Question 1

An organization wants to keep track of different versions of their customer churn prediction model, ensuring that each version’s metadata, performance metrics, and deployment status are easily accessible. What should they prioritize in Vertex AI Model Registry to manage these requirements?

Options :

A : Scheduling batch predictions on model versions to assess performance

B : Storing models as binary files

C : Organizing models in Google Sheets with their metrics and deployment statuses

D : Using tags to label models with version numbers and descriptions

Answer: D

Question 2

Your retail organization stores sensitive application usage data in Cloud Storage. You need to encrypt the data without the operational overhead of managing encryption keys. What should you do?

Options :

A : Use Google-managed encryption keys (GMEK).

B : Use customer-managed encryption keys (CMEK).

C : Use customer-supplied encryption keys (CSEK).

D :

Use customer-supplied encryption keys (CSEK) for the sensitive data and customer-managed encryption keys (CMEK) for the less sensitive data.

Answer: A

Question 3

You are migrating data from a legacy on-premises MySQL database to Google Cloud. The database contains various tables with different data types and sizes, including large tables with millions of rows and transactional dat a. You need to migrate this data while maintaining data integrity, and minimizing downtime and cost. What should you do?

Options :

A :

Set up a Cloud Composer environment to orchestrate a custom data pipeline. Use a Python script to extract data from the MySQL database and load it to MySQL on Compute Engine.

B :

Export the MySQL database to CSV files, transfer the files to Cloud Storage by using Storage Transfer Service, and load the files into a Cloud SQL for MySQL instance.

C :

Use Database Migration Service to replicate the MySQL database to a Cloud SQL for MySQL instance.

D : Use Cloud Data Fusion to migrate the MySQL database to MySQL on Compute Engine.

Answer: C

Question 4

A company needs to migrate their MySQL database to Google Cloud, where they plan to store the data in Cloud SQL for MySQL. They require a solution that can handle continuous replication with minimal downtime. Which tool should they use to perform this migration?

Options :

A : Cloud Data Fusion

B : Google Cloud Storage Transfer Service

C : BigQuery Data Transfer Service

D : Database Migration Service

Answer: D

Question 5

You’re working with a new dataset stored in Cloud Storage (GCS) that contains customer transaction records. To prepare for analysis in BigQuery, you need to ensure the data is structured and efficiently partitioned. Which approach will optimize the performance of your queries in BigQuery and support scalability as the dataset grows over time?

Options :

A : Convert the dataset to a CSV format and load it into BigQuery, partitioned by a timestamp column.

B : Convert the dataset to a Parquet format and load it into BigQuery, partitioned by transaction date.

C : Use the Cloud Storage Transfer Service to load the data directly from GCS into BigQuery without transformation.

D : Use Cloud Dataflow to transform the data, flattening nested records, and load it into BigQuery as a single partition.

Answer: B