Google Associate-Data-Practitioner Practice Questions

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Google Associate-Data-Practitioner Exam Syllabus Topics:

Topic	Details
Topic 1	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.
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 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 (Q102-Q107):

NEW QUESTION # 102
You are constructing a data pipeline to process sensitive customer data stored in a Cloud Storage bucket. You need to ensure that this data remains accessible, even in the event of a single-zone outage. What should you do?

A. Store the data in Nearline storaqe.
B. Store the data in a multi-region bucket.
C. Set up a Cloud CDN in front of the bucket.
D. Enable Object Versioning on the bucket.

Answer: B

Explanation:
Storing the data in a multi-region bucket ensures high availability and durability, even in the event of a single-zone outage. Multi-region buckets replicate data across multiple locations within the selected region, providing resilience against zone-level failures and ensuring that the data remains accessible. This approach is particularly suitable for sensitive customer data that must remain available without interruptions.

NEW QUESTION # 103
You are working with a large dataset of customer reviews stored in Cloud Storage. The dataset contains several inconsistencies, such as missing values, incorrect data types, and duplicate entries. You need to clean the data to ensure that it is accurate and consistent before using it for analysis. What should you do?

A. Use Storage Transfer Service to move the data to a different Cloud Storage bucket. Use event triggers to invoke Cloud Run functions to load the data into BigQuery. Use SQL for analysis.
B. Use Cloud Run functions to clean the data and load it into BigQuery. Use SQL for analysis.
C. Use BigQuery to batch load the data into BigQuery. Use SQL for cleaning and analysis.
D. Use the PythonOperator in Cloud Composer to clean the data and load it into BigQuery. Use SQL for analysis.

Answer: C

Explanation:
Using BigQuery to batch load the data and perform cleaning and analysis with SQL is the best approach for this scenario. BigQuery provides powerful SQL capabilities to handle missing values, enforce correct data types, and remove duplicates efficiently. This method simplifies the pipeline by leveraging BigQuery's built-in processing power for both cleaning and analysis, reducing the need for additional tools or services and minimizing complexity.

NEW QUESTION # 104
You recently inherited a task for managing Dataflow streaming pipelines in your organization and noticed that proper access had not been provisioned to you. You need to request a Google-provided IAM role so you can restart the pipelines. You need to follow the principle of least privilege. What should you do?

A. Request the Dataflow Admin role.
B. Request the Dataflow Developer role.
C. Request the Dataflow Viewer role.
D. Request the Dataflow Worker role.

Answer: B

Explanation:
The Dataflow Developer role provides the necessary permissions to manage Dataflow streaming pipelines, including the ability to restart pipelines. This role adheres to the principle of least privilege, as it grants only the permissions required to manage and operate Dataflow jobs without unnecessary administrative access. Other roles, such as Dataflow Admin, would grant broader permissions, which are not needed in this scenario.

NEW QUESTION # 105
Your company wants to implement a data transformation (ETL) pipeline for their BigQuery data warehouse.
You need to identify a managed transformation solution that allows users to develop with SQL and JavaScript, has version control, allows for modular code, and has data quality checks. What should you do?

A. Create a Cloud Composer environment, and orchestrate the transformations by using the BigQueryinsertJob operator.
B. Create BigQuery scheduled queries to define the transformations in SQL.
C. Use Dataproc to create an Apache Spark cluster and implement the transformations by using PySpark SQL.
D. Use Dataform to define the transformations in SQLX.

Answer: D

Explanation:
Comprehensive and Detailed in Depth Explanation:
Why C is correct:Dataform is a managed data transformation service that allows you to define data pipelines using SQL and JavaScript.
It provides version control, modular code development, and data quality checks.
Why other options are incorrect:A: Cloud Composer is an orchestration tool, not a data transformation tool.
B: Scheduled queries are not suitable for complex ETL pipelines.
D: Dataproc requires setting up a Spark cluster and writing code, which is more complex than using Dataform.

NEW QUESTION # 106
You need to create a weekly aggregated sales report based on a large volume of dat a. You want to use Python to design an efficient process for generating this report. What should you do?

A. Create a Colab Enterprise notebook and use the bigframes.pandas library. Schedule the notebook to execute once a week.
B. Create a Cloud Run function that uses NumPy. Use Cloud Scheduler to schedule the function to run once a week.
C. Create a Dataflow directed acyclic graph (DAG) coded in Python. Use Cloud Scheduler to schedule the code to run once a week.
D. Create a Cloud Data Fusion and Wrangler flow. Schedule the flow to run once a week.

Answer: C

Explanation:
Using Dataflow with a Python-coded Directed Acyclic Graph (DAG) is the most efficient solution for generating a weekly aggregated sales report based on a large volume of data. Dataflow is optimized for large-scale data processing and can handle aggregation efficiently. Python allows you to customize the pipeline logic, and Cloud Scheduler enables you to automate the process to run weekly. This approach ensures scalability, efficiency, and the ability to process large datasets in a cost-effective manner.

NEW QUESTION # 107
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Google Associate-Data-Practitioner Practice Questions

Google Associate-Data-Practitioner Exam Syllabus Topics:

Latest Google Associate-Data-Practitioner Exam Questions in Three Different Formats

Google Cloud Associate Data Practitioner Sample Questions (Q102-Q107):

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