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Google Associate Data Practitioner Sample Questions (Q38-Q43):
NEW QUESTION # 38
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 Dataproc to run the transformations on a cluster.
- C. Use Cloud Data Fusion to visually design and manage the pipeline.
- D. Use Dataflow with a custom template for the transformation logic.
Answer: B
NEW QUESTION # 39
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 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.
- 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 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.
- 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
NEW QUESTION # 40
You work for an ecommerce company that has a BigQuery dataset that contains customer purchase history, demographics, and website interactions. You need to build a machine learning (ML) model to predict which customers are most likely to make a purchase in the next month. You have limited engineering resources and need to minimize the ML expertise required for the solution. What should you do?
- A. Use Colab Enterprise to develop a custom model for purchase prediction.
- B. Use BigQuery ML to create a logistic regression model for purchase prediction.
- C. Export the data to Cloud Storage, and use AutoML Tables to build a classification model for purchase prediction.
- D. Use Vertex Al Workbench to develop a custom model for purchase prediction.
Answer: B
NEW QUESTION # 41
Your company has an on-premises file server with 5 TB of data that needs to be migrated to Google Cloud.
The network operations team has mandated that you can only use up to 250 Mbps of the total available bandwidth for the migration. You need to perform an online migration to Cloud Storage. What should you do?
- A. Use the gcloud storage cp command to copy all files from on- premises to Cloud Storage using the -- daisy-chain option.
- B. Use the gcloud storage cp command to copy all files from on- premises to Cloud Storage using the --no- clobber option.
- C. Request a Transfer Appliance, copy the data to the appliance, and ship it back to Google Cloud.
- D. Use Storage Transfer Service to configure an agent-based transfer. Set the appropriate bandwidth limit for the agent pool.
Answer: D
NEW QUESTION # 42
You are migrating data from a legacy on-premises MySQL database to Google Cloud. The database contains various tables with different data types and sizes, including large tables with millions of rows and transactional dat a. You need to migrate this data while maintaining data integrity, and minimizing downtime and cost. What should you do?
- A. Use Cloud Data Fusion to migrate the MySQL database to MySQL on Compute Engine.
- B. Set up a Cloud Composer environment to orchestrate a custom data pipeline. Use a Python script to extract data from the MySQL database and load it to MySQL on Compute Engine.
- C. Use Database Migration Service to replicate the MySQL database to a Cloud SQL for MySQL instance.
- D. Export the MySQL database to CSV files, transfer the files to Cloud Storage by using Storage Transfer Service, and load the files into a Cloud SQL for MySQL instance.
Answer: C
NEW QUESTION # 43
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