Online Access Free Google.Professional-Data-Engineer.v2025-08-26.q176 Practice Test (Page 29)

Professional-Data-Engineer Exam Question 136

You've migrated a Hadoop job from an on-premises cluster to Dataproc and Good Storage. Your Spark job is a complex analytical workload fiat consists of many shuffling operations, and initial data are parquet toes (on average 200-400 MB size each) You see some degradation in performance after the migration to Dataproc so you'd like to optimize for it. Your organization is very cost-sensitive so you'd Idee to continue using Dataproc on preemptibles (with 2 non-preemptibles workers only) for this workload. What should you do?

A.Increase the see of your parquet files to ensure them to be 1 GB minimum

B.Switch from HDDs to SSDs. copy initial data from Cloud Storage to Hadoop Distributed File System (HDFS) run the Spark job and copy results back to Cloud Storage

C.Switch to TFRecords format (appr 200 MB per We) instead of parquet files

D.Switch from HODs to SSDs override the preemptible VMs configuration to increase the boot disk size

Professional-Data-Engineer Exam Question 137

You work for a shipping company that uses handheld scanners to read shipping labels. Your company has strict data privacy standards that require scanners to only transmit recipients' personally identifiable information (PII) to analytics systems, which violates user privacy rules. You want to quickly build a scalable solution using cloud-native managed services to prevent exposure of PII to the analytics systems. What should you do?

A.Build a Cloud Function that reads the topics and makes a call to the Cloud Data Loss Prevention API. Use the tagging and confidence levels to either pass or quarantine the data in a bucket for review.

B.Use Stackdriver logging to analyze the data passed through the total pipeline to identify transactions that may contain sensitive information.

C.Install a third-party data validation tool on Compute Engine virtual machines to check the incoming data for sensitive information.

D.Create an authorized view in BigQuery to restrict access to tables with sensitive data.

Professional-Data-Engineer Exam Question 138

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?

A.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.

B.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.

C.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.

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

Professional-Data-Engineer Exam Question 139

Flowlogistic wants to use Google BigQuery as their primary analysis system, but they still have Apache Hadoop and Spark workloads that they cannot move to BigQuery. Flowlogistic does not know how to store the data that is common to both workloads. What should they do?

A.Store the common data in BigQuery and expose authorized views.

B.Store he common data in the HDFS storage for a Google Cloud Dataproc cluster.

C.Store the common data encoded as Avro in Google Cloud Storage.

D.Store the common data in BigQuery as partitioned tables.

Professional-Data-Engineer Exam Question 140

Your company has a hybrid cloud initiative. You have a complex data pipeline that moves data between cloud provider services and leverages services from each of the cloud providers. Which cloud-native service should you use to orchestrate the entire pipeline?

A.Cloud Composer

B.Cloud Dataflow

C.Cloud Dataproc

D.Cloud Dataprep

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Professional-Data-Engineer Exam Question 136

Professional-Data-Engineer Exam Question 137

Professional-Data-Engineer Exam Question 138

Professional-Data-Engineer Exam Question 139

Professional-Data-Engineer Exam Question 140

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