ADP practice questions 21–30

1. Question 21 · 90 in bank

   Your organization uses Dataflow pipelines to process real-time financial transactions. You discover that one of your Dataflow jobs has failed. You need to troubleshoot the issue as quickly as possible. What should you do?

   • ASet up a Cloud Monitoring dashboard to track key Dataflow metrics, such as data throughput, error rates, and resource utilization.
   • BCreate a custom script to periodically poll the Dataflow API for job status updates, and send email alerts if any errors are identified.
   • CNavigate to the Dataflow Jobs page in the Google Cloud console. Use the job logs and worker logs to identify the error.
   • DUse the gcloud CLI tool to retrieve job metrics and logs, and analyze them for errors and performance bottlenecks.

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   Answer: C

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2. Question 22 · 90 in bank

   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?

   • ACreate a separate LookML model for each stakeholder with predefined filters, and schedule the dashboards using the Looker Scheduler.
   • BCreate a script using the Looker Python SDK, and configure user attribute filter values. Generate a new scheduled plan for each stakeholder.
   • CEmbed the Looker dashboard in a custom web application, and use the application's scheduling features to send the report with personalized filters.
   • DUse 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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   Answer: D

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3. Question 23 · 90 in bank

   Your company is building a near real-time streaming pipeline to process JSON telemetry data from small appliances. You need to process messages arriving at a Pub/Sub topic, capitalize letters in the serial number field, and write results to BigQuery. You want to use a managed service and write a minimal amount of code for underlying transformations. What should you do?

   • AUse a Pub/Sub to BigQuery subscription, write results directly to BigQuery, and schedule a transformation query to run every five minutes.
   • BUse a Pub/Sub to Cloud Storage subscription, write a Cloud Run service that is triggered when objects arrive in the bucket, performs the transformations, and writes the results to BigQuery.
   • CUse the “Pub/Sub to BigQuery” Dataflow template with a UDF, and write the results to BigQuery.
   • DUse a Pub/Sub push subscription, write a Cloud Run service that accepts the messages, performs the transformations, and writes the results to BigQuery.

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   Answer: C

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4. Question 24 · 90 in bank

   You are predicting customer churn for a subscription-based service. You have a 50 PB historical customer dataset in BigQuery that includes demographics, subscription information, and engagement metrics. You want to build a churn prediction model with minimal overhead. You want to follow the Google-recommended approach. What should you do?

   • AExport the data from BigQuery to a local machine. Use scikit-learn in a Jupyter notebook to build the churn prediction model.
   • BUse Dataproc to create a Spark cluster. Use the Spark MLlib within the cluster to build the churn prediction model.
   • CCreate a Looker dashboard that is connected to BigQuery. Use LookML to predict churn.
   • DUse the BigQuery Python client library in a Jupyter notebook to query and preprocess the data in BigQuery. Use the CREATE MODEL statement in BigQueryML to train the churn prediction model.

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   Answer: D

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5. Question 25 · 90 in bank

   You are a data analyst at your organization. You have been given a BigQuery dataset that includes customer information. The dataset contains inconsistencies and errors, such as missing values, duplicates, and formatting issues. You need to effectively and quickly clean the data. What should you do?

   • ADevelop a Dataflow pipeline to read the data from BigQuery, perform data quality rules and transformations, and write the cleaned data back to BigQuery.
   • BUse Cloud Data Fusion to create a data pipeline to read the data from BigQuery, perform data quality transformations, and write the clean data back to BigQuery.
   • CExport the data from BigQuery to CSV files. Resolve the errors using a spreadsheet editor, and re-import the cleaned data into BigQuery.
   • DUse BigQuery's built-in functions to perform data quality transformations.

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   Answer: D

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6. Question 26 · 90 in bank

   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?

   • ACreate a custom quota for each analyst in BigQuery.
   • BCreate a single reservation by using BigQuery editions. Assign all analysts to the reservation.
   • CAssign each analyst to a separate project associated with their department. Create a single reservation by using BigQuery editions. Assign all projects to the reservation.
   • DAssign 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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   Answer: D

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7. Question 27 · 90 in bank

   You manage a web application that stores data in a Cloud SQL database. You need to improve the read performance of the application by offloading read traffic from the primary database instance. You want to implement a solution that minimizes effort and cost. What should you do?

   • AUse Cloud CDN to cache frequently accessed data.
   • BStore frequently accessed data in a Memorystore instance.
   • CMigrate the database to a larger Cloud SQL instance.
   • DEnable automatic backups, and create a read replica of the Cloud SQL instance.

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   Answer: D

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8. Question 28 · 90 in bank

   Your organization plans to move their on-premises environment to Google Cloud. Your organization’s network bandwidth is less than 1 Gbps. You need to move over 500 ТВ of data to Cloud Storage securely, and only have a few days to move the data. What should you do?

   • ARequest multiple Transfer Appliances, copy the data to the appliances, and ship the appliances back to Google Cloud to upload the data to Cloud Storage.
   • BConnect to Google Cloud using VPN. Use Storage Transfer Service to move the data to Cloud Storage.
   • CConnect to Google Cloud using VPN. Use the gcloud storage command to move the data to Cloud Storage.
   • DConnect to Google Cloud using Dedicated Interconnect. Use the gcloud storage command to move the data to Cloud Storage.

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   Answer: A

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9. Question 29 · 90 in bank

   Your organization uses a BigQuery table that is partitioned by ingestion time. You need to remove data that is older than one year to reduce your organization’s storage costs. You want to use the most efficient approach while minimizing cost. What should you do?

   • ACreate a scheduled query that periodically runs an update statement in SQL that sets the “deleted" column to “yes” for data that is more than one year old. Create a view that filters out rows that have been marked deleted.
   • BCreate a view that filters out rows that are older than one year.
   • CRequire users to specify a partition filter using the alter table statement in SQL.
   • DSet the table partition expiration period to one year using the ALTER TABLE statement in SQL.

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   Answer: D

   Explanation

   The most efficient and cost-effective solution is D. Set the table partition expiration period to one year using the ALTER TABLE statement in SQL. Partition expiration is a built-in BigQuery feature specifically designed to automatically delete old partitions, directly reducing storage costs and requiring minimal effort. Options A and B (soft delete and views) do not reduce storage costs; they only filter or mark data without removing it. Option C (requiring partition filters) is about query optimization, not data removal or storage cost reduction. Therefore, Option D is the only option that directly and effectively addresses the requirement of removing old data to minimize storage costs in a BigQuery partitioned table.

   Source: Adapted from community discussion — n2183712847

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10. Question 30 · 90 in bank

    Your company is migrating their batch transformation pipelines to Google Cloud. You need to choose a solution that supports programmatic transformations using only SQL. You also want the technology to support Git integration for version control of your pipelines. What should you do?

    • AUse Cloud Data Fusion pipelines.
    • BUse Dataform workflows.
    • CUse Dataflow pipelines.
    • DUse Cloud Composer operators.

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    Answer: B

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ADP practice, question 21. 90 questions available.