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Associate-Data-Practitioner Valid Exam Answers, Associate-Data-Practitioner Valid Test Papers

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Google Associate-Data-Practitioner Exam Syllabus Topics:

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.

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 (Q87-Q92):

NEW QUESTION # 87
Your retail organization stores sensitive application usage data in Cloud Storage. You need to encrypt the data without the operational overhead of managing encryption keys. What should you do?

A. Use customer-managed encryption keys (CMEK).
B. Use customer-supplied encryption keys (CSEK).
C. Use customer-supplied encryption keys (CSEK) for the sensitive data and customer-managed encryption keys (CMEK) for the less sensitive data.
D. Use Google-managed encryption keys (GMEK).

Answer: D

Explanation:
Using Google-managed encryption keys (GMEK) is the best choice when you want to encrypt sensitive data in Cloud Storage without the operational overhead of managing encryption keys. GMEK is the default encryption mechanism in Google Cloud, and it ensures that data is automatically encrypted at rest with no additional setup or maintenance required. It provides strong security while eliminating the need for manual key management.
Google Cloud encrypts all data at rest by default, and the simplest way to avoid key management overhead is to use Google-managed encryption keys (GMEK).
* Option A: GMEK is fully managed by Google, requiring no user intervention, and meets the requirement of no operational overhead while ensuring encryption.
* Option B: CMEK requires managing keys in Cloud KMS, adding operational overhead.
* Option C: CSEK requires users to supply and manage keys externally, increasing complexity significantly.

NEW QUESTION # 88
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 Cloud Data Fusion web interface to build data pipelines. Create a directed acyclic graph (DAG) that facilitates pipeline orchestration.
B. Use the existing data pipeline tool's BigQuery connector to reconfigure the data mapping.
C. 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.
D. Use the BigQuery Data Transfer Service to recreate the data pipeline and migrate the data into BigQuery.

Answer: B

Explanation:
Since your existing data pipeline tools already support connectors to BigQuery, the most efficient approach is to use the existing data pipeline tool's BigQuery connector to 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 # 89
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 Worker role.
B. Request the Dataflow Admin role.
C. Request the Dataflow Developer role.
D. Request the Dataflow Viewer role.

Answer: C

Explanation:
TheDataflow Developerrole 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 itgrants 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 # 90
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. Develop a Dataflow pipeline to read the data from BigQuery, perform data quality rules and transformations, and write the cleaned data back to BigQuery.
D. 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.

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 # 91
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. Create a custom script to periodically poll the Dataflow API for job status updates, and send email alerts if any errors are identified.
B. Set up a Cloud Monitoring dashboard to track key Dataflow metrics, such as data throughput, error rates, and resource utilization.
C. Use the gcloud CLI tool to retrieve job metrics and logs, and analyze them for errors and performance bottlenecks.
D. Navigate to the Dataflow Jobs page in the Google Cloud console. Use the job logs and worker logs to identify the error.

Answer: D

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 # 92
......

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