THE BEST OF GOOGLE CERTIFICATION ASSOCIATE-DATA-PRACTITIONER EXAM TEST SOFTWARE

The best of Google certification Associate-Data-Practitioner exam test software

The best of Google certification Associate-Data-Practitioner exam test software

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Google Cloud Associate Data Practitioner Sample Questions (Q64-Q69):

NEW QUESTION # 64
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 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.
  • B. 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.
  • C. 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.
  • D. 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.

Answer: B

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 # 65
Your organization has several datasets in BigQuery. The datasets need to be shared with your external partners so that they can run SQL queries without needing to copy the data to their own projects. You have organized each partner's data in its own BigQuery dataset. Each partner should be able to access only their dat a. You want to share the data while following Google-recommended practices. What should you do?

  • A. Grant the partners the bigquery.user IAM role on the BigQuery project.
  • B. Create a Dataflow job that reads from each BigQuery dataset and pushes the data into a dedicated Pub/Sub topic for each partner. Grant each partner the pubsub. subscriber IAM role.
  • C. Use Analytics Hub to create a listing on a private data exchange for each partner dataset. Allow each partner to subscribe to their respective listings.
  • D. Export the BigQuery data to a Cloud Storage bucket. Grant the partners the storage.objectUser IAM role on the bucket.

Answer: C

Explanation:
Using Analytics Hub to create a listing on a private data exchange for each partner dataset is the Google-recommended practice for securely sharing BigQuery data with external partners. Analytics Hub allows you to manage data sharing at scale, enabling partners to query datasets directly without needing to copy the data into their own projects. By creating separate listings for each partner dataset and allowing only the respective partner to subscribe, you ensure that partners can access only their specific data, adhering to the principle of least privilege. This approach is secure, efficient, and designed for scenarios involving external data sharing.


NEW QUESTION # 66
You have a Dataproc cluster that performs batch processing on data stored in Cloud Storage. You need to schedule a daily Spark job to generate a report that will be emailed to stakeholders. You need a fully-managed solution that is easy to implement and minimizes complexity. What should you do?

  • A. Use Cloud Scheduler to trigger the Spark job. and use Cloud Run functions to email the report.
  • B. Use Cloud Composer to orchestrate the Spark job and email the report.
  • C. Use Cloud Run functions to trigger the Spark job and email the report.
  • D. Use Dataproc workflow templates to define and schedule the Spark job, and to email the report.

Answer: D

Explanation:
Using Dataproc workflow templates is a fully-managed and straightforward solution for defining and scheduling your Spark job on a Dataproc cluster. Workflow templates allow you to automate the execution of Spark jobs with predefined steps, including data processing and report generation. You can integrate email notifications by adding a step to the workflow that sends the report using tools like a Cloud Function or external email service. This approach minimizes complexity while leveraging Dataproc's managed capabilities for batch processing.


NEW QUESTION # 67
Your company has several retail locations. Your company tracks the total number of sales made at each location each day. You want to use SQL to calculate the weekly moving average of sales by location to identify trends for each store. Which query should you use?

  • A.
  • B.
  • C.
  • D.

Answer: C

Explanation:
To calculate the weekly moving average of sales by location:
The query must group by store_id (partitioning the calculation by each store).
The ORDER BY date ensures the sales are evaluated chronologically.
The ROWS BETWEEN 6 PRECEDING AND CURRENT ROW specifies a rolling window of 7 rows (1 week if each row represents daily data).
The AVG(total_sales) computes the average sales over the defined rolling window.
Chosen query meets these requirements:


NEW QUESTION # 68
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 Data Viewer IAM role in the Google Cloud project.
  • B. Grant the data analyst the BigQuery Job User IAM role in the Google Cloud project.
  • C. 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.
  • D. 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.

Answer: D

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 # 69
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