ADP practice questions 11–20

1. Question 11 · 90 in bank

   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?

   • AUse Google-managed encryption keys (GMEK).
   • BUse customer-managed encryption keys (CMEK).
   • CUse customer-supplied encryption keys (CSEK).
   • DUse customer-supplied encryption keys (CSEK) for the sensitive data and customer-managed encryption keys (CMEK) for the less sensitive data.

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   Answer: A

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2. Question 12 · 90 in bank

   Your company has several retail locations. Your company tracks the total number of sales made at each location each day. You want to use SQL to calculate the weekly moving average of sales by location to identify trends for each store. Which query should you use?

   • A-------------------------
   • B-------------------------
   • C-------------------------
   • D-------------------------

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   Answer: C

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3. Question 13 · 90 in bank

   You work for a financial organization that stores transaction data in BigQuery. Your organization has a regulatory requirement to retain data for a minimum of seven years for auditing purposes. You need to ensure that the data is retained for seven years using an efficient and cost-optimized approach. What should you do?

   • ACreate a partition by transaction date, and set the partition expiration policy to seven years.
   • BSet the table-level retention policy in BigQuery to seven years.
   • CSet the dataset-level retention policy in BigQuery to seven years.
   • DExport the BigQuery tables to Cloud Storage daily, and enforce a lifecycle management policy that has a seven-year retention rule.

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   Answer: B

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4. Question 14 · 90 in bank

   You need to create a weekly aggregated sales report based on a large volume of data. You want to use Python to design an efficient process for generating this report. What should you do?

   • ACreate a Cloud Run function that uses NumPy. Use Cloud Scheduler to schedule the function to run once a week.
   • BCreate a Colab Enterprise notebook and use the bigframes.pandas library. Schedule the notebook to execute once a week.
   • CCreate a Cloud Data Fusion and Wrangler flow. Schedule the flow to run once a week.
   • DCreate a Dataflow directed acyclic graph (DAG) coded in Python. Use Cloud Scheduler to schedule the code to run once a week.

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   Answer: D

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5. Question 15 · 90 in bank

   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?

   • AMigrate the Spark jobs to Dataproc Serverless.
   • BConfigure a Google Kubernetes Engine cluster with Spark operators, and deploy the Spark jobs.
   • CMigrate the Spark jobs to Dataproc on Google Kubernetes Engine.
   • DMigrate the Spark jobs to Dataproc on Compute Engine.

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   Answer: A

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6. Question 16 · 90 in bank

   You are developing a data ingestion pipeline to load small CSV files into BigQuery from Cloud Storage. You want to load these files upon arrival to minimize data latency. You want to accomplish this with minimal cost and maintenance. What should you do?

   • AUse the bq command-line tool within a Cloud Shell instance to load the data into BigQuery.
   • BCreate a Cloud Composer pipeline to load new files from Cloud Storage to BigQuery and schedule it to run every 10 minutes.
   • CCreate a Cloud Run function to load the data into BigQuery that is triggered when data arrives in Cloud Storage.
   • DCreate a Dataproc cluster to pull CSV files from Cloud Storage, process them using Spark, and write the results to BigQuery.

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   Answer: C

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7. Question 17 · 90 in bank

   Your organization has a petabyte of application logs stored as Parquet files in Cloud Storage. You need to quickly perform a one-time SQL-based analysis of the files and join them to data that already resides in BigQuery. What should you do?

   • ACreate a Dataproc cluster, and write a PySpark job to join the data from BigQuery to the files in Cloud Storage.
   • BLaunch a Cloud Data Fusion environment, use plugins to connect to BigQuery and Cloud Storage, and use the SQL join operation to analyze the data.
   • CCreate external tables over the files in Cloud Storage, and perform SQL joins to tables in BigQuery to analyze the data.
   • DUse the bq load command to load the Parquet files into BigQuery, and perform SQL joins to analyze the data.

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   Answer: C

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8. Question 18 · 90 in bank

   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?

   • AUse Cloud Scheduler to schedule the jobs to run.
   • BUse Cloud Tasks to schedule and run the jobs asynchronously.
   • CCreate directed acyclic graphs (DAGs) in Cloud Composer. Use the appropriate operators to connect to Cloud Storage, Spark, and BigQuery.
   • DCreate 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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   Answer: C

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9. Question 19 · 90 in bank

   You are responsible for managing Cloud Storage buckets for a research company. Your company has well-defined data tiering and retention rules. You need to optimize storage costs while achieving your data retention needs. What should you do?

   • AConfigure the buckets to use the Archive storage class.
   • BConfigure a lifecycle management policy on each bucket to downgrade the storage class and remove objects based on age.
   • CConfigure the buckets to use the Standard storage class and enable Object Versioning.
   • DConfigure the buckets to use the Autoclass feature.

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   Answer: B

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10. Question 20 · 90 in bank

    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?

    • AWrite and execute a Python script using the BigQuery Storage Write API library.
    • BCreate a Dataproc cluster, copy the files to Cloud Storage, and write an Apache Spark job using the spark-bigquery-connector.
    • CExecute the bq load command on your local machine.
    • DCreate a Dataflow job using the Apache Beam FileIO and BigQueryIO connectors with a local runner.

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    Answer: C

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ADP practice, question 11. 90 questions available.