Google Associate-Data-Practitioner Google Cloud Associate Data Practitioner Exam Exam Practice Test Questions

• Question 1

  You have millions of customer feedback records stored in BigQuery. You want to summarize the data by using the large language model (LLM) Gemini. You need to plan and execute this analysis using the most efficient approach. What should you do?

  • Query the BigQuery table from within a Python notebook, use the Gemini API to summarize thedata within the notebook, and store the summaries in BigQuery.
  • Use a BigQuery ML model to pre-process the text data, export the results to Cloud Storage, and use the Gemini API to summarize the pre- processed data.
  • Create a BigQuery Cloud resource connection to a remote model in Vertex Al, and use Gemini to summarize the data.
  • Export the raw BigQuery data to a CSV file, upload it to Cloud Storage, and use the Gemini API to summarize the data.

  Show Answer Answer: C Next

• Question 2

  Your team wants to create a monthly report to analyze inventory data that is updated daily. You need to aggregate the inventory counts by using only the most recent month of data, and save the results to be used in a Looker Studio dashboard. What should you do?

  • Create a materialized view in BigQuery that uses the SUM( ) function and the DATE_SUB( ) function.
  • Create a saved query in the BigQuery console that uses the SUM( ) function and the DATE_SUB( ) function. Re-run the saved query every month, and save the results to a BigQuery table.
  • Create a BigQuery table that uses the SUM( ) function and the _PARTITIONDATE filter.
  • Create a BigQuery table that uses the SUM( ) function and the DATE_DIFF( ) function.

  Show Answer Answer: A Next

• Question 3

  You created a customer support application that sends several forms of data to Google Cloud. Your application is sending:

  • Audio files from phone interactions with support agents that will be accessed during trainings.
  • CSV files of users’ personally identifiable information (Pll) that will be analyzed with SQL.
  • A large volume of small document files that will power other applications.You need to select the appropriate tool for each data type given the required use case, while following Google-recommended practices. Which should you choose?
  • Cloud Storage
  • CloudSQL for PostgreSQL
  • Bigtable B.
  • Filestore
  • Cloud SQL for PostgreSQL
  • Datastore C.
  • Cloud Storage
  • BigQuery
  • Firestore D.
  • Filestore
  • Bigtable
  • BigQuery

  Show Answer Answer: C Next

• Question 4

  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 data. You need to migrate this data while maintaining data integrity, and minimizing downtime and cost. What should you do?

  • 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.
  • 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.
  • Use Database Migration Service to replicate the MySQL database to a Cloud SQL for MySQL instance.
  • Use Cloud Data Fusion to migrate the MySQL database to MySQL on Compute Engine.

  Show Answer Answer: C Next

• Question 5

  Your organization’s ecommerce website collects user activity logs using a Pub/Sub topic. Your organization’s leadership team wants a dashboard that contains aggregated user engagementmetrics. You need to create a solution that transforms the user activity logs into aggregated metrics, while ensuring that the raw data can be easily queried. What should you do?

  • Create a Dataflow subscription to the Pub/Sub topic, and transform the activity logs. Load the transformed data into a BigQuery table for reporting.
  • Create an event-driven Cloud Run function to trigger a data transformation pipeline to run. Load the transformed activity logs into a BigQuery table for reporting.
  • Create a Cloud Storage subscription to the Pub/Sub topic. Load the activity logs into a bucket using the Avro file format. Use Dataflow to transform the data, and load it into a BigQuery table for reporting.
  • Create a BigQuery subscription to the Pub/Sub topic, and load the activity logs into the table. Create a materialized view in BigQuery using SQL to transform the data for reporting

  Show Answer Answer: A Next