Google Cloud Certified Associate Data Practitioner (GCC-ADP) Associate-Data-Practitioner Exam Questions
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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 data. What should you do?
Correct Answer: 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.
You need to design a data pipeline to process large volumes of raw server log data stored in Cloud Storage. The data needs to be cleaned, transformed, and aggregated before being loaded into BigQuery for analysis. The transformation involves complex data manipulation using Spark scripts that your team developed. You need to implement a solution that leverages your team’s existing skillset, processes data at scale, and minimizes cost. What should you do?
Correct Answer: D
Comprehensive and Detailed In-Depth Explanation:
The pipeline must handle large-scale log processing with existing Spark scripts, prioritizing skillset reuse, scalability, and cost. Let’s break it down:
Option A : Dataflow uses Apache Beam, not Spark, requiring script rewrites (losing skillset leverage). Custom templates scale well but increase development cost and effort.
Option B : Cloud Data Fusion is a visual ETL tool, not Spark-based. It doesn’t reuse existing scripts, requiring redesign, and is less cost-efficient for complex, code-driven transformations.
Option C : Dataform uses SQLX for BigQuery ELT, not Spark. It’s unsuitable for pre-load transformations of raw logs and doesn’t leverage Spark skills.
Your organization’s ecommerce website collects user activity logs using a Pub/Sub topic. Your organization’s leadership team wants a dashboard that contains aggregated user engagement metrics. You need to create a solution that transforms the user activity logs into aggregated metrics, while ensuring that the raw data can be easily queried. What should you do?
Correct Answer: A
Using Dataflow to subscribe to the Pub/Sub topic and transform the activity logs is the best approach for this scenario. Dataflow is a managed service designed for processing and transforming streaming data in real time. It allows you to aggregate metrics from the raw activity logs efficiently and load the transformed data into a BigQuery table for reporting. This solution ensures scalability, supports real-time processing, and enables querying of both raw and aggregated data in BigQuery, providing the flexibility and insights needed for the dashboard.
You recently inherited a task for managing Dataflow streaming pipelines in your organization and noticed that proper access had not been provisioned to you. You need to request a Google-provided IAM role so you can restart the pipelines. You need to follow the principle of least privilege. What should you do?
Correct Answer: A
The Dataflow Developer role provides the necessary permissions to manage Dataflow streaming pipelines, including the ability to restart pipelines. This role adheres to the principle of least privilege, as it grants only the permissions required to manage and operate Dataflow jobs without unnecessary administrative access. Other roles, such as Dataflow Admin, would grant broader permissions, which are not needed in this scenario.
You need to create a data pipeline that streams event information from applications in multiple Google Cloud regions into BigQuery for near real-time analysis. The data requires transformation before loading. You want to create the pipeline using a visual interface. What should you do?
Correct Answer: A
Pushing event information to a Pub/Sub topic and then creating a Dataflow job using the Dataflow job builder is the most suitable solution. The Dataflow job builder provides a visual interface to design pipelines, allowing you to define transformations and load data into BigQuery. This approach is ideal for streaming data pipelines that require near real-time transformations and analysis. It ensures scalability across multiple regions and integrates seamlessly with Pub/Sub for event ingestion and BigQuery for analysis.
The best solution for creating a data pipeline with a visual interface for streaming event information from multiple Google Cloud regions into BigQuery for near real-time analysis with transformations is A. Push event information to a Pub/Sub topic. Create a Dataflow job using the Dataflow job builder.
Here's why:
Pub/Sub and Dataflow:
Pub/Sub is ideal for real-time message ingestion, especially from multiple regions.
Dataflow, particularly with the Dataflow job builder, provides a visual interface for creating data pipelines that can perform real-time stream processing and transformations.
The Dataflow job builder allows creating pipelines with visual tools, fulfilling the requirement of a visual interface.
Dataflow is built for real time streaming and applying transformations.
Let's break down why the other options are less suitable:
B. Push event information to Cloud Storage, and create an external table in BigQuery. Create a BigQuery scheduled job that executes once each day to apply transformations:
This is a batch processing approach, not real-time.
Cloud Storage and scheduled jobs are not designed for near real-time analysis.
This does not meet the real time requirement of the question.
C. Push event information to a Pub/Sub topic. Create a Cloud Run function to subscribe to the Pub/Sub topic, apply transformations, and insert the data into BigQuery:
While Cloud Run can handle transformations, it requires more coding and is less scalable and manageable than Dataflow for complex streaming pipelines.
Cloud run does not provide a visual interface.
D. Push event information to a Pub/Sub topic. Create a BigQuery subscription in Pub/Sub:
BigQuery subscriptions in Pub/Sub are for direct loading of Pub/Sub messages into BigQuery, without the ability to perform transformations.
This option does not provide any transformation functionality.
Therefore, Pub/Sub for ingestion and Dataflow with its job builder for visual pipeline creation and transformations is the most appropriate solution.