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Google Associate Data Practitioner Sample Questions:

1. Your organization uses scheduled queries to perform transformations on data stored in BigQuery. You discover that one of your scheduled queries has failed. You need to troubleshoot the issue as quickly as possible. What should you do?

A) Request access from your admin to the BigQuery information_schema. Query the jobs view with the failed job ID, and analyze error details.
B) Navigate to the Scheduled queries page in the Google Cloud console. Select the failed job, and analyze the error details.
C) Set up a log sink using the gcloud CLI to export BigQuery audit logs to BigQuery. Query those logs to identify the error associated with the failed job I
D) Navigate to the Logs Explorer page in Cloud Logging. Use filters to find the failed job, and analyze the error details.

2. You created a curated dataset of market trends in BigQuery that you want to share with multiple external partners. You want to control the rows and columns that each partner has access to. You want to follow Google-recommended practices. What should you do?

A) Publish the dataset in Analytics Hub. Grant dataset-level access to each partner by using subscriptions.
B) Create a separate Cloud Storage bucket for each partner. Export the dataset to each bucket and assign each partner to their respective bucket. Grant bucket-level access by using 1AM roles.
C) Grant each partner read access to the BigQuery dataset by using 1AM roles.
D) Create a separate project for each partner and copy the dataset into each project. Publish each dataset in Analytics Hub. Grant dataset-level access to each partner by using subscriptions.

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?

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.

4. You are developing a data ingestion pipeline to load small CSV files into BigQuery from Cloud Storage. You want to load these files upon arrival to minimize data latency. You want to accomplish this with minimal cost and maintenance. What should you do?

A) Create a Cloud Composer pipeline to load new files from Cloud Storage to BigQuery and schedule it to run every 10 minutes.
B) Use the bq command-line tool within a Cloud Shell instance to load the data into BigQuery.
C) Create a Dataproc cluster to pull CSV files from Cloud Storage, process them using Spark, and write the results to BigQuery.
D) Create a Cloud Run function to load the data into BigQuery that is triggered when data arrives in Cloud Storage.

5. You need to create a data pipeline that streams event information from applications in multiple Google Cloud regions into BigQuery for near real-time analysis. The data requires transformation before loading. You want to create the pipeline using a visual interface. What should you do?

A) Push event information to a Pub/Sub topic. Create a Cloud Run function to subscribe to the Pub/Sub topic, apply transformations, and insert the data into BigQuery.
B) Push event information to a Pub/Sub topic. Create a BigQuery subscription in Pub/Sub.
C) Push event information to Cloud Storage, and create an external table in BigQuery. Create a BigQuery scheduled job that executes once each day to apply transformations.
D) Push event information to a Pub/Sub topic. Create a Dataflow job using the Dataflow job builder.

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