VALID ASSOCIATE-DATA-PRACTITIONER EXAM DURATION, TEST ASSOCIATE-DATA-PRACTITIONER SIMULATOR FREE

Valid Associate-Data-Practitioner Exam Duration, Test Associate-Data-Practitioner Simulator Free

Valid Associate-Data-Practitioner Exam Duration, Test Associate-Data-Practitioner Simulator Free

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Google Cloud Associate Data Practitioner Sample Questions (Q67-Q72):

NEW QUESTION # 67
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?

  • A. Use Cloud Run functions to trigger the Spark job and email the report.
  • B. Use Cloud Scheduler to trigger the Spark job. and use Cloud Run functions to email the report.
  • C. Use Dataproc workflow templates to define and schedule the Spark job, and to email the report.
  • D. Use Cloud Composer to orchestrate the Spark job and email the report.

Answer: C

Explanation:
Using Dataproc workflow templates is a fully-managed and straightforward solution for defining and scheduling your Spark job on a Dataproc cluster. Workflow templates allow you to automate the execution of Spark jobs with predefined steps, including data processing and report generation. You can integrate email notifications by adding a step to the workflow that sends the report using tools like a Cloud Function or external email service. This approach minimizes complexity while leveraging Dataproc's managed capabilities for batch processing.


NEW QUESTION # 68
Your organization uses Dataflow pipelines to process real-time financial transactions. You discover that one of your Dataflow jobs has failed. You need to troubleshoot the issue as quickly as possible. What should you do?

  • A. Use the gcloud CLI tool to retrieve job metrics and logs, and analyze them for errors and performance bottlenecks.
  • B. Create a custom script to periodically poll the Dataflow API for job status updates, and send email alerts if any errors are identified.
  • C. Navigate to the Dataflow Jobs page in the Google Cloud console. Use the job logs and worker logs to identify the error.
  • D. Set up a Cloud Monitoring dashboard to track key Dataflow metrics, such as data throughput, error rates, and resource utilization.

Answer: C

Explanation:
To troubleshoot a failed Dataflow job as quickly as possible, you should navigate to the Dataflow Jobs page in the Google Cloud console. The console provides access to detailed job logs and worker logs, which can help you identify the cause of the failure. The graphical interface also allows you to visualize pipeline stages, monitor performance metrics, and pinpoint where the error occurred, making it the most efficient way to diagnose and resolve the issue promptly.


NEW QUESTION # 69
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?

  • A. Use 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.
  • B. Use 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.
  • C. Use Cloud Logging to create a chart displaying the pipeline's error logs. Use Metrics Explorer to validate the findings from the chart.
  • D. Use 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.

Answer: D

Explanation:
To troubleshoot intermittent failures in a Dataflow pipeline, you should use Cloud Logging to view detailed error messages in the pipeline's logs. These logs provide insights into the specific issues causing failures, such as data format errors or resource limitations. Additionally, you should use Cloud Monitoring to analyze the pipeline's metrics, such as CPU utilization, memory usage, and throughput, to identify performance bottlenecks or resource constraints that may contribute to the failures. This approach provides a comprehensive view of the pipeline's health and helps pinpoint the root cause of the intermittent issues.


NEW QUESTION # 70
Your retail company collects customer data from various sources:
You are designing a data pipeline to extract this dat
a. Which Google Cloud storage system(s) should you select for further analysis and ML model training?

  • A. 1. Online transactions: Cloud Storage
    2. Customer feedback: Cloud Storage
    3. Social media activity: Cloud Storage
  • B. 1. Online transactions: BigQuery
    2. Customer feedback: Cloud Storage
    3. Social media activity: BigQuery
  • C. 1. Online transactions: Cloud SQL for MySQL
    2. Customer feedback: BigQuery
    3. Social media activity: Cloud Storage
  • D. 1. Online transactions: Bigtable
    2. Customer feedback: Cloud Storage
    3. Social media activity: CloudSQL for MySQL

Answer: B

Explanation:
Online transactions: Storing the transactional data in BigQuery is ideal because BigQuery is a serverless data warehouse optimized for querying and analyzing structured data at scale. It supports SQL queries and is suitable for structured transactional data.
Customer feedback: Storing customer feedback in Cloud Storage is appropriate as it allows you to store unstructured text files reliably and at a low cost. Cloud Storage also integrates well with data processing and ML tools for further analysis.
Social media activity: Storing real-time social media activity in BigQuery is optimal because BigQuery supports streaming inserts, enabling real-time ingestion and analysis of data. This allows immediate analysis and integration into dashboards or ML pipelines.


NEW QUESTION # 71
You have millions of customer feedback records stored in BigQuery. You want to summarize the data by using the large language model (LLM) copyright. You need to plan and execute this analysis using the most efficient approach. What should you do?

  • A. Query the BigQuery table from within a Python notebook, use the copyright API to summarize the data within the notebook, and store the summaries in BigQuery.
  • B. Create a BigQuery Cloud resource connection to a remote model in Vertex Al, and use copyright to summarize the data.
  • C. Export the raw BigQuery data to a CSV file, upload it to Cloud Storage, and use the copyright API to summarize the data.
  • D. Use a BigQuery ML model to pre-process the text data, export the results to Cloud Storage, and use the copyright API to summarize the pre- processed data.

Answer: B

Explanation:
Creating a BigQuery Cloud resource connection to a remote model in Vertex AI and using copyright to summarize the data is the most efficient approach. This method allows you to seamlessly integrate BigQuery with the copyright model via Vertex AI, avoiding the need to export data or perform manual steps. It ensures scalability for large datasets and minimizes data movement, leveraging Google Cloud's ecosystem for efficient data summarization and storage.


NEW QUESTION # 72
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Additionally, the web-based Google Cloud Associate Data Practitioner (Associate-Data-Practitioner) practice test works on all operating systems such as Windows, iOS, Android, and Linux, providing flexibility to users. Browsers including MS Edge, Internet Explorer, Safari, Opera, Chrome, and Firefox also support the online version of the Google Cloud Associate Data Practitioner (Associate-Data-Practitioner) practice exam. Features we have discussed in the above section of the CramPDF Google Cloud Associate Data Practitioner (Associate-Data-Practitioner) practice test software are present in the online format as well. But the web-based version of the Associate-Data-Practitioner practice exam requires a continuous internet connection.

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