Improve Your Knowledge with Associate Data Practitioner Exam Dumps

Practicing with Associate Data Practitioner questions can help you identify areas where you need to improve your knowledge. By answering Associate Data Practitioner questions and reviewing your responses, you can identify gaps in your understanding and focus your study efforts on those areas. The Associate Data Practitioner exam has a strict time limit, and you need to manage your time effectively to answer all the questions. Practicing with Google Associate Data Practitioner dumps questions can help you develop time management skills by simulating the exam's time constraints. You'll learn how to pace yourself, manage your time effectively, and ensure that you complete the Associate Data Practitioner exam within the allotted time. Test Google Associate Data Practitioner exam free dumps below.

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1. Your retail organization stores sensitive application usage data in Cloud Storage. You need to encrypt the data without the operational overhead of managing encryption keys.

What should you do?

Use Google-managed encryption keys (GMEK).

Use customer-managed encryption keys (CMEK).

Use customer-supplied encryption keys (CSEK).

Use customer-supplied encryption keys (CSEK) for the sensitive data and customer-managed encryption keys (CMEK) for the less sensitive data.

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2. 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?

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

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 I

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

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

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3. Your team wants to create a monthly report to analyze inventory data that is updated daily. You need to aggregate the inventory counts by using only the most recent month of data, and save the results to be used in a Looker Studio dashboard.

What should you do?

Create a materialized view in BigQuery that uses the SUM( ) function and the DATE_SUB( ) function.

Create a saved query in the BigQuery console that uses the SUM( ) function and the DATE_SUB( ) function. Re-run the saved query every month, and save the results to a BigQuery table.

Create a BigQuery table that uses the SUM( ) function and the _PARTITIONDATE filter.

Create a BigQuery table that uses the SUM( ) function and the DATE_DIFF( ) function.

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4. 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.

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5. Your organization needs to implement near real-time analytics for thousands of events arriving each second in Pub/Sub. The incoming messages require transformations. You need to configure a pipeline that processes, transforms, and loads the data into BigQuery while minimizing development time.

What should you do?

Use a Google-provided Dataflow template to process the Pub/Sub messages, perform transformations, and write the results to BigQuery.

Create a Cloud Data Fusion instance and configure Pub/Sub as a source. Use Data Fusion to process the Pub/Sub messages, perform transformations, and write the results to BigQuery.

Load the data from Pub/Sub into Cloud Storage using a Cloud Storage subscription. Create a Dataproc cluster, use PySpark to perform transformations in Cloud Storage, and write the results to BigQuery.

Use Cloud Run functions to process the Pub/Sub messages, perform transformations, and write the results to BigQuery.

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6. 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 SQ

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.

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7. 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?

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.

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.

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.

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.

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8. Social media activity: Cloud Storage

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9. Your team is building several data pipelines that contain a collection of complex tasks and dependencies that you want to execute on a schedule, in a specific order. The tasks and dependencies consist of files in Cloud Storage, Apache Spark jobs, and data in BigQuery. You need to design a system that can schedule and automate these data processing tasks using a fully managed approach.

What should you do?

Use Cloud Scheduler to schedule the jobs to run.

Use Cloud Tasks to schedule and run the jobs asynchronously.

Create directed acyclic graphs (DAGs) in Cloud Composer. Use the appropriate operators to connect to Cloud Storage, Spark, and BigQuery.

Create directed acyclic graphs (DAGs) in Apache Airflow deployed on Google Kubernetes Engine. Use the appropriate operators to connect to Cloud Storage, Spark, and BigQuery.

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10. 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?

Load the data into BigQuery using Dataproc. Use Apache Spark to translate the reviews by invoking the Cloud Translation AP

Set BigQuery as the sink.U

Use a Dataflow templates pipeline to translate the reviews using the Cloud Translation AP

Set BigQuery as the sink.

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.

Load the data into BigQuery using a Cloud Run function. Create a BigQuery remote function that invokes the Cloud Translation AP

Use a scheduled query to translate new reviews.

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11. 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.

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12. 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.

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13. 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?

Create a custom quota for each analyst in BigQuery.

Create a single reservation by using BigQuery editions. Assign all analysts to the reservation.

