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Google Associate Data Practitioner Sample Questions (Q19-Q24):
NEW QUESTION # 19
Your company uses Looker to visualize and analyze sales dat
a. You need to create a dashboard that displays sales metrics, such as sales by region, product category, and time period. Each metric relies on its own set of attributes distributed across several tables. You need to provide users the ability to filter the data by specific sales representatives and view individual transactions. You want to follow the Google-recommended approach. What should you do?
- A. Create multiple Explores, each focusing on each sales metric. Link the Explores together in a dashboard using drill-down functionality.
- B. Use BigQuery to create multiple materialized views, each focusing on a specific sales metric. Build the dashboard using these views.
- C. Use Looker's custom visualization capabilities to create a single visualization that displays all the sales metrics with filtering and drill-down functionality.
- D. Create a single Explore with all sales metrics. Build the dashboard using this Explore.
Answer: D
NEW QUESTION # 20
You are storing data in Cloud Storage for a machine learning project. The data is frequently accessed during the model training phase, minimally accessed after 30 days, and unlikely to be accessed after 90 days. You need to choose the appropriate storage class for the different stages of the project to minimize cost. What should you do?
- A. Store the data in Nearline storage during the model training phase. Transition the data to Archive storage 30 days after model deployment, and to Coldline storage 90 days after model deployment.
- B. Store the data in Standard storage during the model training phase. Transition the data to Durable Reduced Availability (DRA) storage 30 days after model deployment, and to Coldline storage 90 days after model deployment.
- C. Store the data in Nearline storage during the model training phase. Transition the data to Coldline storage 30 days after model deployment, and to Archive storage 90 days after model deployment.
- D. Store the data in Standard storage during the model training phase. Transition the data to Nearline storage 30 days after model deployment, and to Coldline storage 90 days after model deployment.
Answer: D
NEW QUESTION # 21
Your company has an on-premises file server with 5 TB of data that needs to be migrated to Google Cloud.
The network operations team has mandated that you can only use up to 250 Mbps of the total available bandwidth for the migration. You need to perform an online migration to Cloud Storage. What should you do?
- A. Use Storage Transfer Service to configure an agent-based transfer. Set the appropriate bandwidth limit for the agent pool.
- B. Use the gcloud storage cp command to copy all files from on- premises to Cloud Storage using the --no- clobber option.
- C. Request a Transfer Appliance, copy the data to the appliance, and ship it back to Google Cloud.
- D. Use the gcloud storage cp command to copy all files from on- premises to Cloud Storage using the -- daisy-chain option.
Answer: A
NEW QUESTION # 22
Your organization uses a BigQuery table that is partitioned by ingestion time. You need to remove data that is older than one year to reduce your organization's storage costs. You want to use the most efficient approach while minimizing cost. What should you do?
- A. Set the table partition expiration period to one year using the ALTER TABLE statement in SQL.
- B. Create a view that filters out rows that are older than one year.
- C. Require users to specify a partition filter using the alter table statement in SQL.
- D. Create a scheduled query that periodically runs an update statement in SQL that sets the "deleted" column to "yes" for data that is more than one year old. Create a view that filters out rows that have been marked deleted.
Answer: A
NEW QUESTION # 23
You need to create a weekly aggregated sales report based on a large volume of data. You want to use Python to design an efficient process for generating this report. What should you do?
- A. Create a Colab Enterprise notebook and use the bigframes.pandas library. Schedule the notebook to execute once a week.
- B. Create a Cloud Data Fusion and Wrangler flow. Schedule the flow to run once a week.
- C. Create a Dataflow directed acyclic graph (DAG) coded in Python. Use Cloud Scheduler to schedule the code to run once a week.
- D. Create a Cloud Run function that uses NumPy. Use Cloud Scheduler to schedule the function to run once a week.
Answer: C
NEW QUESTION # 24
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