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Google Associate Data Practitioner Sample Questions:

1. Your organization has decided to move their on-premises Apache Spark-based workload to Google Cloud.
You want to be able to manage the code without needing to provision and manage your own cluster. What should you do?

A) Migrate the Spark jobs to Dataproc on Compute Engine.
B) Migrate the Spark jobs to Dataproc Serverless.
C) Configure a Google Kubernetes Engine cluster with Spark operators, and deploy the Spark jobs.
D) Migrate the Spark jobs to Dataproc on Google Kubernetes Engine.

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

3. Your data science team needs to collaboratively analyze a 25 TB BigQuery dataset to support the development of a machine learning model. You want to use Colab Enterprise notebooks while ensuring efficient data access and minimizing cost. What should you do?

A) Create a Dataproc cluster connected to a Colab Enterprise notebook, and use Spark to process the data in BigQuery.
B) Use BigQuery magic commands within a Colab Enterprise notebook to query and analyze the data.
C) Export the BigQuery dataset to Google Drive. Load the dataset into the Colab Enterprise notebook using Pandas.
D) Copy the BigQuery dataset to the local storage of the Colab Enterprise runtime, and analyze the data using Pandas.

4. 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) 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.
C) Grant the data analyst the BigQuery Data Viewer IAM role in the Google Cloud project.
D) Grant the data analyst the BigQuery Job User IAM role in the Google Cloud project.

5. Your organization has a BigQuery dataset that contains sensitive employee information such as salaries and performance reviews. The payroll specialist in the HR department needs to have continuous access to aggregated performance data, but they do not need continuous access to other sensitive dat a. You need to grant the payroll specialist access to the performance data without granting them access to the entire dataset using the simplest and most secure approach. What should you do?

A) Use authorized views to share query results with the payroll specialist.
B) Create a SQL query with the aggregated performance data. Export the results to an Avro file in a Cloud Storage bucket. Share the bucket with the payroll specialist.
C) Create a table with the aggregated performance data. Use table-level permissions to grant access to the payroll specialist.
D) Create row-level and column-level permissions and policies on the table that contains performance data in the dataset. Provide the payroll specialist with the appropriate permission set.

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