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[General] Reliable Google Professional-Data-Engineer Exam Papers - Latest Professional-Dat

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【General】 Reliable Google Professional-Data-Engineer Exam Papers - Latest Professional-Dat

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Google Certified Professional Data Engineer Exam Sample Questions (Q138-Q143):NEW QUESTION # 138
You are implementing security best practices on your data pipeline. Currently, you are manually executing jobs as the Project Owner. You want to automate these jobs by taking nightly batch files containing non- public information from Google Cloud Storage, processing them with a Spark Scala job on a Google Cloud Dataproc cluster, and depositing the results into Google BigQuery.
How should you securely run this workload?
  • A. Use a user account with the Project Viewer role on the Cloud Dataproc cluster to read the batch files and write to BigQuery
  • B. Use a service account with the ability to read the batch files and to write to BigQuery
  • C. Restrict the Google Cloud Storage bucket so only you can see the files
  • D. Grant the Project Owner role to a service account, and run the job with it
Answer: B

NEW QUESTION # 139
You have thousands of Apache Spark jobs running in your on-premises Apache Hadoop cluster. You want to migrate the jobs to Google Cloud. You want to use managed services to run your jobs instead of maintaining a long-lived Hadoop cluster yourself. You have a tight timeline and want to keep code changes to a minimum. What should you do?
  • A. Move your data to BigQuery. Convert your Spark scripts to a SQL-based processing approach.
  • B. Move your data to Cloud Storage. Run your jobs on Dataproc.
  • C. Rewrite your jobs in Apache Beam. Run your jobs in Dataflow.
  • D. Copy your data to Compute Engine disks. Manage and run your jobs directly on those instances.
Answer: B
Explanation:
Dataproc's Compatibility with Apache Spark: Dataproc is a managed service for running Hadoop and Spark clusters on Google Cloud. This means it is designed to seamlessly run Apache Spark jobs with minimal code changes. Your existing Spark jobs should run on Dataproc with little to no modification.
Cloud Storage as a Scalable Data Lake: Cloud Storage provides a highly scalable and durable storage solution for your data. It's designed to handle large volumes of data that Spark jobs typically process.
Minimizing Operational Overhead: By using Dataproc, you eliminate the need to manage and maintain a Hadoop cluster yourself. Google Cloud handles the infrastructure, allowing you to focus on your data processing tasks.
Tight Timeline and Minimal Code Changes: This option directly addresses the requirements of the question. It offers a quick and easy way to migrate your Spark jobs to Google Cloud with minimal disruption to your existing codebase.
Why other options are not suitable:
A . Copy your data to Compute Engine disks. Manage and run your jobs directly on those instances: This option requires you to manage the underlying infrastructure yourself, which contradicts the requirement of using managed services.
C . Move your data to BigQuery. Convert your Spark scripts to a SQL-based processing approach: While BigQuery is a powerful data warehouse, converting Spark scripts to SQL would require substantial code changes and might not be feasible within a tight timeline.
D . Rewrite your jobs in Apache Beam. Run your jobs in Dataflow: Rewriting jobs in Apache Beam would be a significant undertaking and not suitable for a quick migration with minimal code changes.

NEW QUESTION # 140
You are developing an application that uses a recommendation engine on Google Cloud. Your solution should display new videos to customers based on past views. Your solution needs to generate labels for the entities in videos that the customer has viewed. Your design must be able to provide very fast filtering suggestions based on data from other customer preferences on several TB of dat
a. What should you do?
  • A. Build an application that calls the Cloud Video Intelligence API to generate labels. Store data in Cloud
    Bigtable, and filter the predicted labels to match the user's viewing history to generate preferences.
  • B. Build and train a complex classification model with Spark MLlib to generate labels and filter the results.
    Deploy the models using Cloud Dataproc. Call the model from your application.
  • C. Build an application that calls the Cloud Video Intelligence API to generate labels. Store data in Cloud
    SQL, and join and filter the predicted labels to match the user's viewing history to generate preferences.
  • D. Build and train a classification model with Spark MLlib to generate labels. Build and train a second
    classification model with Spark MLlib to filter results to match customer preferences. Deploy the models
    using Cloud Dataproc. Call the models from your application.
Answer: A

NEW QUESTION # 141
You are designing an Apache Beam pipeline to enrich data from Cloud Pub/Sub with static reference data from BigQuery. The reference data is small enough to fit in memory on a single worker. The pipeline should write enriched results to BigQuery for analysis. Which job type and transforms should this pipeline use?
  • A. Batch job, PubSubIO, side-inputs
  • B. Streaming job, PubSubIO, BigQueryIO, side-outputs
  • C. Streaming job, PubSubIO, JdbcIO, side-outputs
  • D. Streaming job, PubSubIO, BigQueryIO, side-inputs
Answer: D

NEW QUESTION # 142
You want to encrypt the customer data stored in BigQuery. You need to implement for-user crypto-deletion on data stored in your tables. You want to adopt native features in Google Cloud to avoid custom solutions. What should you do?
  • A. Create a customer-managed encryption key (CMEK) in Cloud KMS. Associate the key to the table while creating the table.
  • B. Encrypt your data during ingestion by using a cryptographic library supported by your ETL pipeline.
  • C. Implement Authenticated Encryption with Associated Data (AEAD) BigQuery functions while storing your data in BigQuery.
  • D. Create a customer-managed encryption key (CMEK) in Cloud KMS. Use the key to encrypt data before storing in BigQuery.
Answer: A
Explanation:
To implement for-user crypto-deletion and ensure that customer data stored in BigQuery is encrypted, using native Google Cloud features, the best approach is to use Customer-Managed Encryption Keys (CMEK) with Cloud Key Management Service (KMS). Here's why:
Customer-Managed Encryption Keys (CMEK):
CMEK allows you to manage your own encryption keys using Cloud KMS. These keys provide additional control over data access and encryption management.
Associating a CMEK with a BigQuery table ensures that data is encrypted with a key you manage.
For-User Crypto-Deletion:
For-user crypto-deletion can be achieved by disabling or destroying the CMEK. Once the key is disabled or destroyed, the data encrypted with that key cannot be decrypted, effectively rendering it unreadable.
Native Integration:
Using CMEK with BigQuery is a native feature, avoiding the need for custom encryption solutions. This simplifies the management and implementation of encryption and decryption processes.
Steps to Implement:
Create a CMEK in Cloud KMS:
Set up a new customer-managed encryption key in Cloud KMS.
Associate the CMEK with BigQuery Tables:
When creating a new table in BigQuery, specify the CMEK to be used for encryption.
This can be done through the BigQuery console, CLI, or API.
Reference:
BigQuery and CMEK
Cloud KMS Documentation
Encrypting Data in BigQuery

NEW QUESTION # 143
......
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