BlackFriday 2024! Hurry Up, Grab the Special Discount - Save 25% - Ends In 00:00:00 Coupon code: SAVE25
Welcome to Pass4Success

- Free Preparation Discussions

Google Professional Data Engineer Exam Questions

Exam Name: Google Cloud Certified Professional Data Engineer
Exam Code: Professional Data Engineer
Related Certification(s): Google Cloud Certified Certification
Certification Provider: Google
Actual Exam Duration: 120 Minutes
Number of Professional Data Engineer practice questions in our database: 373 (updated: Nov. 25, 2024)
Expected Professional Data Engineer Exam Topics, as suggested by Google :
  • Topic 1: Designing data processing systems: It delves into designing for security and compliance, reliability and fidelity, flexibility and portability, and data migrations.
  • Topic 2: Ingesting and processing the data: The topic discusses planning of the data pipelines, building the pipelines, acquisition and import of data, and deploying and operationalizing the pipelines.
  • Topic 3: Storing the data: This topic explains how to select storage systems and how to plan using a data warehouse. Additionally, it discusses how to design for a data mesh.
  • Topic 4: Preparing and using data for analysis: Questions about data for visualization, data sharing, and assessment of data may appear.
  • Topic 5: Maintaining and automating data workloads: It discusses optimizing resources, automation and repeatability design, and organization of workloads as per business requirements. Lastly, the topic explains monitoring and troubleshooting processes and maintaining awareness of failures.
Disscuss Google Professional Data Engineer Topics, Questions or Ask Anything Related

Donte

3 days ago
Machine learning questions popped up. Understand the differences between Cloud AI Platform, AutoML, and BigQuery ML, and when to use each.
upvoted 0 times
...

Antonette

10 days ago
I passed the Google Cloud Certified Professional Data Engineer exam, and Pass4Success practice questions were a key resource. One question that I found difficult was related to operationalizing machine learning models. It asked about the best practices for monitoring model performance in production. I wasn't sure of the exact answer, but I still managed to pass.
upvoted 0 times
...

Son

17 days ago
Data governance is important! Familiarize yourself with Cloud DLP for identifying and protecting sensitive data across GCP services.
upvoted 0 times
...

Douglass

19 days ago
Pass4Success rocks! Their questions were so similar to the actual Google Cloud Data Engineer exam. Passed with flying colors!
upvoted 0 times
...

Aliza

24 days ago
The Google Cloud Certified Professional Data Engineer exam was tough, but Pass4Success practice questions made a big difference. A question that puzzled me was about designing data processing systems, specifically on choosing the right storage solution for a high-throughput, low-latency application. Despite my uncertainty, I passed the exam.
upvoted 0 times
...

Javier

1 months ago
Cloud Spanner was a key topic. Know when to choose it over other database options, especially for global, strongly consistent workloads.
upvoted 0 times
...

Shannon

1 months ago
I just cleared the Google Cloud Certified Professional Data Engineer exam, and I owe a lot to Pass4Success practice questions. One challenging question was about building and operationalizing data processing systems. It asked how to optimize a Dataflow job for cost and performance. I wasn't entirely confident in my answer, but I passed the exam nonetheless.
upvoted 0 times
...

Theron

2 months ago
Nailed the GCP Data Engineer cert! Pass4Success materials were a lifesaver. Exam was tough but I was well-prepared.
upvoted 0 times
...

Kristofer

2 months ago
Dataflow came up a lot in my exam. Be prepared to choose the right windowing technique for various streaming scenarios. Time-based vs. count-based windows are crucial!
upvoted 0 times
...

Launa

2 months ago
Passing the Google Cloud Certified Professional Data Engineer exam was a great achievement, thanks to Pass4Success practice questions. There was a tricky question on ensuring solution quality, specifically about implementing data validation checks in a data pipeline. I had to think hard about the best approach, but I still managed to get through the exam successfully.
upvoted 0 times
...

Derick

2 months ago
Just passed the Google Cloud Data Engineer exam! BigQuery questions were frequent. Make sure you understand partitioning and clustering strategies for optimal performance.
upvoted 0 times
...

Verdell

2 months ago
I recently passed the Google Cloud Certified Professional Data Engineer exam, and the Pass4Success practice questions were incredibly helpful. One question that stumped me was about the best practices for operationalizing machine learning models. It asked about the most efficient way to deploy a model using Google Cloud AI Platform. I wasn't entirely sure about the correct answer, but I managed to pass the exam.
upvoted 0 times
...

Freida

3 months ago
Just passed the Google Cloud Data Engineer exam! Thanks Pass4Success for the spot-on practice questions. Saved me tons of time!
upvoted 0 times
...

Vesta

3 months ago
Passing the Google Cloud Certified Professional Data Engineer exam was a great achievement for me, and I owe a big thanks to Pass4Success practice questions for helping me prepare. The exam covered important topics like designing data processing systems and ingesting and processing the data. One question that I found particularly interesting was about data migrations and the challenges involved in moving data between different systems while maintaining data integrity.
upvoted 0 times
...

Lashaunda

4 months ago
My exam experience was challenging but rewarding as I successfully passed the Google Cloud Certified Professional Data Engineer exam with the assistance of Pass4Success practice questions. The topics on designing data processing systems and ingesting and processing the data were crucial for the exam. One question that I remember was about planning data pipelines and ensuring reliability and fidelity in the data processing process.
upvoted 0 times
...

Lon

5 months ago
Achieved Google Cloud Professional Data Engineer certification! Data warehousing was heavily tested. Prepare for scenarios on optimizing BigQuery performance and managing partitioned tables. Review best practices for cost optimization. Pass4Success's practice tests were a lifesaver, closely mirroring the actual exam questions.
upvoted 0 times
...

Eric

5 months ago
Just passed the GCP Data Engineer exam! Big thanks to Pass4Success for their spot-on practice questions. A key topic was BigQuery optimization - expect questions on partitioning and clustering strategies. Make sure you understand how to choose between them based on query patterns. The exam tests practical knowledge, so hands-on experience is crucial!
upvoted 0 times
...

Erasmo

5 months ago
Successfully certified as a Google Cloud Professional Data Engineer! Machine learning questions were tricky. Be ready to design ML pipelines and choose appropriate models. Study BigQuery ML and AutoML thoroughly. Pass4Success's exam dumps were invaluable for my last-minute preparation.
upvoted 0 times
...

Dierdre

5 months ago
I just passed the Google Cloud Certified Professional Data Engineer exam and I couldn't have done it without the help of Pass4Success practice questions. The exam covered topics like designing data processing systems and ingesting and processing the data. One question that stood out to me was related to designing for security and compliance - it really made me think about the importance of data protection in data processing systems.
upvoted 0 times
...

Zack

5 months ago
Just passed the Google Cloud Professional Data Engineer exam! Big data processing was a key focus. Expect questions on choosing the right tools for batch vs. streaming data. Brush up on Dataflow and Pub/Sub. Thanks to Pass4Success for the spot-on practice questions that helped me prepare quickly!
upvoted 0 times
...

saqib

7 months ago
Comment about question 1: If I encountered this question in an exam, I would choose Option D as the correct answer. It effectively handles the challenge of processing streaming data with potential invalid values by leveraging Pub/Sub for ingestion, Dataflow for preprocessing, and streaming the sanitized data into BigQuery. This is the best approach to make sure efficient data handling...
upvoted 1 times
...

anderson

8 months ago
Comment about question 1: If I encountered this question in an exam, I would choose Option D as the correct answer. It effectively handles the challenge of processing streaming data with potential invalid values by leveraging Pub/Sub for ingestion, Dataflow for preprocessing, and streaming the sanitized data into BigQuery. This is the best approach to make sure efficient data handling.
upvoted 1 times
...

Free Google Professional Data Engineer Exam Actual Questions

Note: Premium Questions for Professional Data Engineer were last updated On Nov. 25, 2024 (see below)

Question #1

You are running your BigQuery project in the on-demand billing model and are executing a change data capture (CDC) process that ingests dat

a. The CDC process loads 1 GB of data every 10 minutes into a temporary table, and then performs a merge into a 10 TB target table. This process is very scan intensive and you want to explore options to enable a predictable cost model. You need to create a BigQuery reservation based on utilization information gathered from BigQuery Monitoring and apply the reservation to the CDC process. What should you do?

Reveal Solution Hide Solution
Correct Answer: D

https://cloud.google.com/blog/products/data-analytics/manage-bigquery-costs-with-custom-quotas.

Here's why creating a BigQuery reservation for the project is the most suitable solution:

Project-Level Reservation: BigQuery reservations are applied at the project level. This means that the reserved slots (processing capacity) are shared across all jobs and queries running within that project. Since your CDC process is a significant contributor to your BigQuery usage, reserving slots for the entire project ensures that your CDC process always has access to the necessary resources, regardless of other activities in the project.

