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Associate-Data-Practitioner Exam Success Reliable IT Certifications | Associate-Data-Practitioner: Google Cloud Associate Data Practitioner
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| Topic |
Details |
| 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.
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| Topic 2 |
- 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.
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| Topic 3 |
- 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
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Google Cloud Associate Data Practitioner Sample Questions (Q61-Q66):
NEW QUESTION # 61
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. 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. Set up a Cloud Monitoring dashboard to track key Dataflow metrics, such as data throughput, error rates, and resource utilization.
Answer: B
Explanation:
To troubleshoot a failed Dataflow job as quickly as possible, you should navigate to theDataflow 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.
Extract from Google Documentation: From "Monitoring Dataflow Jobs" (https://cloud.google.com/dataflow
/docs/guides/monitoring-jobs):"To troubleshoot a failed Dataflow job quickly, go to the Dataflow Jobs page in the Google Cloud Console, where you can view job logs and worker logs to identify errors and their root causes."
NEW QUESTION # 62
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?
- A. Create a row-level access policy.
- B. Create a data masking rule.
- C. Grant the appropriate 1AM permissions on the dataset.
- D. Add a policy tag in BigQuery.
Answer: A
Explanation:
Creating a row-level access policy in BigQuery ensures that each sales representative can see only the transactions relevant to their region. Row-level access policies allow you to define fine-grained access control by filtering rows based on specific conditions, such as matching the sales representative's region. This approach enforces security while providing tailored data access, aligning with the principle of least privilege.
NEW QUESTION # 63
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?
- A. Create a new dimension that categorizes products based on their profit margin ranges (e.g., high, medium, low).
- B. Define a new measure that calculates the profit margin by using the existing revenue and cost fields.
- C. Create a derived table that pre-calculates the profit margin for each product, and include it in the Looker model.
- D. Apply a filter to only show products with a positive profit margin.
Answer: B
Explanation:
Comprehensive and Detailed in Depth Explanation:
Why B is correct:Defining a new measure in LookML is the most efficient and direct way to calculate and visualize aggregated metrics like profit margin.
Measures are designed for calculations based on existing fields.
Why other options are incorrect:A: Filtering doesn't calculate or visualize the profit margin itself.
C: Dimensions are for categorizing data, not calculating aggregated metrics.
D: Derived tables are more complex and unnecessary for a simple calculation like profit margin, which can be done using a measure.
NEW QUESTION # 64
Your organization has decided to migrate their existing enterprise data warehouse to BigQuery. The existing data pipeline tools already support connectors to BigQuery. You need to identify a data migration approach that optimizes migration speed. What should you do?
- A. Use the existing data pipeline tool's BigQuery connector to reconfigure the data mapping.
- B. Use the Cloud Data Fusion web interface to build data pipelines. Create a directed acyclic graph (DAG) that facilitates pipeline orchestration.
- C. Use the BigQuery Data Transfer Service to recreate the data pipeline and migrate the data into BigQuery.
- D. Create a temporary file system to facilitate data transfer from the existing environment to Cloud Storage. Use Storage Transfer Service to migrate the data into BigQuery.
Answer: A
Explanation:
Since your existing data pipeline tools already support connectors to BigQuery, the most efficient approach is touse the existing data pipeline tool's BigQuery connectorto reconfigure the data mapping. This leverages your current tools, reducing migration complexity and setup time, while optimizing migration speed. By reconfiguring the data mapping within the existing pipeline, you can seamlessly direct the data into BigQuery without needing additional services or intermediary steps.
NEW QUESTION # 65
You work for a healthcare company. You have a daily ETL pipeline that extracts patient data from a legacy system, transforms it, and loads it into BigQuery for analysis. The pipeline currently runs manually using a shell script. You want to automate this process and add monitoring to ensure pipeline observability and troubleshooting insights. You want one centralized solution, using open-source tooling, without rewriting the ETL code. What should you do?
- A. Create a direct acyclic graph (DAG) in Cloud Composer to orchestrate a pipeline trigger daily. Monitor the pipeline's execution using the Apache Airflow web interface and Cloud Monitoring.
- B. Configure Cloud Dataflow to implement the ETL pipeline, and use Cloud Scheduler to trigger the Dataflow pipeline daily. Monitor the pipelines execution using the Dataflow job monitoring interface and Cloud Monitoring.
- C. Create a Cloud Run function that runs the pipeline daily. Monitor the functions execution using Cloud Monitoring.
- D. Use Cloud Scheduler to trigger a Dataproc job to execute the pipeline daily. Monitor the job's progress using the Dataproc job web interface and Cloud Monitoring.
Answer: A
Explanation:
Comprehensive and Detailed in Depth Explanation:
Why A is correct:Cloud Composer is a managed Apache Airflow service, which is a popular open-source workflow orchestration tool.
DAGs in Airflow can be used to automate ETL pipelines.
Airflow's web interface and Cloud Monitoring provide comprehensive monitoring capabilities.
It also allows you to run existing shell scripts.
Why other options are incorrect:B: Dataflow requires rewriting the ETL pipeline using its SDK.
C: Dataproc is for big data processing, not orchestration.
D: Cloud Run functions are for stateless applications, not long-running ETL pipelines.
NEW QUESTION # 66
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