDe

21. List out objects created by create statement in MySQL.

Objects created by create statement in MySQL are as follows:

• Database
• Index
• Table
• User
• Procedure
• Trigger
• Event
• View
• Function

22. How to see the database structure in MySQL?

In order to see database structure in MySQL, you can use DESCRIBE command. Syntax of this command is DESCRIBE Table name;.

23. What is the difference between a Data Warehouse and a Database, in a nutshell?

When it comes to Data Warehousing, the main emphasis is on using aggregation functions, conducting calculations, and choosing data subsets for processing. The primary use of databases is for data manipulation, deletion, and other similar tasks. When dealing with any of these, speed and reliability are crucial.

24. What are the functions of Secondary NameNode?

Following are the functions of Secondary NameNode:

• FsImage which stores a copy of EditLog and FsImage file.
• NameNode crash: If the NameNode crashes, then Secondary NameNode’s FsImage can be used to recreate the NameNode.
• Checkpoint: It is used by Secondary NameNode to confirm that data is not corrupted in HDFS.
• Update: It automatically updates the EditLog and FsImage file. It helps to keep FsImage file on Secondary NameNode updated

25. What do you mean by Rack Awareness?

In Haddop cluster, Namenode uses the Datanode to improve the network traffic while reading or writing any file that is closer to the nearby rack to Read or Write request. To obtain rack information, Namenode keeps track of each DataNode’s rack id. In Hadoop, this idea refers to as Rack Awareness.

Final Words:

We have covered all the important questions for a Google Professional Data Engineer Exam on examination interview. You can also, try out free practice test and get the best of them. It will also, help you in giving a better understanding of the examination. You can also, check Google Professional Data Engineer Exam online training for further knowledge.