Free Access Google.Professional-Data-Engineer.v2026-02-05.q279 Practice Test (Page 36)

Question 171

You are creating a data model in BigQuery that will hold retail transaction data. Your two largest tables, sales_transation_header and sales_transation_line. have a tightly coupled immutable relationship. These tables are rarely modified after load and are frequently joined when queried. You need to model the sales_transation_header and sales_transation_line tables to improve the performance of data analytics queries.
What should you do?

A.Create a sal es_transaction table that Stores the sales_tran3action_header and sales_transaction_line data as a JSON data type.

B.Create a sales_transaction table that holds the sales_transaction_header information as rows and thesales_transaction_line rows as nested and repeated fields.

C.Create a sale_transaction table that holds the sales_transaction_header and sales_transaction_line information as rows, duplicating the sales_transaction_header data for each line.

D.Create separate sales_transation_header and sales_transation_line tables and. when querying, specify the sales transition line first in the WHERE clause.

Question 172

You are running a Dataflow streaming pipeline, with Streaming Engine and Horizontal Autoscaling enabled. You have set the maximum number of workers to 1000. The input of your pipeline is Pub/Sub messages with notifications from Cloud Storage One of the pipeline transforms reads CSV files and emits an element for every CSV line. The Job performance is low. the pipeline is using only 10 workers, and you notice that the autoscaler is not spinning up additional workers. What should you do to improve performance?

A.Use Dataflow Prime, and enable Right Fitting to increase the worker resources.

B.Update the job to increase the maximum number of workers.

C.Enable Vertical Autoscaling to let the pipeline use larger workers.

D.Change the pipeline code, and introduce a Reshuffle step to prevent fusion.

Question 173

You want to archive data in Cloud Storage. Because some data is very sensitive, you want to use the "Trust No One" (TNO) approach to encrypt your data to prevent the cloud provider staff from decrypting your data. What should you do?

A.Use gcloud kms keys createto create a symmetric key. Then use gcloud kms encryptto encrypt each archival file with the key and unique additional authenticated data (AAD). Use gsutil cp to upload each encrypted file to the Cloud Storage bucket, and keep the AAD outside of Google Cloud.

B.Use gcloud kms keys create to create a symmetric key. Then use gcloud kms encryptto encrypt each archival file with the key. Use gsutil cpto upload each encrypted file to the Cloud Storage bucket.
Manually destroy the key previously used for encryption, and rotate the key once.

C.Specify customer-supplied encryption key (CSEK) in the .botoconfiguration file. Use gsutil cpto upload each archival file to the Cloud Storage bucket. Save the CSEK in Cloud Memorystore as permanent storage of the secret.

D.Specify customer-supplied encryption key (CSEK) in the .botoconfiguration file. Use gsutil cpto upload each archival file to the Cloud Storage bucket. Save the CSEK in a different project that only the security team can access.

Question 174

A data scientist has created a BigQuery ML model and asks you to create an ML pipeline to serve predictions. You have a REST API application with the requirement to serve predictions for an individual user ID with latency under 100 milliseconds. You use the following query to generate predictions: SELECT predicted_label, user_id FROM ML.PREDICT (MODEL `dataset.model', table . How should you create the ML pipeline?
user_features)

A.Add a WHERE clause to the query, and grant the BigQuery Data Viewer role to the application service account.

B.Create a Cloud Dataflow pipeline using BigQueryIO to read results from the query. Grant the Dataflow Worker role to the application service account.

C.Create a Cloud Dataflow pipeline using BigQueryIO to read predictions for all users from the query.
Write the results to Cloud Bigtable using BigtableIO. Grant the Bigtable Reader role to the application service account so that the application can read predictions for individual users from Cloud Bigtable.

D.Create an Authorized View with the provided query. Share the dataset that contains the view with the application service account.

Question 175

You are developing a new deep teaming model that predicts a customer's likelihood to buy on your ecommerce site. Alter running an evaluation of the model against both the original training data and new test data, you find that your model is overfitting the data. You want to improve the accuracy of the model when predicting new data. What should you do?

A.Increase the size of the training dataset, and increase the number of input features.

B.Increase the size of the training dataset, and decrease the number of input features.

C.Reduce the size of the training dataset, and increase the number of input features.

D.Reduce the size of the training dataset, and decrease the number of input features.

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Question 171

Question 172

Question 173

Question 174

Question 175

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