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.

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.

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14. 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?

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.

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

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.

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.

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15. You are using your own data to demonstrate the capabilities of BigQuery to your organization’s leadership team. You need to perform a one- time load of the files stored on your local machine into BigQuery using as little effort as possible.

What should you do?

Write and execute a Python script using the BigQuery Storage Write API library.

Create a Dataproc cluster, copy the files to Cloud Storage, and write an Apache Spark job using the spark-bigquery-connector.

Execute the bq load command on your local machine.

Create a Dataflow job using the Apache Beam FileIO and BigQueryIO connectors with a local runner.

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16. Your organization has decided to migrate their existing enterprise data warehouse to BigQuery. The existing data pipeline tools already support connectors to BigQuery. You need to identify a data migration approach that optimizes migration speed.

What should you do?

Create a temporary file system to facilitate data transfer from the existing environment to Cloud Storage. Use Storage Transfer Service to migrate the data into BigQuery.

Use the Cloud Data Fusion web interface to build data pipelines. Create a directed acyclic graph (DAG) that facilitates pipeline orchestration.

Use the existing data pipeline tool’s BigQuery connector to reconfigure the data mapping.

Use the BigQuery Data Transfer Service to recreate the data pipeline and migrate the data into BigQuery.

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17. 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?

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.

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.

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

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

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18. 1.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)





B)





C)





D)

Option A

Option B

Option C

Option D

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19. You are working with a large dataset of customer reviews stored in Cloud Storage. The dataset contains several inconsistencies, such as missing values, incorrect data types, and duplicate entries. You need to clean the data to ensure that it is accurate and consistent before using it for analysis.

What should you do?

Use the PythonOperator in Cloud Composer to clean the data and load it into BigQuery. Use SQL for analysis.

Use BigQuery to batch load the data into BigQuery. Use SQL for cleaning and analysis.

Use Storage Transfer Service to move the data to a different Cloud Storage bucket. Use event triggers to invoke Cloud Run functions to load the data into BigQuery. Use SQL for analysis.

Use Cloud Run functions to clean the data and load it into BigQuery. Use SQL for analysis.

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20. Your organization stores highly personal data in BigQuery and needs to comply with strict data privacy regulations. You need to ensure that sensitive data values are rendered unreadable whenever an employee leaves the organization.

What should you do?

Use AEAD functions and delete keys when employees leave the organization.

Use dynamic data masking and revoke viewer permissions when employees leave the organization.

Use customer-managed encryption keys (CMEK) and delete keys when employees leave the organization.

Use column-level access controls with policy tags and revoke viewer permissions when employees leave the organization.

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21. Your team needs to analyze large datasets stored in BigQuery to identify trends in user behavior. The analysis will involve complex statistical calculations, Python packages, and visualizations. You need to recommend a managed collaborative environment to develop and share the analysis.

What should you recommend?

Create a Colab Enterprise notebook and connect the notebook to BigQuery. Share the notebook with your team. Analyze the data and generate visualizations in Colab Enterprise.

Create a statistical model by using BigQuery M

Share the query with your team. Analyze the data and generate visualizations in Looker Studio.

Create a Looker Studio dashboard and connect the dashboard to BigQuery. Share the dashboard with your team. Analyze the data and generate visualizations in Looker Studio.

Connect Google Sheets to BigQuery by using Connected Sheets. Share the Google Sheet with your team. Analyze the data and generate visualizations in Gooqle Sheets.

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22. You are a data analyst working with sensitive customer data in BigQuery. You need to ensure that only authorized personnel within your organization can query this data, while following the principle of least privilege.

What should you do?

Enable access control by using IAM roles.

Encrypt the data by using customer-managed encryption keys (CMEK).

Update dataset privileges by using the SQL GRANT statement.

Export the data to Cloud Storage, and use signed URLs to authorize access.

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23. Your company’s customer support audio files are stored in a Cloud Storage bucket. You plan to analyze the audio files’ metadata and file content within BigQuery to create inference by using BigQuery ML. You need to create a corresponding table in BigQuery that represents the bucket containing the audio files.

What should you do?

Create an external table.

Create a temporary table.

Create a native table.

Create an object table.

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