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Curious about Actual Google Cloud Associate Data Practitioner Exam Questions?
Here are sample Google Cloud Associate Data Practitioner (Associate Data Practitioner) Exam questions from real exam. You can get more Google Cloud Certified (Associate Data Practitioner) Exam premium practice questions at TestInsights.
Question 1
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?
Correct : 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.
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Question 2
You manage a BigQuery table that is used for critical end-of-month reports. The table is updated weekly with new sales dat
a. You want to prevent data loss and reporting issues if the table is accidentally deleted. What should you do?
Correct : B
Scheduling the creation of a snapshot of the table weekly ensures that you have a point-in-time backup of the table. In case of accidental deletion, you can re-create the table from the snapshot. Additionally, BigQuery's time travel feature allows you to recover data from up to seven days prior to deletion. Combining snapshots with time travel provides a robust solution for preventing data loss and ensuring reporting continuity for critical tables. This approach minimizes risks while offering flexibility for recovery.
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Question 3
Your organization sends IoT event data to a Pub/Sub topic. Subscriber applications read and perform transformations on the messages before storing them in the data warehouse. During particularly busy times when more data is being written to the topic, you notice that the subscriber applications are not acknowledging messages within the deadline. You need to modify your pipeline to handle these activity spikes and continue to process the messages. What should you do?
Correct : B
Implementing flow control on the subscribers allows the subscriber applications to manage message processing during activity spikes by controlling the rate at which messages are pulled and processed. This prevents overwhelming the subscribers and ensures that messages are acknowledged within the deadline. Flow control helps maintain the stability of your pipeline during high-traffic periods without dropping or delaying messages unnecessarily.
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Question 4
You have millions of customer feedback records stored in BigQuery. You want to summarize the data by using the large language model (LLM) Gemini. You need to plan and execute this analysis using the most efficient approach. What should you do?
Correct : C
Creating a BigQuery Cloud resource connection to a remote model in Vertex AI and using Gemini to summarize the data is the most efficient approach. This method allows you to seamlessly integrate BigQuery with the Gemini model via Vertex AI, avoiding the need to export data or perform manual steps. It ensures scalability for large datasets and minimizes data movement, leveraging Google Cloud's ecosystem for efficient data summarization and storage.
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Question 5
You are working on a data pipeline that will validate and clean incoming data before loading it into BigQuery for real-time analysis. You want to ensure that the data validation and cleaning is performed efficiently and can handle high volumes of dat
a. What should you do?
Correct : C
Using Dataflow to create a streaming pipeline that includes validation and transformation steps is the most efficient and scalable approach for real-time analysis. Dataflow is optimized for high-volume data processing and allows you to apply validation and cleaning logic as the data flows through the pipeline. This ensures that only clean, validated data is loaded into BigQuery, supporting real-time analysis while handling high data volumes effectively.
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