ADP practice questions 31–40

1. Question 31 · 90 in bank

   You manage a BigQuery table that is used for critical end-of-month reports. The table is updated weekly with new sales data. You want to prevent data loss and reporting issues if the table is accidentally deleted. What should you do?

   • AConfigure the time travel duration on the table to be exactly seven days. On deletion, re-create the deleted table solely from the time travel data.
   • BSchedule the creation of a new snapshot of the table once a week. On deletion, re-create the deleted table using the snapshot and time travel data.
   • CCreate a clone of the table. On deletion, re-create the deleted table by copying the content of the clone.
   • DCreate a view of the table. On deletion, re-create the deleted table from the view and time travel data.

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   Answer: B

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2. Question 32 · 90 in bank

   Your organization sends IoT event data to a Pub/Sub topic. Subscriber applications read and perform transformations on the messages before storing them in the data warehouse. During particularly busy times when more data is being written to the topic, you notice that the subscriber applications are not acknowledging messages within the deadline. You need to modify your pipeline to handle these activity spikes and continue to process the messages. What should you do?

   • ARetry messages until they are acknowledged.
   • BImplement flow control on the subscribers.
   • CForward unacknowledged messages to a dead-letter topic.
   • DSeek back to the last acknowledged message.

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   Answer: B

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3. Question 33 · 90 in bank

   You have millions of customer feedback records stored in BigQuery. You want to summarize the data by using the large language model (LLM) Gemini. You need to plan and execute this analysis using the most efficient approach. What should you do?

   • AQuery the BigQuery table from within a Python notebook, use the Gemini API to summarize the data within the notebook, and store the summaries in BigQuery.
   • BUse a BigQuery ML model to pre-process the text data, export the results to Cloud Storage, and use the Gemini API to summarize the pre- processed data.
   • CCreate a BigQuery Cloud resource connection to a remote model in Vertex Al, and use Gemini to summarize the data.
   • DExport the raw BigQuery data to a CSV file, upload it to Cloud Storage, and use the Gemini API to summarize the data.

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   Answer: C

   Explanation

   Querying BigQuery from a Python notebook and using the Gemini API manually introduces unnecessary complexity and latency. It also requires additional infrastructure (like a notebook environment) and manual intervention. The most efficient approach should minimize data movement, leverage Google Cloud's native integrations, and avoid unnecessary steps like exporting data to external files or manually processing it.

   Source: Adapted from community discussion — SaquibHerman

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4. Question 34 · 90 in bank

   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?

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

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   Answer: A

   Explanation

   The best option is A. Cloud Data Fusion pipeline (Cloud Storage to BigQuery). Option A is best because Cloud Data Fusion is visual and fast for pipeline building, scalable, handles transformations visually, and provides data quality insights within the pipeline. Option B (BigQuery load + SQL) is incorrect because scheduled queries are less of a pipeline and offer fewer built-in data quality features. Option C (BigQuery load + Data Fusion BQ to BQ) is incorrect because it's inefficient and redundant to load to BigQuery before Data Fusion. Option D (Dataflow template) is incorrect because while scalable, Data Fusion is often quicker to build visually for simpler pipelines. Therefore, Option A, Cloud Data Fusion, is the best balance of speed, scalability, and data quality for this task.

   Source: Adapted from community discussion — n2183712847

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5. Question 35 · 90 in bank

   You are working on a data pipeline that will validate and clean incoming data before loading it into BigQuery for real-time analysis. You want to ensure that the data validation and cleaning is performed efficiently and can handle high volumes of data. What should you do?

   • AWrite custom scripts in Python to validate and clean the data outside of Google Cloud. Load the cleaned data into BigQuery.
   • BUse Cloud Run functions to trigger data validation and cleaning routines when new data arrives in Cloud Storage.
   • CUse Dataflow to create a streaming pipeline that includes validation and transformation steps.
   • DLoad the raw data into BigQuery using Cloud Storage as a staging area, and use SQL queries in BigQuery to validate and clean the data.

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   Answer: C

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6. Question 36 · 90 in bank

   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?

   • AUse a Google-provided Dataflow template to process the Pub/Sub messages, perform transformations, and write the results to BigQuery.
   • BCreate 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.
   • CLoad 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.
   • DUse Cloud Run functions to process the Pub/Sub messages, perform transformations, and write the results to BigQuery.

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   Answer: A

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7. Question 37 · 90 in bank

   Your organization needs to store historical customer order data. The data will only be accessed once a month for analysis and must be readily available within a few seconds when it is accessed. You need to choose a storage class that minimizes storage costs while ensuring that the data can be retrieved quickly. What should you do?

   • AStore the data in Cloud Storage using Nearline storage.
   • BStore the data in Cloud Storage using Coldline storage.
   • CStore the data in Cloud Storage using Standard storage.
   • DStore the data in Cloud Storage using Archive storage.

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   Answer: A

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8. Question 38 · 90 in bank

   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?

   • AUse 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.
   • BUse Cloud Logging to create a chart displaying the pipeline’s error logs. Use Metrics Explorer to validate the findings from the chart.
   • CUse 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.
   • DUse 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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   Answer: C

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9. Question 39 · 90 in bank

   Your organization’s business analysts require near real-time access to streaming data. However, they are reporting that their dashboard queries are loading slowly. After investigating BigQuery query performance, you discover the slow dashboard queries perform several joins and aggregations. You need to improve the dashboard loading time and ensure that the dashboard data is as up-to-date as possible. What should you do?

   • ADisable BigQuery query result caching.
   • BModify the schema to use parameterized data types.
   • CCreate a scheduled query to calculate and store intermediate results.
   • DCreate materialized views.

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   Answer: D

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10. Question 40 · 90 in bank

    You need to create a data pipeline that streams event information from applications in multiple Google Cloud regions into BigQuery for near real-time analysis. The data requires transformation before loading. You want to create the pipeline using a visual interface. What should you do?

    • APush event information to a Pub/Sub topic. Create a Dataflow job using the Dataflow job builder.
    • BPush event information to a Pub/Sub topic. Create a Cloud Run function to subscribe to the Pub/Sub topic, apply transformations, and insert the data into BigQuery.
    • CPush event information to a Pub/Sub topic. Create a BigQuery subscription in Pub/Sub.
    • DPush event information to Cloud Storage, and create an external table in BigQuery. Create a BigQuery scheduled job that executes once each day to apply transformations.

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    Answer: A

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ADP practice, question 31. 90 questions available.