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Reliable Google Associate-Data-Practitioner Dumps Free - Associate-Data-Practitioner Reliable Test Answers

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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 (Q100-Q105):

NEW QUESTION # 100
Your organization is conducting analysis on regional sales metrics. Data from each regional sales team is stored as separate tables in BigQuery and updated monthly. You need to create a solution that identifies the top three regions with the highest monthly sales for the next three months. You want the solution to automatically provide up-to-date results. What should you do?

A. Create a BigQuery materialized view that performs a union across all of the regional sales tables. Use the rank() window function to query the new materialized view.
B. Create a BigQuery table that performs a union across all of the regional sales tables. Use the row_number() window function to query the new table.
C. Create a BigQuery table that performs a cross join across all of the regional sales tables. Use the rank() window function to query the new table.
D. Create a BigQuery materialized view that performs a cross join across all of the regional sales tables.Use the row_number() window function to query the new materialized view.

Answer: A

Explanation:
Comprehensive and Detailed in Depth Explanation:
Why C is correct:Materialized views in BigQuery are precomputed views that periodically cache the results of a query. This ensures up-to-date results automatically.
A UNION is the correct operation to combine the data from multiple regional sales tables.
RANK() function is correct to rank the sales regions. ROW_NUMBER() would create a unique number for each row, even if sales amount is the same, this is not the desired function.
Why other options are incorrect:A and B: Standard tables do not provide automatic updates.
D: A CROSS JOIN would produce a Cartesian product, which is not appropriate for combining regional sales data.
Cross join is used when you want every combination of rows from tables, not a aggregation of data.

NEW QUESTION # 101
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. 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.
C. Use the Cloud Data Fusion web interface to build data pipelines. Create a directed acyclic graph (DAG) that facilitates pipeline orchestration.
D. Use the BigQuery Data Transfer Service to recreate the data pipeline and 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 # 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. Set up a Cloud CDN in front of the bucket.
B. Enable Object Versioning on the bucket.
C. Store the data in Nearline storaqe.
D. Store the data in a multi-region bucket.

Answer: D

Explanation:
Storing the data in amulti-region bucketensures 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.
A single-zone outage requires high availability across zones or regions. Cloud Storage offers location-based redundancy options:
* Option A: Cloud CDN caches content for web delivery but doesn't protect against underlying storage outages-it's for performance, not availability of the source data.
* Option B: Object Versioning retains old versions of objects, protecting against overwrites or deletions, but doesn't ensure availability during a zone failure (still tied to one location).
* Option C: Multi-region buckets (e.g., us or eu) replicate data across multiple regions, ensuring accessibility even if a single zone or region fails. This provides the highest availability for sensitive data in a pipeline.

NEW QUESTION # 103
You want to process and load a daily sales CSV file stored in Cloud Storage into BigQuery for downstream reporting. You need to quickly build a scalable data pipeline that transforms the data while providing insights into data quality issues. What should you do?

A. Create a batch pipeline in Dataflow by using the Cloud Storage CSV file to BigQuery batch template.
B. Load the CSV file as a table in BigQuery, and use scheduled queries to run SQL transformation scripts.
C. Create a batch pipeline in Cloud Data Fusion by using a Cloud Storage source and a BigQuery sink.
D. Load the CSV file as a table in BigQuery. Create a batch pipeline in Cloud Data Fusion by using a BigQuery source and sink.

Answer: C

Explanation:
Using Cloud Data Fusion to create a batch pipeline with a Cloud Storage source and a BigQuery sink is the best solution because:
Scalability: Cloud Data Fusion is a scalable, fully managed data integration service.
Data transformation: It provides a visual interface to design pipelines, enabling quick transformation of data.
Data quality insights: Cloud Data Fusion includes built-in tools for monitoring and addressing data quality issues during the pipeline creation and execution process.

NEW QUESTION # 104
Your organization has highly sensitive data that gets updated once a day and is stored across multiple datasets in BigQuery. You need to provide a new data analyst access to query specific data in BigQuery while preventing access to sensitive dat a. What should you do?

A. Create a new Google Cloud project, and copy the limited data into a BigQuery table. Grant the data analyst the BigQuery Data Owner IAM role in the new Google Cloud project.
B. Grant the data analyst the BigQuery Job User IAM role in the Google Cloud project.
C. Grant the data analyst the BigQuery Data Viewer IAM role in the Google Cloud project.
D. Create a materialized view with the limited data in a new dataset. Grant the data analyst BigQuery Data Viewer IAM role in the dataset and the BigQuery Job User IAM role in the Google Cloud project.

Answer: D

Explanation:
Creating a materialized view with the limited data in a new dataset and granting the data analyst the BigQuery Data Viewer role on the dataset and the BigQuery Job User role in the project ensures that the analyst can query only the non-sensitive data without access to sensitive datasets. Materialized views allow you to predefine what subset of data is visible, providing a secure and efficient way to control access while maintaining compliance with data governance policies. This approach follows the principle of least privilege while meeting the requirements.

NEW QUESTION # 105
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