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Google Associate-Data-Practitioner - Google Cloud Associate Data Practitioner (ADP Exam)

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Total 106 questions

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 Storage Transfer Service to configure an agent-based transfer. Set the appropriate bandwidth limit for the agent pool.

B.

Use the gcloud storage cp command to copy all files from on-premises to Cloud Storage using the --daisy-chain option.

C.

Request a Transfer Appliance, copy the data to the appliance, and ship it back to Google Cloud.

D.

Use the gcloud storage cp command to copy all files from on-premises to Cloud Storage using the --no-clobber option.

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 BigQuery ML to create a logistic regression model for purchase prediction.

B.

Use Vertex AI Workbench to develop a custom model for purchase prediction.

C.

Use Colab Enterprise to develop a custom model for purchase prediction.

D.

Export the data to Cloud Storage, and use AutoML Tables to build a classification model for purchase prediction.

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 data. You need to migrate this data while maintaining data integrity, and minimizing downtime and cost. What should you do?

A.

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.

B.

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.

C.

Use Database Migration Service to replicate the MySQL database to a Cloud SQL for MySQL instance.

D.

Use Cloud Data Fusion to migrate the MySQL database to MySQL on Compute Engine.

You have a Dataflow pipeline that processes website traffic logs stored in Cloud Storage and writes the processed data to BigQuery. You noticed that the pipeline is failing intermittently. You need to troubleshoot the issue. What should you do?

A.

Use Cloud Logging to identify error groups in the pipeline's logs. Use Cloud Monitoring to create a dashboard that tracks the number of errors in each group.

B.

Use Cloud Logging to create a chart displaying the pipeline’s error logs. Use Metrics Explorer to validate the findings from the chart.

C.

Use Cloud Logging to view error messages in the pipeline's logs. Use Cloud Monitoring to analyze the pipeline's metrics, such as CPU utilization and memory usage.

D.

Use the Dataflow job monitoring interface to check the pipeline's status every hour. Use Cloud Profiler to analyze the pipeline’s metrics, such as CPU utilization and memory usage.

Your retail company wants to predict customer churn using historical purchase data stored in BigQuery. The dataset includes customer demographics, purchase history, and a label indicating whether the customer churned or not. You want to build a machine learning model to identify customers at risk of churning. You need to create and train a logistic regression model for predicting customer churn, using the customer_data table with the churned column as the target label. Which BigQuery ML query should you use?

A.

CREATE OR REPLACE MODEL churn_prediction_model OPTIONS(model_uype='logisric_reg') AS SELECT * from cusromer_data;

B.

CREATE OR REPLACE MODEL churn_prediction_model OPTIONS (rr.odel_type=' logisric_reg *) AS select * except(churned), churned AS label FROM customer_data;

C.

CREATE OR REPLACE MODEL churn_prediction_model options (model type=’logistic_reg’) AS select churned as label FROM customer_data;

D.

CREATE OR REPLACE MODEL churn_prediction_model options(model_type='logistic_reg*) as select ’ except(churned) FROM customer data;

You need to create a data pipeline for a new application. Your application will stream data that needs to be enriched and cleaned. Eventually, the data will be used to train machine learning models. You need to determine the appropriate data manipulation methodology and which Google Cloud services to use in this pipeline. What should you choose?

A.

ETL; Dataflow -> BigQuery

B.

ETL; Cloud Data Fusion -> Cloud Storage

C.

ELT; Cloud Storage -> Bigtable

D.

ELT; Cloud SQL -> Analytics Hub

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 Dataflow subscription to the Pub/Sub topic, and transform the activity logs. Load the transformed data into a BigQuery table for reporting.

B.

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.

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

Another team in your organization is requesting access to a BigQuery dataset. You need to share the dataset with the team while minimizing the risk of unauthorized copying of data. You also want to create a reusable framework in case you need to share this data with other teams in the future. What should you do?

A.

Create authorized views in the team’s Google Cloud project that is only accessible by the team.

B.

Create a private exchange using Analytics Hub with data egress restriction, and grant access to the team members.

C.

Enable domain restricted sharing on the project. Grant the team members the BigQuery Data Viewer IAM role on the dataset.

D.

Export the dataset to a Cloud Storage bucket in the team’s Google Cloud project that is only accessible by the team.

Your company uses Looker to visualize and analyze sales data. You need to create a dashboard that displays sales metrics, such as sales by region, product category, and time period. Each metric relies on its own set of attributes distributed across several tables. You need to provide users the ability to filter the data by specific sales representatives and view individual transactions. You want to follow the Google-recommended approach. What should you do?

A.

Create multiple Explores, each focusing on each sales metric. Link the Explores together in a dashboard using drill-down functionality.

B.

Use BigQuery to create multiple materialized views, each focusing on a specific sales metric. Build the dashboard using these views.

C.

Create a single Explore with all sales metrics. Build the dashboard using this Explore.

D.

Use Looker's custom visualization capabilities to create a single visualization that displays all the sales metrics with filtering and drill-down functionality.

Your organization uses scheduled queries to perform transformations on data stored in BigQuery. You discover that one of your scheduled queries has failed. You need to troubleshoot the issue as quickly as possible. What should you do?

A.

Navigate to the Logs Explorer page in Cloud Logging. Use filters to find the failed job, and analyze the error details.

B.

Set up a log sink using the gcloud CLI to export BigQuery audit logs to BigQuery. Query those logs to identify the error associated with the failed job ID.

C.

Request access from your admin to the BigQuery information_schema. Query the jobs view with the failed job ID, and analyze error details.

D.

Navigate to the Scheduled queries page in the Google Cloud console. Select the failed job, and analyze the error details.