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Google Cloud Associate Data Practitioner Sample Questions (Q58-Q63):

NEW QUESTION # 58
You manage a large amount of data in Cloud Storage, including raw data, processed data, and backups. Your organization is subject to strict compliance regulations that mandate data immutability for specific data types. You want to use an efficient process to reduce storage costs while ensuring that your storage strategy meets retention requirements. What should you do?

  • A. Move objects to different storage classes based on their age and access patterns. Use Cloud Key Management Service (Cloud KMS) to encrypt specific objects with customer-managed encryption keys (CMEK) to meet immutability requirements.
  • B. Configure lifecycle management rules to transition objects to appropriate storage classes based on access patterns. Set up Object Versioning for all objects to meet immutability requirements.
  • C. Create a Cloud Run function to periodically check object metadata, and move objects to the appropriate storage class based on age and access patterns. Use object holds to enforce immutability for specific objects.
  • D. Use object holds to enforce immutability for specific objects, and configure lifecycle management rules to transition objects to appropriate storage classes based on age and access patterns.

Answer: D

Explanation:
Using object holds and lifecycle management rules is the most efficient and compliant strategy for this scenario because:
Immutability: Object holds (temporary or event-based) ensure that objects cannot be deleted or overwritten, meeting strict compliance regulations for data immutability.
Cost efficiency: Lifecycle management rules automatically transition objects to more cost-effective storage classes based on their age and access patterns.
Compliance and automation: This approach ensures compliance with retention requirements while reducing manual effort, leveraging built-in Cloud Storage features.


NEW QUESTION # 59
You are designing a pipeline to process data files that arrive in Cloud Storage by 3:00 am each day. Data processing is performed in stages, where the output of one stage becomes the input of the next. Each stage takes a long time to run. Occasionally a stage fails, and you have to address the problem. You need to ensure that the final output is generated as quickly as possible. What should you do?

  • A. Design the workflow as a Cloud Workflow instance. Code the workflow to jump to a given stage based on an input parameter. Rerun the workflow after correcting any stage output data errors.
  • B. Design a Spark program that runs under Dataproc. Code the program to wait for user input when an error is detected. Rerun the last action after correcting any stage output data errors.
  • C. Design the processing as a directed acyclic graph (DAG) in Cloud Composer. Clear the state of the failed task after correcting any stage output data errors.
  • D. Design the pipeline as a set of PTransforms in Dataflow. Restart the pipeline after correcting any stage output data errors.

Answer: C

Explanation:
Using Cloud Composer to design the processing pipeline as a Directed Acyclic Graph (DAG) is the most suitable approach because:
Fault tolerance: Cloud Composer (based on Apache Airflow) allows for handling failures at specific stages. You can clear the state of a failed task and rerun it without reprocessing the entire pipeline.
Stage-based processing: DAGs are ideal for workflows with interdependent stages where the output of one stage serves as input to the next.
Efficiency: This approach minimizes downtime and ensures that only failed stages are rerun, leading to faster final output generation.


NEW QUESTION # 60
Your organization has several datasets in their data warehouse in BigQuery. Several analyst teams in different departments use the datasets to run queries. Your organization is concerned about the variability of their monthly BigQuery costs. You need to identify a solution that creates a fixed budget for costs associated with the queries run by each department. What should you do?

  • A. Create a single reservation by using BigQuery editions. Assign all analysts to the reservation.
  • B. Create a custom quota for each analyst in BigQuery.
  • C. Assign each analyst to a separate project associated with their department. Create a single reservation for each department by using BigQuery editions. Create assignments for each project in the appropriate reservation.
  • D. Assign each analyst to a separate project associated with their department. Create a single reservation by using BigQuery editions. Assign all projects to the reservation.

Answer: C

Explanation:
Assigning each analyst to a separate project associated with their department and creating a single reservation for each department using BigQuery editions allows for precise cost management. By assigning each project to its department's reservation, you can allocate fixed compute resources and budgets for each department, ensuring that their query costs are predictable and controlled. This approach aligns with your organization's goal of creating a fixed budget for query costs while maintaining departmental separation and accountability.


NEW QUESTION # 61
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?

  • A. Create an event-driven Cloud Run function to trigger a data transformation pipeline to run. Load the transformed activity logs into a BigQuery table for reporting.
  • B. Create a Dataflow subscription to the Pub/Sub topic, and transform the activity logs. Load the transformed data into a BigQuery table for reporting.
  • C. Create a Cloud Storage subscription to the Pub/Sub topic. Load the activity logs into a bucket using the Avro file format. Use Dataflow to transform the data, and load it into a BigQuery table for reporting.
  • D. Create a BigQuery subscription to the Pub/Sub topic, and load the activity logs into the table. Create a materialized view in BigQuery using SQL to transform the data for reporting

Answer: B

Explanation:
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.


NEW QUESTION # 62
Your company's ecommerce website collects product reviews from customers. The reviews are loaded as CSV files daily to a Cloud Storage bucket. The reviews are in multiple languages and need to be translated to Spanish. You need to configure a pipeline that is serverless, efficient, and requires minimal maintenance. What should you do?

  • A. Load the data into BigQuery using Dataproc. Use Apache Spark to translate the reviews by invoking the Cloud Translation API. Set BigQuery as the sink.U
  • B. Use a Dataflow templates pipeline to translate the reviews using the Cloud Translation API. Set BigQuery as the sink.
  • C. Load the data into BigQuery using a Cloud Run function. Create a BigQuery remote function that invokes the Cloud Translation API. Use a scheduled query to translate new reviews.
  • D. Load the data into BigQuery using a Cloud Run function. Use the BigQuery ML create model statement to train a translation model. Use the model to translate the product reviews within BigQuery.

Answer: C

Explanation:
Loading the data into BigQuery using a Cloud Run function and creating a BigQuery remote function that invokes the Cloud Translation API is a serverless and efficient approach. With this setup, you can use a scheduled query in BigQuery to invoke the remote function and translate new product reviews on a regular basis. This solution requires minimal maintenance, as BigQuery handles storage and querying, and the Cloud Translation API provides accurate translations without the need for custom ML model development.


NEW QUESTION # 63
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