Free Access Google.Professional-Data-Engineer.v2026-02-05.q279 Practice Test (Page 34)

Question 161

You are planning to load some of your existing on-premises data into BigQuery on Google Cloud. You want to either stream or batch-load data, depending on your use case. Additionally, you want to mask some sensitive data before loading into BigQuery. You need to do this in a programmatic way while keeping costs to a minimum. What should you do?

A.Use the BigQuery Data Transfer Service to schedule your migration. After the data is populated in BigQuery. use the connection to the Cloud Data Loss Prevention {Cloud DLP} API to de-identify the necessary data.

B.Create your pipeline with Dataflow through the Apache Beam SDK for Python, customizing separate options within your code for streaming.batch processing, and Cloud DLP Select BigQuery as your data sink.

C.Use Cloud Data Fusion to design your pipeline, use the Cloud DLP plug-in to de-identify data within your pipeline, and then move the datainto BigQuery.

D.Set up Datastream to replicate your on-premise data on BigQuery.

Correct Answer: B

To load on-premises data into BigQuery while masking sensitive data, we need a solution that offers flexibility for both streaming and batch processing, as well as data masking capabilities. Here's a detailed explanation of why option B is the best choice:
Apache Beam and Dataflow:
Apache Beam SDKprovides a unified programming model for both batch and stream data processing.
Google Cloud Dataflowis a fully managed service for executing Apache Beam pipelines, offering scalability and ease of use.
Customization for Different Use Cases:
By using the Apache Beam SDK, you can write custom pipelines that can handle both streaming and batch processing within the same framework.
This allows you to switch between streaming and batch modes based on your use case without changing the core logic of your data pipeline.
Data Masking with Cloud DLP:
Google Cloud Data Loss Prevention (DLP)API can be integrated into your Apache Beam pipeline to de- identify and mask sensitive data programmatically before loading it into BigQuery.
This ensures that sensitive data is handled securely and complies with privacy requirements.
Cost Efficiency:
Using Dataflow can be cost-effective because it is a fully managed service, reducing the operational overhead associated with managing your own infrastructure.
The pay-as-you-go model ensures you only pay for the resources you consume, which can help keep costs under control.
Implementation Steps:
Set up Apache Beam Pipeline:
Write a pipeline using the Apache Beam SDK for Python that reads data from your on-premises storage.
Add transformations for data processing, including the integration with Cloud DLP for data masking.
Configure Dataflow:
Deploy the Apache Beam pipeline on Google Cloud Dataflow.
Customize the pipeline options for both streaming and batch use cases.
Load Data into BigQuery:
Set BigQuery as the sink for your data in the Apache Beam pipeline.
Ensure the processed and masked data is loaded into the appropriate BigQuery tables.
Reference Links:
Apache Beam Documentation
Google Cloud Dataflow Documentation
Google Cloud DLP Documentation
BigQuery Documentation

Question 162

Business owners at your company have given you a database of bank transactions. Each row contains the
user ID, transaction type, transaction location, and transaction amount. They ask you to investigate what
type of machine learning can be applied to the data. Which three machine learning applications can you
use? (Choose three.)

A.Supervised learning to determine which transactions are most likely to be fraudulent.

B.Unsupervised learning to determine which transactions are most likely to be fraudulent.

C.Reinforcement learning to predict the location of a transaction.

D.Clustering to divide the transactions into N categories based on feature similarity.

E.Supervised learning to predict the location of a transaction.

F.Unsupervised learning to predict the location of a transaction.

Question 163

Which of the following statements about the Wide & Deep Learning model are true? (Select 2 answers.)

A.The wide model is used for memorization, while the deep model is used for generalization.

B.A good use for the wide and deep model is a recommender system.

C.The wide model is used for generalization, while the deep model is used for memorization.

D.A good use for the wide and deep model is a small-scale linear regression problem.

Question 164

You receive data files in CSV format monthly from a third party. You need to cleanse this data, but every third month the schema of the files changes. Your requirements for implementing these transformations include:
* Executing the transformations on a schedule
* Enabling non-developer analysts to modify transformations
* Providing a graphical tool for designing transformations
What should you do?

A.Use Apache Spark on Cloud Dataproc to infer the schema of the CSV file before creating a Dataframe.
Then implement the transformations in Spark SQL before writing the data out to Cloud Storage and loading into BigQuery

B.Use Cloud Dataprep to build and maintain the transformation recipes, and execute them on a scheduled basis

C.Help the analysts write a Cloud Dataflow pipeline in Python to perform the transformation. The Python code should be stored in a revision control system and modified as the incoming data's schema changes

D.Load each month's CSV data into BigQuery, and write a SQL query to transform the data to a standard schema. Merge the transformed tables together with a SQL query

Question 165

You need to migrate a Redis database from an on-premises data center to a Memorystore for Redis instance.
You want to follow Google-recommended practices and perform the migration for minimal cost. time, and effort. What should you do?

A.Make a secondary instance of the Redis database on a Compute Engine instance, and then perform a live cutover.

B.Write a shell script to migrate the Redis data, and create a new Memorystore for Redis instance.

C.Create a Dataflow job to road the Redis database from the on-premises data center. and write the data to a Memorystore for Redis instance

D.Make an RDB backup of the Redis database, use the gsutil utility to copy the RDB file into a Cloud Storage bucket, and then import the RDB tile into the Memorystore for Redis instance.

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