Google Associate-Data-Practitioner Exam Dumps

Get All Google Cloud Associate Data Practitioner Exam Questions with Validated Answers

Associate-Data-Practitioner Pack
Vendor: Google
Exam Code: Associate-Data-Practitioner
Exam Name: Google Cloud Associate Data Practitioner
Exam Questions: 106
Last Updated: September 17, 2025
Related Certifications: Google Cloud Certified, Data Practitioner
Exam Tags: Associate Level Google Data AnalystsGoogle Data Engineers
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Free Google Associate-Data-Practitioner Exam Actual Questions

Question No. 1

You need to create a weekly aggregated sales report based on a large volume of dat

a. You want to use Python to design an efficient process for generating this report. What should you do?

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Correct Answer: D

Using Dataflow with a Python-coded Directed Acyclic Graph (DAG) is the most efficient solution for generating a weekly aggregated sales report based on a large volume of data. Dataflow is optimized for large-scale data processing and can handle aggregation efficiently. Python allows you to customize the pipeline logic, and Cloud Scheduler enables you to automate the process to run weekly. This approach ensures scalability, efficiency, and the ability to process large datasets in a cost-effective manner.


Question No. 2

Your organization needs to implement near real-time analytics for thousands of events arriving each second in Pub/Sub. The incoming messages require transformations. You need to configure a pipeline that processes, transforms, and loads the data into BigQuery while minimizing development time. What should you do?

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Correct Answer: A

Using a Google-provided Dataflow template is the most efficient and development-friendly approach to implement near real-time analytics for Pub/Sub messages. Dataflow templates are pre-built and optimized for processing streaming data, allowing you to quickly configure and deploy a pipeline with minimal development effort. These templates can handle message ingestion from Pub/Sub, perform necessary transformations, and load the processed data into BigQuery, ensuring scalability and low latency for near real-time analytics.


Question No. 3

Another team in your organization is requesting access to a BigQuery dataset. You need to share the dataset with the team while minimizing the risk of unauthorized copying of dat

a. You also want to create a reusable framework in case you need to share this data with other teams in the future. What should you do?

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Correct Answer: B

Using Analytics Hub to create a private exchange with data egress restrictions ensures controlled sharing of the dataset while minimizing the risk of unauthorized copying. This approach allows you to provide secure, managed access to the dataset without giving direct access to the raw data. The egress restriction ensures that data cannot be exported or copied outside the designated boundaries. Additionally, this solution provides a reusable framework that simplifies future data sharing with other teams or projects while maintaining strict data governance.

Extract from Google Documentation: From 'Analytics Hub Overview' (https://cloud.google.com/analytics-hub/docs): 'Analytics Hub enables secure, controlled data sharing with private exchanges. Combine with organization policies like restrictDataEgress to prevent data copying, providing a reusable framework for sharing BigQuery datasets across teams.' Reference: Google Cloud Documentation - 'Analytics Hub' (https://cloud.google.com/analytics-hub).


Question No. 4

Your company has developed a website that allows users to upload and share video files. These files are most frequently accessed and shared when they are initially uploaded. Over time, the files are accessed and shared less frequently, although some old video files may remain very popular. You need to design a storage system that is simple and cost-effective. What should you do?

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Correct Answer: B

The storage system must balance cost, simplicity, and access patterns: high initial access, decreasing over time, with some files remaining popular. Google Cloud Storage offers tailored options for this:

Option A: Custom Object Lifecycle Management (OLM) policies (e.g., transition to Nearline after 30 days, Archive after 90 days) are effective but static. They don't adapt to actual usage, so popular old files in Archive would incur high retrieval costs.

Option B: Autoclass automatically adjusts storage classes (Standard, Nearline, Coldline, Archive) based on object access patterns, not just age. It keeps frequently accessed files in Standard (low latency/cost for access) and moves inactive ones to cheaper classes, minimizing costs while preserving simplicity. This fits the ''some files remain popular'' nuance.

Option C: A Cloud Scheduler job to manually change classes daily is complex (requires scripting, monitoring), error-prone, and less cost-effective than automated solutions like Autoclass or OLM.

Option D: Defaulting to Archive is cheapest for storage but disastrous for access---retrieval costs and latency would skyrocket for initial high-access periods. Why B is Best: Autoclass simplifies management (no rules to define) and optimizes costs dynamically. For videos, where access varies unpredictably, it ensures popular files stay accessible without manual intervention, aligning with Google's cost-optimization guidance. Extract from Google Documentation: From 'Autoclass in Cloud Storage' (https://cloud.google.com/storage/docs/autoclass): 'Autoclass automatically transitions objects to the most cost-effective storage class based on access patterns, simplifying management and reducing costs for workloads with variable access, such as media files.' Reference: Google Cloud Documentation - 'Cloud Storage Autoclass' (https://cloud.google.com/storage/docs/autoclass).

Why B is Best: Autoclass simplifies management (no rules to define) and optimizes costs dynamically. For videos, where access varies unpredictably, it ensures popular files stay accessible without manual intervention, aligning with Google's cost-optimization guidance.

Extract from Google Documentation: From 'Autoclass in Cloud Storage' (https://cloud.google.com/storage/docs/autoclass): 'Autoclass automatically transitions objects to the most cost-effective storage class based on access patterns, simplifying management and reducing costs for workloads with variable access, such as media files.'

Option D: Defaulting to Archive is cheapest for storage but disastrous for access---retrieval costs and latency would skyrocket for initial high-access periods. Why B is Best: Autoclass simplifies management (no rules to define) and optimizes costs dynamically. For videos, where access varies unpredictably, it ensures popular files stay accessible without manual intervention, aligning with Google's cost-optimization guidance. Extract from Google Documentation: From 'Autoclass in Cloud Storage' (https://cloud.google.com/storage/docs/autoclass): 'Autoclass automatically transitions objects to the most cost-effective storage class based on access patterns, simplifying management and reducing costs for workloads with variable access, such as media files.' Reference: Google Cloud Documentation - 'Cloud Storage Autoclass' (https://cloud.google.com/storage/docs/autoclass).


Question No. 5

Your company uses Looker as its primary business intelligence platform. You want to use LookML to visualize the profit margin for each of your company's products in your Looker Explores and dashboards. You need to implement a solution quickly and efficiently. What should you do?

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Correct Answer: B

Comprehensive and Detailed in Depth

Why B is correct:Defining a new measure in LookML is the most efficient and direct way to calculate and visualize aggregated metrics like profit margin.

Measures are designed for calculations based on existing fields.

Why other options are incorrect:A: Filtering doesn't calculate or visualize the profit margin itself.

C: Dimensions are for categorizing data, not calculating aggregated metrics.

D: Derived tables are more complex and unnecessary for a simple calculation like profit margin, which can be done using a measure.


Looker Measures: https://cloud.google.com/looker/docs/reference/field-params/measure

Looker Dimensions: https://cloud.google.com/looker/docs/reference/field-params/dimension

Looker Derived Tables: https://cloud.google.com/looker/docs/data-modeling/derived-tables

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