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Google Associate-Data-Practitioner Exam Syllabus Topics:

Topic
Details

Topic 1

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 2

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 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 (Q11-Q16):

NEW QUESTION # 11
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 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.
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 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.

Answer: A

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 # 12
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. Navigate to the Dataflow Jobs page in the Google Cloud console. Use the job logs and worker logs to identify the error.
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. Set up a Cloud Monitoring dashboard to track key Dataflow metrics, such as data throughput, error rates, and resource utilization.
D. Use the gcloud CLI tool to retrieve job metrics and logs, and analyze them for errors and performance bottlenecks.

Answer: A

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 # 13
You need to design a data pipeline that ingests data from CSV, Avro, and Parquet files into Cloud Storage. The data includes raw user input. You need to remove all malicious SQL injections before storing the data in BigQuery. Which data manipulation methodology should you choose?

A. ETL
B. ETLT
C. ELT
D. EL

Answer: A

NEW QUESTION # 14
You are a data analyst at your organization. You have been given a BigQuery dataset that includes customer information. The dataset contains inconsistencies and errors, such as missing values, duplicates, and formatting issues. You need to effectively and quickly clean the dat a. What should you do?

A. Export the data from BigQuery to CSV files. Resolve the errors using a spreadsheet editor, and re-import the cleaned data into BigQuery.
B. Use BigQuery's built-in functions to perform data quality transformations.
C. Use Cloud Data Fusion to create a data pipeline to read the data from BigQuery, perform data quality transformations, and write the clean data back to BigQuery.
D. Develop a Dataflow pipeline to read the data from BigQuery, perform data quality rules and transformations, and write the cleaned data back to BigQuery.

Answer: B

Explanation:
Using BigQuery's built-in functions is the most effective and efficient way to clean the dataset directly within BigQuery. BigQuery provides powerful SQL capabilities to handle missing values, remove duplicates, and resolve formatting issues without needing to export data or create complex pipelines. This approach minimizes overhead and leverages the scalability of BigQuery for large datasets, making it an ideal solution for quickly addressing data quality issues.

NEW QUESTION # 15
Your team needs to analyze large datasets stored in BigQuery to identify trends in user behavior. The analysis will involve complex statistical calculations, Python packages, and visualizations. You need to recommend a managed collaborative environment to develop and share the analysis. What should you recommend?

A. Create a Looker Studio dashboard and connect the dashboard to BigQuery. Share the dashboard with your team. Analyze the data and generate visualizations in Looker Studio.
B. Connect Google Sheets to BigQuery by using Connected Sheets. Share the Google Sheet with your team. Analyze the data and generate visualizations in Gooqle Sheets.
C. Create a Colab Enterprise notebook and connect the notebook to BigQuery. Share the notebook with your team. Analyze the data and generate visualizations in Colab Enterprise.
D. Create a statistical model by using BigQuery ML. Share the query with your team. Analyze the data and generate visualizations in Looker Studio.

Answer: C

Explanation:
Using a Colab Enterprise notebook connected to BigQuery provides a managed, collaborative environment ideal for complex statistical calculations, Python packages, and visualizations. Colab Enterprise supports Python libraries for advanced analytics and offers seamless integration with BigQuery for querying large datasets. It allows teams to collaboratively develop and share analyses while taking advantage of its visualization capabilities. This approach is particularly suitable for tasks involving sophisticated computations and custom visualizations.

NEW QUESTION # 16
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