Predictable Cost Model: Reservations provide a fixed, predictable cost model. Instead of paying the on-demand price for each query, you pay a fixed monthly fee for the reserved slots. This eliminates the variability of costs associated with on-demand billing, making it easier to budget and forecast your BigQuery expenses.

BigQuery Monitoring: You can use BigQuery Monitoring to analyze the historical usage patterns of your CDC process and other queries within your project. This information helps you determine the appropriate amount of slots to reserve, ensuring that you have enough capacity to handle your workload while optimizing costs.

Why other options are not suitable:

A . Create a BigQuery reservation for the job: BigQuery does not support reservations at the individual job level. Reservations are applied at the project or assignment level.

B . Create a BigQuery reservation for the service account running the job: While you can create reservations for assignments (groups of users or service accounts), it's less efficient than a project-level reservation in this scenario. A project-level reservation covers all jobs within the project, regardless of the service account used.

C . Create a BigQuery reservation for the dataset: BigQuery does not support reservations at the dataset level.

By creating a BigQuery reservation for your project based on your utilization analysis, you can achieve a predictable cost model while ensuring that your CDC process and other queries have the necessary resources to run smoothly.


Question #2

You are migrating your on-premises data warehouse to BigQuery. As part of the migration, you want to facilitate cross-team collaboration to get the most value out of the organization's dat

a. You need to design an architecture that would allow teams within the organization to securely publish, discover, and subscribe to read-only data in a self-service manner. You need to minimize costs while also maximizing data freshness What should you do?

Reveal Solution Hide Solution
Correct Answer: C

To provide a cost-effective storage and processing solution that allows data scientists to explore data similarly to using the on-premises HDFS cluster with SQL on the Hive query engine, deploying a Dataproc cluster is the best choice. Here's why:

Compatibility with Hive:

Dataproc is a fully managed Apache Spark and Hadoop service that provides native support for Hive, making it easy for data scientists to run SQL queries on the data as they would in an on-premises Hadoop environment.

This ensures that the transition to Google Cloud is smooth, with minimal changes required in the workflow.

Cost-Effective Storage:

Storing the ORC files in Cloud Storage is cost-effective and scalable, providing a reliable and durable storage solution that integrates seamlessly with Dataproc.

Cloud Storage allows you to store large datasets at a lower cost compared to other storage options.

Hive Integration:

Dataproc supports running Hive directly, which is essential for data scientists familiar with SQL on the Hive query engine.

This setup enables the use of existing Hive queries and scripts without significant modifications.

Steps to Implement:

Copy ORC Files to Cloud Storage:

Transfer the ORC files from the on-premises HDFS cluster to Cloud Storage, ensuring they are organized in a similar directory structure.

Deploy Dataproc Cluster:

Set up a Dataproc cluster configured to run Hive. Ensure that the cluster has access to the ORC files stored in Cloud Storage.

Configure Hive:

Configure Hive on Dataproc to read from the ORC files in Cloud Storage. This can be done by setting up external tables in Hive that point to the Cloud Storage location.

Provide Access to Data Scientists:

Grant the data scientist team access to the Dataproc cluster and the necessary permissions to interact with the Hive tables.


Dataproc Documentation

Hive on Dataproc

Google Cloud Storage Documentation

Question #3

A web server sends click events to a Pub/Sub topic as messages. The web server includes an event Timestamp attribute in the messages, which is the time when the click occurred. You have a Dataflow streaming job that reads from this Pub/Sub topic through a subscription, applies some transformations, and writes the result to another Pub/Sub topic for use by the advertising department. The advertising department needs to receive each message within 30 seconds of the corresponding click occurrence, but they report receiving the messages late. Your Dataflow job's system lag is about 5 seconds, and the data freshness is about 40 seconds. Inspecting a few messages show no more than 1 second lag between their event Timestamp and publish Time. What is the problem and what should you do?

Reveal Solution Hide Solution
Correct Answer: B

To ensure that the advertising department receives messages within 30 seconds of the click occurrence, and given the current system lag and data freshness metrics, the issue likely lies in the processing capacity of the Dataflow job. Here's why option B is the best choice:

System Lag and Data Freshness:

The system lag of 5 seconds indicates that Dataflow itself is processing messages relatively quickly.

