ADP practice questions 41–50

1. Question 41 · 90 in bank

   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?

   • AUse 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.
   • BSchedule 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.
   • CUse 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.
   • DCreate 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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   Answer: C

   Explanation

   The best solution is C. Dataflow with Pub/Sub notifications. Dataflow offers low-overhead, real-time streaming, ideal for immediate processing of files as they land in Cloud Storage, and handles the required transformations efficiently before loading into BigQuery. Option A (Composer/Dataproc) is too heavyweight and costly for frequent, small batches. Option B (Composer Hourly Batch) is too slow, introducing delays and data staleness. Option D (Data Fusion/Cloud Run) is more complex and potentially higher overhead than a simpler Dataflow pipeline for this streaming use case.

   Source: Adapted from community discussion — n2183712847

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2. Question 42 · 90 in bank

   You work for a home insurance company. You are frequently asked to create and save risk reports with charts for specific areas using a publicly available storm event dataset. You want to be able to quickly create and re-run risk reports when new data becomes available. What should you do?

   • AExport the storm event dataset as a CSV file. Import the file to Google Sheets, and use cell data in the worksheets to create charts.
   • BCopy the storm event dataset into your BigQuery project. Use BigQuery Studio to query and visualize the data in Looker Studio.
   • CReference and query the storm event dataset using SQL in BigQuery Studio. Export the results to Google Sheets, and use cell data in the worksheets to create charts.
   • DReference and query the storm event dataset using SQL in a Colab Enterprise notebook. Display the table results and document with Markdown, and use Matplotlib to create charts.

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   Answer: B

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3. Question 43 · 90 in bank

   Your company currently uses an on-premises network file system (NFS) and is migrating data to Google Cloud. You want to be able to control how much bandwidth is used by the data migration while capturing detailed reporting on the migration status. What should you do?

   • AUse a Transfer Appliance.
   • BUse Cloud Storage FUSE.
   • CUse Storage Transfer Service.
   • DUse gcloud storage commands.

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   Answer: C

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4. Question 44 · 90 in bank

   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?

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

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   Answer: D

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5. Question 45 · 90 in bank

   You manage a Cloud Storage bucket that stores temporary files created during data processing. These temporary files are only needed for seven days, after which they are no longer needed. To reduce storage costs and keep your bucket organized, you want to automatically delete these files once they are older than seven days. What should you do?

   • ASet up a Cloud Scheduler job that invokes a weekly Cloud Run function to delete files older than seven days.
   • BConfigure a Cloud Storage lifecycle rule that automatically deletes objects older than seven days.
   • CDevelop a batch process using Dataflow that runs weekly and deletes files based on their age.
   • DCreate a Cloud Run function that runs daily and deletes files older than seven days.

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   Answer: B

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6. Question 46 · 90 in bank

   Your organization’s ecommerce website collects user activity logs using a Pub/Sub topic. Your organization’s leadership team wants a dashboard that contains aggregated user engagement metrics. You need to create a solution that transforms the user activity logs into aggregated metrics, while ensuring that the raw data can be easily queried. What should you do?

   • ACreate a Dataflow subscription to the Pub/Sub topic, and transform the activity logs. Load the transformed data into a BigQuery table for reporting.
   • BCreate an event-driven Cloud Run function to trigger a data transformation pipeline to run. Load the transformed activity logs into a BigQuery table for reporting.
   • CCreate a Cloud Storage subscription to the Pub/Sub topic. Load the activity logs into a bucket using the Avro file format. Use Dataflow to transform the data, and load it into a BigQuery table for reporting.
   • DCreate a BigQuery subscription to the Pub/Sub topic, and load the activity logs into the table. Create a materialized view in BigQuery using SQL to transform the data for reporting

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   Answer: D

   Explanation

   Option D is the clear winner. BigQuery subscriptions + materialized views are purpose-built for this. Dataflow (A) works, but is more overhead. Cloud Run (B) isn't suitable for continuous processing, and Cloud Storage (C) adds unnecessary steps. Option D is the most efficient and keeps the raw data accessible.

   Source: Adapted from community discussion — n2183712847

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7. Question 47 · 90 in bank

   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?

   • ASet up a Cloud CDN in front of the bucket.
   • BEnable Object Versioning on the bucket.
   • CStore the data in a multi-region bucket.
   • DStore the data in Nearline storage.

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   Answer: C

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8. Question 48 · 90 in bank

   Your retail company collects customer data from various sources: Online transactions: Stored in a MySQL database Customer feedback: Stored as text files on a company server Social media activity: Streamed in real-time from social media platforms You are designing a data pipeline to extract this data. Which Google Cloud storage system(s) should you select for further analysis and ML model training?

   • A1. Online transactions: Cloud Storage 2. Customer feedback: Cloud Storage 3. Social media activity: Cloud Storage
   • B1. Online transactions: BigQuery 2. Customer feedback: Cloud Storage 3. Social media activity: BigQuery
   • C1. Online transactions: Bigtable 2. Customer feedback: Cloud Storage 3. Social media activity: CloudSQL for MySQL
   • D1. Online transactions: Cloud SQL for MySQL 2. Customer feedback: BigQuery 3. Social media activity: Cloud Storage

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   Answer: B

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9. Question 49 · 90 in bank

   Your company uses Looker as its primary business intelligence platform. You want to use LookML to visualize the profit margin for each of your company’s products in your Looker Explores and dashboards. You need to implement a solution quickly and efficiently. What should you do?

   • ACreate a derived table that pre-calculates the profit margin for each product, and include it in the Looker model.
   • BDefine a new measure that calculates the profit margin by using the existing revenue and cost fields.
   • CCreate a new dimension that categorizes products based on their profit margin ranges (e.g., high, medium, low).
   • DApply a filter to only show products with a positive profit margin.

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   Answer: B

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10. Question 50 · 90 in bank

    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?

    • AEnable access control by using IAM roles.
    • BEncrypt the data by using customer-managed encryption keys (CMEK).
    • CUpdate dataset privileges by using the SQL GRANT statement.
    • DExport the data to Cloud Storage, and use signed URLs to authorize access.

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    Answer: A

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ADP practice, question 41. 90 questions available.