Google ADP Exam : Associate Data Practitioner

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Google ADP Exam Syllabus Topics:

Google Associate Data Practitioner Sample Questions:

1. You are working with a large dataset of customer reviews stored in Cloud Storage. The dataset contains several inconsistencies, such as missing values, incorrect data types, and duplicate entries. You need toclean the data to ensure that it is accurate and consistent before using it for analysis. What should you do?

A) Use the PythonOperator in Cloud Composer to clean the data and load it into BigQuery. Use SQL for analysis.
B) Use Cloud Run functions to clean the data and load it into BigQuery. Use SQL for analysis.
C) Use Storage Transfer Service to move the data to a different Cloud Storage bucket. Use event triggers to invoke Cloud Run functions to load the data into BigQuery. Use SQL for analysis.
D) Use BigQuery to batch load the data into BigQuery. Use SQL for cleaning and analysis.

2. Your organization has decided to migrate their existing enterprise data warehouse to BigQuery. The existing data pipeline tools already support connectors to BigQuery. You need to identify a data migration approach that optimizes migration speed. What should you do?

A) Use the existing data pipeline tool's BigQuery connector to reconfigure the data mapping.
B) Create a temporary file system to facilitate data transfer from the existing environment to Cloud Storage. Use Storage Transfer Service to migrate the data into BigQuery.
C) Use the BigQuery Data Transfer Service to recreate the data pipeline and migrate the data into BigQuery.
D) Use the Cloud Data Fusion web interface to build data pipelines. Create a directed acyclic graph (DAG) that facilitates pipeline orchestration.

3. You are designing a BigQuery data warehouse with a team of experienced SQL developers. You need to recommend a cost- effective, fully-managed, serverless solution to build ELT processes with SQL pipelines.
Your solution must include source code control, environment parameterization, and data quality checks. What should you do?

A) Use Cloud Data Fusion to visually design and manage the pipelines.
B) Use Dataproc to run MapReduce jobs for distributed data processing.
C) Use Dataform to build, orchestrate, and monitor the pipelines.
D) Use Cloud Composer to orchestrate and run data workflows.

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 -- daisy-chain option.
B) Use Storage Transfer Service to configure an agent-based transfer. Set the appropriate bandwidth limit for the agent pool.
C) Use the gcloud storage cp command to copy all files from on- premises to Cloud Storage using the --no- clobber option.
D) Request a Transfer Appliance, copy the data to the appliance, and ship it back to Google Cloud.

5. Your organization has highly sensitive data that gets updated once a day and is stored across multiple datasets in BigQuery. You need to provide a new data analyst access to query specific data in BigQuery while preventing access to sensitive dat a. What should you do?

A) Create a new Google Cloud project, and copy the limited data into a BigQuery table. Grant the data analyst the BigQuery Data Owner IAM role in the new Google Cloud project.
B) Grant the data analyst the BigQuery Job User IAM role in the Google Cloud project.
C) Create a materialized view with the limited data in a new dataset. Grant the data analyst BigQuery Data Viewer IAM role in the dataset and the BigQuery Job User IAM role in the Google Cloud project.
D) Grant the data analyst the BigQuery Data Viewer IAM role in the Google Cloud project.

Solutions: