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Professional Data Engineer Exam (Professional-Data-Engineer) - Google Cloud Exam Questions
Last updated on June 20, 2026
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384
Total Questions
1
Question
When you design a Google Cloud Bigtable schema it is recommended that you _________.
Options
A
Create schema designs that are based on a relational database design
B
Avoid schema designs that require atomicity across rows
C
Avoid schema designs that are based on NoSQL concepts
D
Create schema designs that require atomicity across rows
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2
Question
Your company's data platform ingests CSV file dumps of booking and user profile data from upstream sources into Cloud Storage. The data analyst team wants to join these datasets on the email field available in both the datasets to perform analysis. However, personally identifiable information (PII) should not be accessible to the analysts. You need to de-identify the email field in both the datasets before loading them into BigQuery for analysts. What should you do?
Options
A
1. Create a pipeline to de-identify the email field by using recordTransformations in Cloud DLP
with format-preserving encryption with FFX as the de-identification transformation type.
2. Load the booking and user profile data into a BigQuery table.
with format-preserving encryption with FFX as the de-identification transformation type.
2. Load the booking and user profile data into a BigQuery table.
B
1. Load the CSV files from Cloud Storage into a BigQuery table, and enable dynamic data masking.
2. Create a policy tag with the email mask as the data masking rule.
3. Assign the policy to the email field in both tables. A
4. Assign the Identity and Access Management bigquerydatapolicy.maskedReader role for the
BigQuery tables to the analysts.
2. Create a policy tag with the email mask as the data masking rule.
3. Assign the policy to the email field in both tables. A
4. Assign the Identity and Access Management bigquerydatapolicy.maskedReader role for the
BigQuery tables to the analysts.
C
1. Create a pipeline to de-identify the email field by using recordTransformations in Cloud Data
Loss Prevention (Cloud DLP) with masking as the de-identification transformations type.
2. Load the booking and user profile data into a BigQuery table.
Loss Prevention (Cloud DLP) with masking as the de-identification transformations type.
2. Load the booking and user profile data into a BigQuery table.
D
1. Load the CSV files from Cloud Storage into a BigQuery table, and enable dynamic data masking.
2. Create a policy tag with the default masking value as the data masking rule.
3. Assign the policy to the email field in both tables.
4. Assign the Identity and Access Management bigquerydatapolicy.maskedReader role for the
BigQuery tables to the analysts
2. Create a policy tag with the default masking value as the data masking rule.
3. Assign the policy to the email field in both tables.
4. Assign the Identity and Access Management bigquerydatapolicy.maskedReader role for the
BigQuery tables to the analysts
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3
Question
Your team is responsible for developing and maintaining ETLs in your company. One of your Dataflow jobs is failing because of some errors in the input data, and you need to improve reliability of the pipeline (incl. being able to reprocess all failing data).
What should you do?
What should you do?
Options
A
Add a try… catch block to your DoFn that transforms the data, extract erroneous rows from logs.
B
Add a try… catch block to your DoFn that transforms the data, write erroneous rows to PubSub directly from the DoFn.
C
Add a filtering step to skip these types of errors in the future, extract erroneous rows from logs.
D
Add a try… catch block to your DoFn that transforms the data, use a sideOutput to create a PCollection that can be stored to PubSub later.
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4
Question
To give a user read permission for only the first three columns of a table, which access control method would you use?
Options
A
Predefined role
B
Authorized view
C
Primitive role
D
It's not possible to give access to only the first three columns of a table.
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5
Question
You are developing an application that uses a recommendation engine on Google Cloud. Your solution should display new videos to customers based on past views. Your solution needs to generate labels for the entities in videos that the customer has viewed. Your design must be able to provide very fast filtering suggestions based on data from other customer preferences on several TB of dat
Options
A
Build and train a classification model with Spark MLlib to generate labels. Build and train a second classification model with Spark MLlib to filter results to match customer preferences. Deploy the models using Cloud Dataproc. Call the models from your application.
B
Build an application that calls the Cloud Video Intelligence API to generate labels. Store data in Cloud Bigtable, and filter the predicted labels to match the user’s viewing history to generate preferences.
C
Build and train a complex classification model with Spark MLlib to generate labels and filter the results.
Deploy the models using Cloud Dataproc. Call the model from your application.
Deploy the models using Cloud Dataproc. Call the model from your application.
D
Build an application that calls the Cloud Video Intelligence API to generate labels. Store data in Cloud SQL, and join and filter the predicted labels to match the user’s viewing history to generate preferences.
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