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Google Cloud Associate Data Practitioner Sample Questions (Q17-Q22):

NEW QUESTION # 17
You work for an online retail company. Your company collects customer purchase data in CSV files and pushes them to Cloud Storage every 10 minutes. The data needs to be transformed and loaded into BigQuery for analysis. The transformation involves cleaning the data, removing duplicates, and enriching it with product information from a separate table in BigQuery. You need to implement a low-overhead solution that initiates data processing as soon as the files are loaded into Cloud Storage. What should you do?

A. Use Dataflow to implement a streaming pipeline using anOBJECT_FINALIZEnotification from Pub
/Sub to read the data from Cloud Storage, perform the transformations, and write the data to BigQuery.
B. Use Cloud Composer sensors to detect files loading in Cloud Storage. Create a Dataproc cluster, and use a Composer task to execute a job on the cluster to process and load the data into BigQuery.
C. Schedule a direct acyclic graph (DAG) in Cloud Composer to run hourly to batch load the data from Cloud Storage to BigQuery, and process the data in BigQuery using SQL.
D. Create a Cloud Data Fusion job to process and load the data from Cloud Storage into BigQuery. Create anOBJECT_FINALIZE notification in Pub/Sub, and trigger a Cloud Run function to start the Cloud Data Fusion job as soon as new files are loaded.

Answer: A

Explanation:
UsingDataflowto implement a streaming pipeline triggered by anOBJECT_FINALIZEnotification from Pub
/Sub is the best solution. This approach automatically starts the data processing as soon as new files are uploaded to Cloud Storage, ensuring low latency. Dataflow can handle the data cleaning, deduplication, and enrichment with product information from the BigQuery table in a scalable and efficient manner. This solution minimizes overhead, as Dataflow is a fully managed service, and it is well-suited for real-time or near-real-time data pipelines.

NEW QUESTION # 18
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. Forward unacknowledged messages to a dead-letter topic.
B. Retry messages until they are acknowledged.
C. Implement flow control on the subscribers
D. Seek back to the last acknowledged message.

Answer: C

Explanation:
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.

NEW QUESTION # 19
Your organization has a BigQuery dataset that contains sensitive employee information such as salaries and performance reviews. The payroll specialist in the HR department needs to have continuous access to aggregated performance data, but they do not need continuous access to other sensitive dat a. You need to grant the payroll specialist access to the performance data without granting them access to the entire dataset using the simplest and most secure approach. What should you do?

A. Create a SQL query with the aggregated performance data. Export the results to an Avro file in a Cloud Storage bucket. Share the bucket with the payroll specialist.
B. Create row-level and column-level permissions and policies on the table that contains performance data in the dataset. Provide the payroll specialist with the appropriate permission set.
C. Create a table with the aggregated performance data. Use table-level permissions to grant access to the payroll specialist.
D. Use authorized views to share query results with the payroll specialist.

Answer: D

Explanation:
Using authorized views is the simplest and most secure way to grant the payroll specialist access to aggregated performance data without exposing the entire dataset. Authorized views allow you to create a view in BigQuery that contains only the query results for the aggregated performance data. The payroll specialist can query the view without being granted access to the underlying sensitive data. This approach ensures security, adheres to the principle of least privilege, and eliminates the need to manage complex row-level or column-level permissions.

NEW QUESTION # 20
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?

A. Use Cloud Run functions to trigger data validation and cleaning routines when new data arrives in Cloud Storage.
B. Use Dataflow to create a streaming pipeline that includes validation and transformation steps.
C. Write custom scripts in Python to validate and clean the data outside of Google Cloud. Load the cleaned data into BigQuery.
D. Load the raw data into BigQuery using Cloud Storage as a staging area, and use SQL queries in BigQuery to validate and clean the data.

Answer: B

Explanation:
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.

NEW QUESTION # 21
Your organization is building a new application on Google Cloud. Several data files will need to be stored in Cloud Storage. Your organization has approved only two specific cloud regions where these data files can reside. You need to determine a Cloud Storage bucket strategy that includes automated high availability.
What should you do?

A. Create a multi-region bucket, and upload the files to this bucket.
B. Create a single-region bucket in each of the two regions, and use Storage Transfer Service to replicate the data across the buckets in both regions.
C. Create a single-region bucket in each of the two regions, and use the gcloud storage command to replicate the data across the buckets in both regions.
D. Create a dual-region bucket, and upload the files to this bucket.

Answer: D

Explanation:
Comprehensive and Detailed In-Depth Explanation:
The strategy requires storage in two specific regions with automated high availability (HA). Cloud Storage location options dictate the solution:
* Option A: A dual-region bucket (e.g., us-west1 and us-east1) replicates data synchronously across two user-specified regions, ensuring HA without manual intervention. It's fully automated and meets the requirement.
* Option B: Two single-region buckets with gcloud storage replication is manual, not automated, and lacks real-time HA (requires scripting and monitoring).
* Option C: Multi-region buckets (e.g., us) span multiple regions within a geography but don't let you specify exactly two regions, potentially violating the restriction.

NEW QUESTION # 22
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