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Google Cloud Associate Data Practitioner Sample Questions (Q37-Q42):

NEW QUESTION # 37
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?

A. Retry messages until they are acknowledged.
B Implement flow control on the subscribers
B. Seek back to the last acknowledged message.
C. Forward unacknowledged messages to a dead-letter topic.

Answer: C

Explanation:
Implementingflow control on the subscribersallows 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.

NEW QUESTION # 38
You need to create a new data pipeline. You want a serverless solution that meets the following requirements:
* Data is streamed from Pub/Sub and is processed in real-time.
* Data is transformed before being stored.
* Data is stored in a location that will allow it to be analyzed with SQL using Looker.

Which Google Cloud services should you recommend for the pipeline?

A. 1. Cloud Composer
2. Cloud SQL for MySQL
B. 1. Dataflow
2. BigQuery
C. 1. Dataproc Serverless
2. Bigtable
D. 1. BigQuery
2. Analytics Hub

Answer: B

Explanation:
To build a serverless data pipeline that processes data in real-time from Pub/Sub, transforms it, and stores it for SQL-based analysis using Looker, the best solution is to use Dataflow and BigQuery. Dataflow is a fully managed service for real-time data processing and transformation, while BigQuery is a serverless data warehouse that supports SQL-based querying and integrates seamlessly with Looker for data analysis and visualization. This combination meets the requirements for real-time streaming, transformation, and efficient storage for analytical queries.

NEW QUESTION # 39
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?

A. Use Dataform to define the transformations in SQLX.
B. Use Dataflow with a custom template for the transformation logic.
C. Use Cloud Data Fusion to visually design and manage the pipeline.
D. Use Dataproc to run the transformations on a cluster.

Answer: D

Explanation:
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.

NEW QUESTION # 40
Your company uses Looker to visualize and analyze sales dat
a. You need to create a dashboard that displays sales metrics, such as sales by region, product category, and time period. Each metric relies on its own set of attributes distributed across several tables. You need to provide users the ability to filter the data by specific sales representatives and view individual transactions. You want to follow the Google-recommended approach. What should you do?

A. Create multiple Explores, each focusing on each sales metric. Link the Explores together in a dashboard using drill-down functionality.
B. Create a single Explore with all sales metrics. Build the dashboard using this Explore.
C. Use Looker's custom visualization capabilities to create a single visualization that displays all the sales metrics with filtering and drill-down functionality.
D. Use BigQuery to create multiple materialized views, each focusing on a specific sales metric. Build the dashboard using these views.

Answer: B

Explanation:
Creating a single Explore with all the sales metrics is the Google-recommended approach. This Explore should be designed to include all relevant attributes and dimensions, enabling users to analyze sales data by region, product category, time period, and other filters like sales representatives. With a well-structured Explore, you can efficiently build a dashboard that supports filtering and drill-down functionality. This approach simplifies maintenance, provides a consistent data model, and ensures users have the flexibility to interact with and analyze the data seamlessly within a unified framework.

NEW QUESTION # 41
Your organization stores highly personal data in BigQuery and needs to comply with strict data privacy regulations. You need to ensure that sensitive data values are rendered unreadable whenever an employee leaves the organization. What should you do?

A. Use dynamic data masking and revoke viewer permissions when employees leave the organization.
B. Use AEAD functions and delete keys when employees leave the organization.
C. Use customer-managed encryption keys (CMEK) and delete keys when employees leave the organization.
D. Use column-level access controls with policy tags and revoke viewer permissions when employees leave the organization.

Answer: C

Explanation:
Using customer-managed encryption keys (CMEK) allows you to encrypt highly sensitive data in BigQuery with encryption keys managed by your organization. When an employee leaves the organization, you can render the data unreadable by deleting or revoking access to the encryption keys associated with the data. This approach ensures compliance with strict data privacy regulations by making the data inaccessible without the encryption keys, providing strong control over data access and security.

NEW QUESTION # 42
......

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