Free Google Cloud Associate Data Practitioner Exam Associate-Data-Practitioner Exam Practice Test

Your team uses the Google Ads platform to visualize metrics. You want to export the data to BigQuery to get more granular insights. You need to execute a one-time transfer of historical data and automatically update data daily. You want a solution that is low-code, serverless, and requires minimal maintenance. What should you do?

• Export the historical data to BigQuery by using BigQuery Data Transfer Service. Use Cloud Composer for daily automation.
• Export the historical data to Cloud Storage by using Storage Transfer Service. Use Pub/Sub to trigger a Dataflow template that loads data for daily automation.
• Export the historical data as a CSV file. Import the file into BigQuery for analysis. Use Cloud Composer for daily automation.
• Export the historical data to BigQuery by using BigQuery Data Transfer Service. Use BigQuery Data Transfer Service for daily automation.

You are a Looker analyst. You need to add a new field to your Looker report that generates SQL that will run against your company's database. You do not have the Develop permission. What should you do?

• Create a new field in the LookML layer, refresh your report, and select your new field from the field picker.
• Create a calculated field using the Add a field option in Looker Studio, and add it to your report.
• Create a table calculation from the field picker in Looker, and add it to your report.
• Create a custom field from the field picker in Looker, and add it to your report.

Your company uses Looker to generate and share reports with various stakeholders. You have a complex dashboard with several visualizations that needs to be delivered to specific stakeholders on a recurring basis, with customized filters applied for each recipient. You need an efficient and scalable solution to automate the delivery of this customized dashboard. You want to follow the Google-recommended approach. What should you do?

• Create a separate LookML model for each stakeholder with predefined filters, and schedule the dashboards using the Looker Scheduler.
• Create a script using the Looker Python SDK, and configure user attribute filter values. Generate a new scheduled plan for each stakeholder.
• Embed the Looker dashboard in a custom web application, and use the application's scheduling features to send the report with personalized filters.
• Use the Looker Scheduler with a user attribute filter on the dashboard, and send the dashboard with personalized filters to each stakeholder based on their attributes.

You want to process and load a daily sales CSV file stored in Cloud Storage into BigQuery for downstream reporting. You need to quickly build a scalable data pipeline that transforms the data while providing insights into data quality issues. What should you do?

• Create a batch pipeline in Cloud Data Fusion by using a Cloud Storage source and a BigQuery sink.
• Load the CSV file as a table in BigQuery, and use scheduled queries to run SQL transformation scripts.
• Load the CSV file as a table in BigQuery. Create a batch pipeline in Cloud Data Fusion by using a BigQuery source and sink.
• Create a batch pipeline in Dataflow by using the Cloud Storage CSV file to BigQuery batch template.

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?

• Create authorized views in the team’s Google Cloud project that is only accessible by the team.
• Create a private exchange using Analytics Hub with data egress restriction, and grant access to the team members.
• Enable domain restricted sharing on the project. Grant the team members the BigQuery Data Viewer IAM role on the dataset.
• Export the dataset to a Cloud Storage bucket in the team’s Google Cloud project that is only
• accessible by the team.

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?

• 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.
• 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.
• 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.
• 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.

You are constructing a data pipeline to process sensitive customer data stored in a Cloud Storage bucket. You need to ensure that this data remains accessible, even in the event of a single-zone outage. What should you do?

• Set up a Cloud CDN in front of the bucket.
• Enable Object Versioning on the bucket.
• Store the data in a multi-region bucket.
• Store the data in Nearline storaqe.

You used BigQuery ML to build a customer purchase propensity model six months ago. You want to compare the current serving data with the historical serving data to determine whether you need to retrain the model. What should you do?

• Compare the two different models.
• Evaluate the data skewness.
• Evaluate data drift.
• Compare the confusion matrix.

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, productcategory, 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?

• Create multiple Explores, each focusing on each sales metric. Link the Explores together in a dashboard using drill-down functionality.
• Use BigQuery to create multiple materialized views, each focusing on a specific sales metric. Build the dashboard using these views.
• Create a single Explore with all sales metrics. Build the dashboard using this Explore.
• 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 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?

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