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Google Associate Data Practitioner Sample Questions:

1. Your organization is conducting analysis on regional sales metrics. Data from each regional sales team is stored as separate tables in BigQuery and updated monthly. You need to create a solution that identifies the top three regions with the highest monthly sales for the next three months. You want the solution to automatically provide up-to-date results. What should you do?

A) Create a BigQuery materialized view that performs a union across all of the regional sales tables. Use the rank() window function to query the new materialized view.
B) Create a BigQuery table that performs a union across all of the regional sales tables. Use the row_number() window function to query the new table.
C) Create a BigQuery table that performs a cross join across all of the regional sales tables. Use the rank() window function to query the new table.
D) Create a BigQuery materialized view that performs a cross join across all of the regional sales tables. Use the row_number() window function to query the new materialized view.

2. Your company's customer support audio files are stored in a Cloud Storage bucket. You plan to analyze the audio files' metadata and file content within BigQuery to create inference by using BigQuery ML. You need to create a corresponding table in BigQuery that represents the bucket containing the audio files. What should you do?

A) Create an object table.
B) Create an external table.
C) Create a temporary table.
D) Create a native table.

3. Your retail company wants to analyze customer reviews to understand sentiment and identify areas for improvement. Your company has a large dataset of customer feedback text stored in BigQuery that includes diverse language patterns, emojis, and slang. You want to build a solution to classify customer sentiment from the feedback text. What should you do?

A) Export the raw data from BigQuery. Use AutoML Natural Language to train a custom sentiment analysis model.
B) Develop a custom sentiment analysis model using TensorFlow. Deploy it on a Compute Engine instance.
C) Preprocess the text data in BigQuery using SQL functions. Export the processed data to AutoML Natural Language for model training and deployment.
D) Use Dataproc to create a Spark cluster, perform text preprocessing using Spark NLP, and build a sentiment analysis model with Spark MLlib.

4. 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 the gcloud storage cp command to copy all files from on- premises to Cloud Storage using the --no- clobber option.
B) Use Storage Transfer Service to configure an agent-based transfer. Set the appropriate bandwidth limit for the agent pool.
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.

5. You have a Dataproc cluster that performs batch processing on data stored in Cloud Storage. You need to schedule a daily Spark job to generate a report that will be emailed to stakeholders. You need a fully-managed solution that is easy to implement and minimizes complexity. What should you do?

A) Use Cloud Scheduler to trigger the Spark job. and use Cloud Run functions to email the report.
B) Use Cloud Run functions to trigger the Spark job and email the report.
C) Use Cloud Composer to orchestrate the Spark job and email the report.
D) Use Dataproc workflow templates to define and schedule the Spark job, and to email the report.

Solutions: