ADP practice questions 51–60

1. Question 51 · 90 in bank

   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?

   • AUse AEAD functions and delete keys when employees leave the organization.
   • BUse dynamic data masking and revoke viewer permissions when employees leave the organization.
   • CUse customer-managed encryption keys (CMEK) and delete keys when employees leave the organization.
   • DUse column-level access controls with policy tags and revoke viewer permissions when employees leave the organization.

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   Answer: A

   Explanation

   With AEAD, we can target specific fields and ensure that only sensitive data is affected when an employee leaves, which is more efficient than locking down the entire dataset using CMEK.

   Source: Adapted from community discussion — SaquibHerman

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2. Question 52 · 90 in bank

   You used BigQuery ML to build a customer purchase propensity model six months ago. You want to compare the current serving data with the historical serving data to determine whether you need to retrain the model. What should you do?

   • ACompare the two different models.
   • BEvaluate the data skewness.
   • CEvaluate data drift.
   • DCompare the confusion matrix.

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   Answer: C

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3. Question 53 · 90 in bank

   Your company uses Looker to visualize and analyze sales data. You need to create a dashboard that displays sales metrics, such as sales by region, product category, and time period. Each metric relies on its own set of attributes distributed across several tables. You need to provide users the ability to filter the data by specific sales representatives and view individual transactions. You want to follow the Google-recommended approach. What should you do?

   • ACreate multiple Explores, each focusing on each sales metric. Link the Explores together in a dashboard using drill-down functionality.
   • BUse BigQuery to create multiple materialized views, each focusing on a specific sales metric. Build the dashboard using these views.
   • CCreate a single Explore with all sales metrics. Build the dashboard using this Explore.
   • DUse Looker's custom visualization capabilities to create a single visualization that displays all the sales metrics with filtering and drill-down functionality.

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   Answer: C

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4. Question 54 · 90 in bank

   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?

   • ALoad the data into BigQuery using Dataproc. Use Apache Spark to translate the reviews by invoking the Cloud Translation API. Set BigQuery as the sink.
   • BUse a Dataflow templates pipeline to translate the reviews using the Cloud Translation API. Set BigQuery as the sink.
   • CLoad 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.
   • DLoad the data into BigQuery using a Cloud Run function. Create a BigQuery remote function that invokes the Cloud Translation API. Use a scheduled query to translate new reviews.

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   Answer: B

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5. Question 55 · 90 in bank

   You have a Dataproc cluster that performs batch processing on data stored in Cloud Storage. You need to schedule a daily Spark job to generate a report that will be emailed to stakeholders. You need a fully-managed solution that is easy to implement and minimizes complexity. What should you do?

   • AUse Cloud Composer to orchestrate the Spark job and email the report.
   • BUse Dataproc workflow templates to define and schedule the Spark job, and to email the report.
   • CUse Cloud Run functions to trigger the Spark job and email the report.
   • DUse Cloud Scheduler to trigger the Spark job, and use Cloud Run functions to email the report.

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   Answer: A

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6. Question 56 · 90 in bank

   You work for a healthcare company that has a large on-premises data system containing patient records with personally identifiable information (PII) such as names, addresses, and medical diagnoses. You need a standardized managed solution that de-identifies PII across all your data feeds prior to ingestion to Google Cloud. What should you do?

   • AUse Cloud Run functions to create a serverless data cleaning pipeline. Store the cleaned data in BigQuery.
   • BUse Cloud Data Fusion to transform the data. Store the cleaned data in BigQuery.
   • CLoad the data into BigQuery, and inspect the data by using SQL queries. Use Dataflow to transform the data and remove any errors.
   • DUse Apache Beam to read the data and perform the necessary cleaning and transformation operations. Store the cleaned data in BigQuery.

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   Answer: B

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7. Question 57 · 90 in bank

   Your organization has highly sensitive data that gets updated once a day and is stored across multiple datasets in BigQuery. You need to provide a new data analyst access to query specific data in BigQuery while preventing access to sensitive data. What should you do?

   • AGrant the data analyst the BigQuery Job User IAM role in the Google Cloud project.
   • BCreate a materialized view with the limited data in a new dataset. Grant the data analyst BigQuery Data Viewer IAM role in the dataset and the BigQuery Job User IAM role in the Google Cloud project.
   • CCreate a new Google Cloud project, and copy the limited data into a BigQuery table. Grant the data analyst the BigQuery Data Owner IAM role in the new Google Cloud project.
   • DGrant the data analyst the BigQuery Data Viewer IAM role in the Google Cloud project.

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   Answer: B

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8. Question 58 · 90 in bank

   You are a database administrator managing sales transaction data by region stored in a BigQuery table. You need to ensure that each sales representative can only see the transactions in their region. What should you do?

   • AAdd a policy tag in BigQuery.
   • BCreate a row-level access policy.
   • CCreate a data masking rule.
   • DGrant the appropriate IAM permissions on the dataset.

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   Answer: B

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9. Question 59 · 90 in bank

   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?

   • ACreate an external table.
   • BCreate a temporary table.
   • CCreate a native table.
   • DCreate an object table.

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   Answer: D

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10. Question 60 · 90 in bank

    You work for a financial services company that handles highly sensitive data. Due to regulatory requirements, your company is required to have complete and manual control of data encryption. Which type of keys should you recommend to use for data storage?

    • AUse customer-supplied encryption keys (CSEK).
    • BUse a dedicated third-party key management system (KMS) chosen by the company.
    • CUse Google-managed encryption keys (GMEK).
    • DUse customer-managed encryption keys (CMEK).

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    Answer: A

    Explanation

    The best option is A. Use customer-supplied encryption keys (CSEK). Option A is best because CSEK provides complete manual control over keys, meeting strict regulatory needs. Option B (Third-party KMS) is incorrect because CSEK already provides control within Google Cloud. Option C (GMEK) is incorrect because Google manages keys, not the company. Option D (CMEK) is incorrect because keys are managed within Google KMS, not fully manual control. Therefore, Option A, CSEK, offers the most direct and complete manual key control.

    Source: Adapted from community discussion — n2183712847

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ADP practice, question 51. 90 questions available.