ASSOCIATE-DATA-PRACTITIONER EXAM TESTS, ASSOCIATE-DATA-PRACTITIONER BRAINDUMPS, ASSOCIATE-DATA-PRACTITIONER ACTUAL TEST

Associate-Data-Practitioner Exam Tests, Associate-Data-Practitioner Braindumps, Associate-Data-Practitioner Actual Test

Associate-Data-Practitioner Exam Tests, Associate-Data-Practitioner Braindumps, Associate-Data-Practitioner Actual Test

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There is no doubt that advanced technologies are playing an important role in boosting the growth of Google companies. This is the reason why the employees have now started upgrading their skillset with the Google Cloud Associate Data Practitioner (Associate-Data-Practitioner) certification exam because they want to work with those latest applications and save their jobs. They attempt the Associate-Data-Practitioner exam to validate their skills and try to get their dream job.

Google Associate-Data-Practitioner Exam Syllabus Topics:

TopicDetails
Topic 1
  • Data Preparation and Ingestion: This section of the exam measures the skills of Google Cloud Engineers and covers the preparation and processing of data. Candidates will differentiate between various data manipulation methodologies such as ETL, ELT, and ETLT. They will choose appropriate data transfer tools, assess data quality, and conduct data cleaning using tools like Cloud Data Fusion and BigQuery. A key skill measured is effectively assessing data quality before ingestion.
Topic 2
  • Data Management: This domain measures the skills of Google Database Administrators in configuring access control and governance. Candidates will establish principles of least privilege access using Identity and Access Management (IAM) and compare methods of access control for Cloud Storage. They will also configure lifecycle management rules to manage data retention effectively. A critical skill measured is ensuring proper access control to sensitive data within Google Cloud services
Topic 3
  • Data Analysis and Presentation: This domain assesses the competencies of Data Analysts in identifying data trends, patterns, and insights using BigQuery and Jupyter notebooks. Candidates will define and execute SQL queries to generate reports and analyze data for business questions.| Data Pipeline Orchestration: This section targets Data Analysts and focuses on designing and implementing simple data pipelines. Candidates will select appropriate data transformation tools based on business needs and evaluate use cases for ELT versus ETL.

Google Cloud Associate Data Practitioner Sample Questions (Q78-Q83):

NEW QUESTION # 78
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?

  • A. Create a Dataflow subscription to the Pub/Sub topic, and transform the activity logs. Load the transformed data into a BigQuery table for reporting.
  • B. Create 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
  • C. Create 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.
  • D. Create 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.

Answer: A

Explanation:
UsingDataflowto subscribe to the Pub/Sub topic and transform the activity logs is the best approach for this scenario. Dataflow is a managed service designed for processing and transforming streaming data in real time.
It allows you to aggregate metrics from the raw activity logs efficiently and load the transformed data into a BigQuery table for reporting. This solution ensures scalability, supports real-time processing, and enables querying of both raw and aggregated data in BigQuery, providing the flexibility and insights needed for the dashboard.


NEW QUESTION # 79
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?

  • A. Use the bq load command to load the Parquet files into BigQuery, and perform SQL joins to analyze the data.
  • B. Launch a Cloud Data Fusion environment, use plugins to connect to BigQuery and Cloud Storage, and use the SQL join operation to analyze the data.
  • C. Create external tables over the files in Cloud Storage, and perform SQL joins to tables in BigQuery to analyze the data.
  • D. Create a Dataproc cluster, and write a PySpark job to join the data from BigQuery to the files in Cloud Storage.

Answer: C

Explanation:
Creating external tables over the Parquet files in Cloud Storage allows you to perform SQL-based analysis and joins with data already in BigQuery without needing to load the files into BigQuery. This approach is efficient for a one-time analysis as it avoids the time and cost associated with loading large volumes of data into BigQuery. External tables provide seamless integration with Cloud Storage, enabling quick and cost-effective analysis of data stored in Parquet format.


NEW QUESTION # 80
You are storing data in Cloud Storage for a machine learning project. The data is frequently accessed during the model training phase, minimally accessed after 30 days, and unlikely to be accessed after 90 days. You need to choose the appropriate storage class for the different stages of the project to minimize cost. What should you do?

  • A. Store the data in Standard storage during the model training phase. Transition the data to Nearline storage 30 days after model deployment, and to Coldline storage 90 days after model deployment.
  • B. Store the data in Nearline storage during the model training phase. Transition the data to Archive storage 30 days after model deployment, and to Coldline storage 90 days after model deployment.
  • C. Store the data in Nearline storage during the model training phase. Transition the data to Coldline storage 30 days after model deployment, and to Archive storage 90 days after model deployment.
  • D. Store the data in Standard storage during the model training phase. Transition the data to Durable Reduced Availability (DRA) storage 30 days after model deployment, and to Coldline storage 90 days after model deployment.

Answer: A

Explanation:
Comprehensive and Detailed In-Depth Explanation:
Cost minimization requires matching storage classes to access patterns using lifecycle rules. Let's assess:
* Option A: Nearline during training (frequent access) incurs high retrieval costs and latency, unsuitable for ML workloads. Coldline after 30 days and Archive after 90 days are reasonable but misaligned initially.
* Option B: Standard storage (no retrieval fees, low latency) is ideal for frequent access during training.
Transitioning to Nearline (30-day minimum, low access) after 30 days and Coldline (90-day minimum, rare access) after 90 days matches the pattern and minimizes costs effectively.
* Option C: Nearline during training is costly for frequent access, and Archive to Coldline is illogical (Archive is cheaper than Coldline).


NEW QUESTION # 81
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. Set up a Cloud Monitoring dashboard to track key Dataflow metrics, such as data throughput, error rates, and resource utilization.
  • B. Navigate to the Dataflow Jobs page in the Google Cloud console. Use the job logs and worker logs to identify the error.
  • C. Create a custom script to periodically poll the Dataflow API for job status updates, and send email alerts if any errors are identified.
  • D. Use the gcloud CLI tool to retrieve job metrics and logs, and analyze them for errors and performance bottlenecks.

Answer: B

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 # 82
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?

  • A. Export the storm event dataset as a CSV file. Import the file to Google Sheets, and use cell data in the worksheets to create charts.
  • B. Reference 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.
  • C. Copy the storm event dataset into your BigQuery project. Use BigQuery Studio to query and visualize the data in Looker Studio.
  • D. Reference 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.

Answer: C

Explanation:
Copying the storm event dataset into your BigQuery project and using BigQuery Studio to query and visualize the data in Looker Studio is the best approach. This solution allows you to create reusable and automated workflows for generating risk reports. BigQuery handles the querying efficiently, and Looker Studio provides powerful tools for creating and sharing dynamic charts and dashboards. This setup ensures that reports can be easily re-run with updated data, minimizing manual effort and providing a scalable, interactive solution for visualizing risk reports.


NEW QUESTION # 83
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