Associate-Data-Practitioner Pdf Version & Test Associate-Data-Practitioner Topics Pdf

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Google Cloud Associate Data Practitioner Sample Questions (Q65-Q70):

NEW QUESTION # 65
Your organization has decided to move their on-premises Apache Spark-based workload to Google Cloud. You want to be able to manage the code without needing to provision and manage your own cluster. What should you do?

A. Migrate the Spark jobs to Dataproc Serverless.
B. Migrate the Spark jobs to Dataproc on Google Kubernetes Engine.
C. Configure a Google Kubernetes Engine cluster with Spark operators, and deploy the Spark jobs.
D. Migrate the Spark jobs to Dataproc on Compute Engine.

Answer: A

Explanation:
Migrating the Spark jobs to Dataproc Serverless is the best approach because it allows you to run Spark workloads without the need to provision or manage clusters. Dataproc Serverless automatically scales resources based on workload requirements, simplifying operations and reducing administrative overhead. This solution is ideal for organizations that want to focus on managing their Spark code without worrying about the underlying infrastructure. It is cost-effective and fully managed, aligning well with the goal of minimizing cluster management.

NEW QUESTION # 66
Your organization has highly sensitive data that gets updated once a day and is stored across multiple datasets in BigQuery. You need to provide a new data analyst access to query specific data in BigQuery while preventing access to sensitive dat a. What should you do?

A. Grant the data analyst the BigQuery Job User IAM role in the Google Cloud project.
B. Create a materialized view with the limited data in a new dataset. Grant the data analyst BigQuery Data Viewer IAM role in the dataset and the BigQuery Job User IAM role in the Google Cloud project.
C. Grant the data analyst the BigQuery Data Viewer IAM role in the Google Cloud project.
D. Create a new Google Cloud project, and copy the limited data into a BigQuery table. Grant the data analyst the BigQuery Data Owner IAM role in the new Google Cloud project.

Answer: B

Explanation:
Creating a materialized view with the limited data in a new dataset and granting the data analyst the BigQuery Data Viewer role on the dataset and the BigQuery Job User role in the project ensures that the analyst can query only the non-sensitive data without access to sensitive datasets. Materialized views allow you to predefine what subset of data is visible, providing a secure and efficient way to control access while maintaining compliance with data governance policies. This approach follows the principle of least privilege while meeting the requirements.

NEW QUESTION # 67
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. Create a Cloud Data Fusion job to process and load the data from Cloud Storage into BigQuery. Create an OBJECT_FINALI ZE notification in Pub/Sub, and trigger a Cloud Run function to start the Cloud Data Fusion job as soon as new files are loaded.
B. 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.
C. 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.
D. Use Dataflow to implement a streaming pipeline using an OBJECT_FINALIZE notification from Pub/Sub to read the data from Cloud Storage, perform the transformations, and write the data to BigQuery.

Answer: D

Explanation:
Using Dataflow to implement a streaming pipeline triggered by an OBJECT_FINALIZE notification 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 # 68
You work for a financial organization that stores transaction data in BigQuery. Your organization has a regulatory requirement to retain data for a minimum of seven years for auditing purposes. You need to ensure that the data is retained for seven years using an efficient and cost-optimized approach. What should you do?

A. Create a partition by transaction date, and set the partition expiration policy to seven years.
B. Set the table-level retention policy in BigQuery to seven years.
C. Set the dataset-level retention policy in BigQuery to seven years.
D. Export the BigQuery tables to Cloud Storage daily, and enforce a lifecycle management policy that has a seven-year retention rule.

Answer: B

Explanation:
Setting a table-level retention policy in BigQuery to seven years is the most efficient and cost-optimized solution to meet the regulatory requirement. A table-level retention policy ensures that the data cannot be deleted or overwritten before the specified retention period expires, providing compliance with auditing requirements while keeping the data within BigQuery for easy access and analysis. This approach avoids the complexity and additional costs of exporting data to Cloud Storage.

NEW QUESTION # 69
You manage a Cloud Storage bucket that stores temporary files created during data processing. These temporary files are only needed for seven days, after which they are no longer needed. To reduce storage costs and keep your bucket organized, you want to automatically delete these files once they are older than seven days. What should you do?

A. Configure a Cloud Storage lifecycle rule that automatically deletes objects older than seven days.
B. Create a Cloud Run function that runs daily and deletes files older than seven days.
C. Develop a batch process using Dataflow that runs weekly and deletes files based on their age.
D. Set up a Cloud Scheduler job that invokes a weekly Cloud Run function to delete files older than seven days.

Answer: A

Explanation:
Configuring a Cloud Storage lifecycle rule to automatically delete objects older than seven days is the best solution because:
Built-in feature: Cloud Storage lifecycle rules are specifically designed to manage object lifecycles, such as automatically deleting or transitioning objects based on age.
No additional setup: It requires no external services or custom code, reducing complexity and maintenance.
Cost-effective: It directly achieves the goal of deleting files after seven days without incurring additional compute costs.

NEW QUESTION # 70
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ASSOCIATE-DATA-PRACTITIONER PDF VERSION & TEST ASSOCIATE-DATA-PRACTITIONER TOPICS PDF

Associate-Data-Practitioner Pdf Version & Test Associate-Data-Practitioner Topics Pdf