Pass Guaranteed 2026 Latest Google ADP: Free Sample Associate Data Practitioner Questions

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Google ADP Exam Syllabus Topics:

Section	Weight	Objectives
Data Pipeline Orchestration	18%	- Pipeline design and automation 1. Workflow orchestration concepts (e.g., Cloud Composer) 2. Dataflow pipelines
Data Preparation and Ingestion	30%	- Data ingestion into Google Cloud services 1. Cloud Storage ingestion patterns 2. Streaming ingestion with Pub/Sub concepts
Data Management	25%	- Storage and data organization 1. Cloud Storage lifecycle policies 2. BigQuery datasets and tables management - Data governance and security 1. IAM and access control 2. Data lifecycle management
Data Analysis and Presentation	27%	- Data visualization 1. Looker / Looker Studio reporting - Querying and analyzing data 1. Data aggregation and transformation 2. BigQuery SQL usage

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Google Associate Data Practitioner Sample Questions (Q65-Q70):

NEW QUESTION # 65
You are a data analyst working with sensitive customer data in BigQuery. You need to ensure that only authorized personnel within your organization can query this data, while following the principle of least privilege. What should you do?

A. Enable access control by using IAM roles.
B. Update dataset privileges by using the SQL GRANT statement.
C. Export the data to Cloud Storage, and use signed URLs to authorize access.
D. Encrypt the data by using customer-managed encryption keys (CMEK).

Answer: A

NEW QUESTION # 66
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 Dataflow directed acyclic graph (DAG) coded in Python. Use Cloud Scheduler to schedule the code to run once a week.
B. Create a Colab Enterprise notebook and use the bigframes.pandas library. Schedule the notebook to execute once a week.
C. Create a Cloud Data Fusion and Wrangler flow. Schedule the flow 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: A

NEW QUESTION # 67
You are predicting customer churn for a subscription-based service. You have a 50 PB historical customer dataset in BigQuery that includes demographics, subscription information, and engagement metrics. You want to build a churn prediction model with minimal overhead. You want to follow the Google-recommended approach. What should you do?

A. Create a Looker dashboard that is connected to BigQuery. Use LookML to predict churn.
B. Use the BigQuery Python client library in a Jupyter notebook to query and preprocess the data in BigQuery. Use the CREATE MODEL statement in BigQueryML to train the churn prediction model.
C. Use Dataproc to create a Spark cluster. Use the Spark MLlib within the cluster to build the churn prediction model.
D. Export the data from BigQuery to a local machine. Use scikit- learn in a Jupyter notebook to build the churn prediction model.

Answer: B

NEW QUESTION # 68
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 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.
B. 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.
C. Grant the data analyst 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.

Answer: A

NEW QUESTION # 69
Your company is migrating their batch transformation pipelines to Google Cloud. You need to choose a solution that supports programmatic transformations using only SQL. You also want the technology to support Git integration for version control of your pipelines. What should you do?

A. Use Cloud Data Fusion pipelines.
B. Use Cloud Composer operators.
C. Use Dataform workflows.
D. Use Dataflow pipelines.

Answer: C

NEW QUESTION # 70
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