However, the data freshness of 40 seconds suggests a significant delay before processing begins, indicating a backlog.

Backlog in Pub/Sub Subscription:

A backlog occurs when the rate of incoming messages exceeds the rate at which the Dataflow job can process them, causing delays.

Optimizing the Dataflow Job:

To handle the incoming message rate, the Dataflow job needs to be optimized or scaled up by increasing the number of workers, ensuring it can keep up with the message inflow.

Steps to Implement:

Analyze the Dataflow Job:

Inspect the Dataflow job metrics to identify bottlenecks and inefficiencies.

Optimize Processing Logic:

Optimize the transformations and operations within the Dataflow pipeline to improve processing efficiency.

Increase Number of Workers:

Scale the Dataflow job by increasing the number of workers to handle the higher load, reducing the backlog.


Dataflow Monitoring

Scaling Dataflow Jobs

Question #4

You have a BigQuery dataset named "customers". All tables will be tagged by using a Data Catalog tag template named "gdpr". The template contains one mandatory field, "has sensitive data~. with a boolean value. All employees must be able to do a simple search and find tables in the dataset that have either true or false in the "has sensitive data" field. However, only the Human Resources (HR) group should be able to see the data inside the tables for which "hass-ensitive-data" is true. You give the all employees group the bigquery.metadataViewer and bigquery.connectionUser roles on the dataset. You want to minimize configuration overhead. What should you do next?

Reveal Solution Hide Solution
Correct Answer: D

To ensure that all employees can search and find tables with GDPR tags while restricting data access to sensitive tables only to the HR group, follow these steps:

Data Catalog Tag Template:

Use Data Catalog to create a tag template named 'gdpr' with a boolean field 'has sensitive data'. Set the visibility to public so all employees can see the tags.

Roles and Permissions:

Assign the datacatalog.tagTemplateViewer role to the all employees group. This role allows users to view the tags and search for tables based on the 'has sensitive data' field.

Assign the bigquery.dataViewer role to the HR group specifically on tables that contain sensitive data. This ensures only HR can access the actual data in these tables.

Steps to Implement:

Create the GDPR Tag Template:

Define the tag template in Data Catalog with the necessary fields and set visibility to public.

Assign Roles:

Grant the datacatalog.tagTemplateViewer role to the all employees group for visibility into the tags.

Grant the bigquery.dataViewer role to the HR group on tables marked as having sensitive data.


Data Catalog Documentation

Managing Access Control in BigQuery

IAM Roles in Data Catalog

Question #5

You are architecting a data transformation solution for BigQuery. Your developers are proficient with SOL and want to use the ELT development technique. In addition, your developers need an intuitive coding environment and the ability to manage SQL as code. You need to identify a solution for your developers to build these pipelines. What should you do?

Reveal Solution Hide Solution
Correct Answer: C

To architect a data transformation solution for BigQuery that aligns with the ELT development technique and provides an intuitive coding environment for SQL-proficient developers, Dataform is an optimal choice. Here's why:

ELT Development Technique:

ELT (Extract, Load, Transform) is a process where data is first extracted and loaded into a data warehouse, and then transformed using SQL queries. This is different from ETL, where data is transformed before being loaded into the data warehouse.

BigQuery supports ELT, allowing developers to write SQL transformations directly in the data warehouse.

Dataform:

Dataform is a development environment designed specifically for data transformations in BigQuery and other SQL-based warehouses.

It provides tools for managing SQL as code, including version control and collaborative development.

Dataform integrates well with existing development workflows and supports scheduling and managing SQL-based data pipelines.

Intuitive Coding Environment:

Dataform offers an intuitive and user-friendly interface for writing and managing SQL queries.

It includes features like SQLX, a SQL dialect that extends standard SQL with features for modularity and reusability, which simplifies the development of complex transformation logic.

Managing SQL as Code:

Dataform supports version control systems like Git, enabling developers to manage their SQL transformations as code.

This allows for better collaboration, code reviews, and version tracking.


Dataform Documentation

BigQuery Documentation

Managing ELT Pipelines with Dataform


Unlock Premium Professional Data Engineer Exam Questions with Advanced Practice Test Features:
  • Select Question Types you want
  • Set your Desired Pass Percentage
  • Allocate Time (Hours : Minutes)
  • Create Multiple Practice tests with Limited Questions
  • Customer Support
Get Full Access Now

Save Cancel