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Google Professional-Data-Engineer Exam Overview:

Certification Vendor:	Google Cloud
Exam Name:	Google Cloud Certified Professional Data Engineer
Exam Number:	Professional-Data-Engineer
Exam Price:	$200 USD
Available Languages:	English, Japanese
Certificate Validity Period:	2 years
Passing Score:	Not officially published (estimated ~80%)
Real Exam Qty:	50-60
Exam Format:	Multiple-select, Multiple-choice
Exam Duration:	120 minutes
Related Certifications:	Google Cloud Certified Professional Data Engineer
Sample Questions:	Google Professional-Data-Engineer Sample Questions
Exam Way:	Online (remote proctored) or at a testing center (Kryterion)
Pre Condition:	No mandatory prerequisites. Recommended: 3+ years of industry experience including 1+ years designing and managing solutions using Google Cloud.
Official Syllabus URL:	https://cloud.google.com/learn/certification/data-engineer

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In order to be eligible for the exam, candidates must have at least three years of industry experience, with at least one year of experience working with Google Cloud Platform. Professional-Data-Engineer Exam consists of multiple-choice and multiple-select questions and is designed to be completed within two hours. The passing score for the exam is 70%.

Google Professional-Data-Engineer Certification Exam is an exam offered by Google Cloud that provides individuals with the opportunity to demonstrate their proficiency in designing and building data processing systems on Google Cloud Platform. Google Certified Professional Data Engineer Exam certification is intended for individuals who have experience in data processing and have worked with Google Cloud Platform technologies. Google Certified Professional Data Engineer Exam certification exam measures a candidate's ability to design, build, operationalize, secure, and monitor data processing systems.

Google Certified Professional Data Engineer Exam Sample Questions (Q258-Q263):

NEW QUESTION # 258
Your organization has been collecting and analyzing data in Google BigQuery for 6 months. The majority of the data analyzed is placed in a time-partitioned table named events_partitioned. To reduce the cost of queries, your organization created a view called events, which queries only the last 14 days of dat
a. The view is described in legacy SQL. Next month, existing applications will be connecting to BigQuery to read the events data via an ODBC connection. You need to ensure the applications can connect. Which two actions should you take? (Choose two.)

A. Create a service account for the ODBC connection to use for authentication
B. Create a new partitioned table using a standard SQL query
C. Create a new view over events_partitioned using standard SQL
D. Create a Google Cloud Identity and Access Management (Cloud IAM) role for the ODBC connection and shared "events"
E. Create a new view over events using standard SQL

Answer: D,E

NEW QUESTION # 259
You are deploying a new storage system for your mobile application, which is a media streaming service.
You decide the best fit is Google Cloud Datastore. You have entities with multiple properties, some of which can take on multiple values. For example, in the entity 'Movie'the property 'actors'and the property 'tags' have multiple values but the property 'date released' does not. A typical query would ask for all movies with actor=<actorname>ordered by date_releasedor all movies with tag=Comedyordered by date_released. How should you avoid a combinatorial explosion in the number of indexes?

C: Set the following in your entity options: exclude_from_indexes = 'actors, tags' D: Set the following in your entity options: exclude_from_indexes = 'date_published'

A. Option B.
B. Option D
C. Option A
D. Option C

Answer: C

NEW QUESTION # 260
You are working on a niche product in the image recognition domain. Your team has developed a model that is dominated by custom C++ TensorFlow ops your team has implemented. These ops are used inside your main training loop and are performing bulky matrix multiplications. It currently takes up to several days to train a model. You want to decrease this time significantly and keep the cost low by using an accelerator on Google Cloud. What should you do?

A. Stay on CPUs, and increase the size of the cluster you're training your model on.
B. Use Cloud GPUs after implementing GPU kernel support for your customs ops.
C. Use Cloud TPUs after implementing GPU kernel support for your customs ops.
D. Use Cloud TPUs without any additional adjustment to your code.

Answer: B

NEW QUESTION # 261
You have designed an Apache Beam processing pipeline that reads from a Pub/Sub topic. The topic has a message retention duration of one day, and writes to a Cloud Storage bucket. You need to select a bucket location and processing strategy to prevent data loss in case of a regional outage with an RPO of 15 minutes. What should you do?

A. 1. Use a dual-region Cloud Storage bucket.
2. Monitor Dataflow metrics with Cloud Monitoring to determine when an outage occurs.
3. Seek the subscription back in time by 15 minutes to recover the acknowledged messages.
4. Start the Dataflow job in a secondary region.
B. 1. Use a regional Cloud Storage bucket.
2. Monitor Dataflow metrics with Cloud Monitoring to determine when an outage occurs.
3. Seek the subscription back in time by one day to recover the acknowledged messages.
4. Start the Dataflow job in a secondary region and write in a bucket in the same region.
C. 1. Use a multi-regional Cloud Storage bucket.
2. Monitor Dataflow metrics with Cloud Monitoring to determine when an outage occurs.
3. Seek the subscription back in time by 60 minutes to recover the acknowledged messages.
4. Start the Dataflow job in a secondary region.
D. 1. Use a dual-region Cloud Storage bucket with turbo replication enabled.
2. Monitor Dataflow metrics with Cloud Monitoring to determine when an outage occurs.
3. Seek the subscription back in time by 60 minutes to recover the acknowledged messages.
4. Start the Dataflow job in a secondary region.

Answer: D

NEW QUESTION # 262
You are designing a data lake on Google Cloud to store vast amounts of customer interaction data from various sources, such as websites, mobile apps, and social media. You need to ensure that this data, which arrives in different formats, is consistently cataloged and easy for data analysts to discover and use. You also want to perform basic data quality checks and transformations before the data is consumed by downstream applications. You need an automated and managed data governance solution. What should you do?

A. Stream all the data directly into BigQuery, where it is automatically cataloged and governed.
B. Use Cloud Storage and BigQuery as repositories. Use Dataplex Universal Catalog for metadata discovery, data quality checks, and transformations.
C. Use Cloud Storage as the central repository. Use a Cloud Run function to catalog, transform the data, and perform data quality checks.
D. Use Cloud Storage as the central repository. Use Vertex AI to classify and process the data and perform data quality checks.

Answer: B

Explanation:
Dataplex provides a managed data governance solution with a unified catalog, automated metadata discovery, and support for data quality checks and transformations. Using Cloud Storage and BigQuery as repositories allows analysts to discover and consume data consistently across the data lake.

NEW QUESTION # 263
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