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Associate-Data-Practitioner Google Cloud Certified Associate Data Practitioner (GCC-ADP)

  • Google
  • 5 free sample questions

About this exam

Associate-Data-Practitioner is the Google exam Google Cloud Certified Associate Data Practitioner (GCC-ADP). Paid practice material for this exam is not available yet, but you can try its free sample questions.

Exam code
Associate-Data-Practitioner
Vendor
Google
Practice questions
Not available yet
Free sample questions
5
Question bank last updated
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Try 2 sample questions

Taken from the free sample set for Associate-Data-Practitioner.

Question 1

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?

  1. Option A:

    Publish the dataset in Analytics Hub. Grant dataset-level access to each partner by using subscriptions.

  2. Option 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.

  3. Option C:

    Grant each partner read access to the BigQuery dataset by using 1AM roles.

  4. Option 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.

Show answer and explanation

Correct answer: A

Comprehensive and Detailed in Depth Explanation:

Why A is correct:Analytics Hub allows you to share datasets with external partners while maintaining control over access.

Subscriptions allow granular control.

Why other options are incorrect:B: Cloud storage is for files, not bigquery datasets.

C: IAM roles do not allow for granular row and column level control.

D: Creating a separate project for each partner is complex and not scalable.

[References:, Analytics Hub: https://cloud.google.com/analytics-hub/docs]

Question 2

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?

  1. Option A:

    Use Cloud Data Fusion pipelines.

  2. Option B:

    Use Dataform workflows.

  3. Option C:

    Use Dataflow pipelines.

  4. Option D:

    Use Cloud Composer operators.

Show answer and explanation

Correct answer: B

Dataform workflows are the ideal solution for migrating batch transformation pipelines to Google Cloud when you want to perform programmatic transformations using only SQL. Dataform allows you to define SQL-based workflows for data transformations and supports Git integration for version control, enabling collaboration and version tracking of your pipelines. This approach is purpose-built for SQL-driven data pipeline management and aligns perfectly with your requirements.

The solution must use SQL for transformations and integrate with Git for version control, focusing on batch pipelines. Let’s evaluate:

    Option A : Cloud Data Fusion uses a visual UI with plugins, not SQL-only transformations. It lacks native Git integration (requires external tools), missing a key requirement.

    Option B : Dataform is a SQL-based workflow tool for BigQuery transformations, defining pipelines as SQLX scripts. It integrates natively with Git for version control, supporting batch ELT processes with minimal overhead.

    Option C : Cloud Composer uses Python DAGs and operators, not SQL-only transformations. Git is possible but not intrinsic to its workflow design.

[: Google Cloud Documentation - "Dataform" (https://cloud.google.com/dataform)., , , ]
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