Free Access Google.Professional-Data-Engineer.v2026-02-05.q279 Practice Test (Page 28)

Question 131

Your organization stores highly personal data in BigQuery and needs to comply with strict data privacy regulations. You need to ensure that sensitive data values are rendered unreadable whenever an employee leaves the organization. What should you do?

A.Use dynamic data masking and revoke viewer permissions when employees leave the organization.

B.Use column-level access controls with policy tags and revoke viewer permissions when employees leave the organization.

C.Use AEAD functions and delete keys when employees leave the organization.

D.Use customer-managed encryption keys (CMEK) and delete keys when employees leave the organization.

Correct Answer: C

Comprehensive and Detailed Explanation:
The core requirement is to make specific data values permanently unreadable, a concept often referred to as cryptographic erasure or "crypto-shredding." Option C is the correct answer because it achieves this at a granular, field-level. BigQuery's AEAD (Authenticated Encryption with Associated Data) functions allow you to encrypt individual field values within your tables using a keyset from Cloud KMS. You can encrypt the sensitive data, store the encrypted value in BigQuery, and when an employee leaves, you simply destroy the specific KMS key version used for their data. Once the key is destroyed, the encrypted data is permanently unrecoverable, thus rendering it unreadable.
Option A and B are incorrect because data masking and column-level access controls are about controlling access to the data, not about making the underlying data itself unreadable. If an administrator with sufficient privileges were to query the table, the data would still be there in its original form. Revoking permissions prevents a specific user from seeing the data, but it doesn't erase it.
Option D is incorrect because Customer-Managed Encryption Keys (CMEK) operate at the entire table level.
Deleting a CMEK key would make the entire table unreadable, which is far too broad and disruptive. The goal is to target specific sensitive values, not destroy whole tables.
Reference (Google Cloud Documentation Concepts):
This solution leverages field-level encryption within BigQuery. The official documentation for "Protecting data with Cloud KMS keys" states that BigQuery's AEAD functions (AEAD.ENCRYPT and AEAD.
DECRYPT) are the intended mechanism for this purpose. This approach provides fine-grained control, allowing you to manage the lifecycle of encrypted data by managing the lifecycle of the encryption keys in Cloud Key Management Service (Cloud KMS). The act of destroying a key version is the mechanism for crypto-shredding.

Question 132

These primary tool in use, and the data format is Optimized Row Columnar (ORC). All ORC files have been successfully copied to a Cloud Storage bucket. You need to replicate some data to the cluster's local Hadoop Distributed File System (HDFS) to maximize performance. What are two ways to start using Hive in Cloud Dataproc? (Choose two.)

A.Load the ORC files into BigQuery. Leverage BigQuery connector for Hadoop to mount the BigQuery tables as external Hive tables. Replicate external Hive tables to the native ones.

B.Run the gsutil utility to transfer all ORC files from the Cloud Storage bucket to HDFS. Mount the Hive tables locally.

C.Leverage Cloud Storage connector for Hadoop to mount the ORC files as external Hive tables. Replicate external Hive tables to the native ones.

D.Run the gsutil utility to transfer all ORC files from the Cloud Storage bucket to the master node of the Dataproc cluster. Then run the Hadoop utility to copy them do HDFS. Mount the Hive tables from HDFS.

E.Run the gsutil utility to transfer all ORC files from the Cloud Storage bucket to any node of the Dataproc cluster. Mount the Hive tables locally.

Question 133

What is the recommended action to do in order to switch between SSD and HDD storage for your Google Cloud Bigtable instance?

A.create a third instance and sync the data from the two storage types via batch jobs

B.export the data from the existing instance and import the data into a new instance

C.run parallel instances where one is HDD and the other is SDD

D.the selection is final and you must resume using the same storage type

Question 134

You are building a new application that you need to collect data from in a scalable way. Data arrives continuously from the application throughout the day, and you expect to generate approximately 150 GB of JSON data per day by the end of the year. Your requirements are:
Decoupling producer from consumer
Space and cost-efficient storage of the raw ingested data, which is to be stored indefinitely
Near real-time SQL query
Maintain at least 2 years of historical data, which will be queried with SQ
Which pipeline should you use to meet these requirements?

A.Create an application that publishes events to Cloud Pub/Sub, and create Spark jobs on Cloud Dataproc to convert the JSON data to Avro format, stored on HDFS on Persistent Disk.

B.Create an application that writes to a Cloud SQL database to store the data. Set up periodic exports of the database to write to Cloud Storage and load into BigQuery.

C.Create an application that publishes events to Cloud Pub/Sub, and create a Cloud Dataflow pipeline that transforms the JSON event payloads to Avro, writing the data to Cloud Storage and BigQuery.

D.Create an application that provides an API. Write a tool to poll the API and write data to Cloud Storage as gzipped JSON files.

Question 135

Your company is performing data preprocessing for a learning algorithm in Google Cloud Dataflow. Numerous data logs are being are being generated during this step, and the team wants to analyze them. Due to the dynamic nature of the campaign, the data is growing exponentially every hour.
The data scientists have written the following code to read the data for a new key features in the logs.
BigQueryIO.Read
.named("ReadLogData")
.from("clouddataflow-readonly:samples.log_data")
You want to improve the performance of this data read. What should you do?

A.Specify the TableReferenceobject in the code.

B.Use .fromQueryoperation to read specific fields from the table.

C.Call a transform that returns TableRowobjects, where each element in the PCollectionrepresents a single row in the table.

D.Use of both the Google BigQuery TableSchemaand TableFieldSchemaclasses.

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