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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 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 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.

Google Cloud Associate Data Practitioner Sample Questions (Q79-Q84):

NEW QUESTION # 79
You work for a retail company that collects customer data from various sources:
* Online transactions: Stored in a MySQL database
* Customer feedback: Stored as text files on a company server
* Social media activity: Streamed in real-time from social media platformsYou need to design a data pipeline to extract and load the data into the appropriate Google Cloud storage system(s) for further analysis and ML model training. What should you do?

A. Extract and load the online transactions data, customer feedback data, and social media activity into Cloud Storage.
B. Copy the online transactions data into Cloud SQL for MySQL. Import the customer feedback into BigQuery. Stream the social media activity into Cloud Storage.
C. Extract and load the online transactions data into Bigtable. Import the customer feedback data into Cloud Storage. Store the social media activity in Cloud SQL for MySQL.
D. Extract and load the online transactions data into BigQuery. Load the customer feedback data into Cloud Storage. Stream the social media activity by using Pub/Sub and Dataflow, and store the data in BigQuery.

Answer: D

Explanation:
Comprehensive and Detailed In-Depth Explanation:
The pipeline must extract diverse data types and load them into systems optimized for analysis and ML. Let's assess:
* Option A: Cloud SQL for transactions keeps data relational but isn't ideal for analysis/ML (less scalable than BigQuery). BigQuery for feedback is fine but skips staging. Cloud Storage for streaming social media loses real-time context and requires extra steps for analysis.
* Option B: BigQuery for transactions (via export from MySQL) supports analysis/ML with SQL. Cloud Storage stages feedback text files for preprocessing, then BigQuery ingestion. Pub/Sub and Dataflow stream social media into BigQuery, enabling real-time analysis-optimal for all sources.
* Option C: Cloud Storage for all data is a staging step, not a final solution for analysis/ML, requiring additional pipelines.

NEW QUESTION # 80
You manage a web application that stores data in a Cloud SQL database. You need to improve the read performance of the application by offloading read traffic from the primary database instance. You want to implement a solution that minimizes effort and cost. What should you do?

A. Migrate the database to a larger Cloud SQL instance.
B. Enable automatic backups, and create a read replica of the Cloud SQL instance.
C. Store frequently accessed data in a Memorystore instance.
D. Use Cloud CDN to cache frequently accessed data.

Answer: B

Explanation:
Enabling automatic backups and creating a read replica of the Cloud SQL instance is the best solution to improve read performance. Read replicas allow you to offload read traffic from the primary database instance, reducing its load and improving overall performance. This approach is cost-effective and easy to implement within Cloud SQL. It ensures that the primary instance focuses on write operations while replicas handle read queries, providing a seamless performance boost with minimal effort.

NEW QUESTION # 81
Another team in your organization is requesting access to a BigQuery dataset. You need to share the dataset with the team while minimizing the risk of unauthorized copying of dat a. You also want to create a reusable framework in case you need to share this data with other teams in the future. What should you do?

A. Create authorized views in the team's Google Cloud project that is only accessible by the team.
B. Export the dataset to a Cloud Storage bucket in the team's Google Cloud project that is only accessible by the team.
C. Enable domain restricted sharing on the project. Grant the team members the BigQuery Data Viewer IAM role on the dataset.
D. Create a private exchange using Analytics Hub with data egress restriction, and grant access to the team members.

Answer: D

Explanation:
Using Analytics Hub to create a private exchange with data egress restrictions ensures controlled sharing of the dataset while minimizing the risk of unauthorized copying. This approach allows you to provide secure, managed access to the dataset without giving direct access to the raw data. The egress restriction ensures that data cannot be exported or copied outside the designated boundaries. Additionally, this solution provides a reusable framework that simplifies future data sharing with other teams or projects while maintaining strict data governance.

NEW QUESTION # 82
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 BigQuery Data Transfer Service to recreate the data pipeline and migrate the data into BigQuery.
B. Use the Cloud Data Fusion web interface to build data pipelines. Create a directed acyclic graph (DAG) that facilitates pipeline orchestration.
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 existing data pipeline tool's BigQuery connector to reconfigure the data mapping.

Answer: D

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 # 83
You work for a home insurance company. You are frequently asked to create and save risk reports with charts for specific areas using a publicly available storm event dataset. You want to be able to quickly create and re-run risk reports when new data becomes available. What should you do?

A. Reference and query the storm event dataset using SQL in a Colab Enterprise notebook. Display the table results and document with Markdown, and use Matplotlib to create charts.
B. Reference and query the storm event dataset using SQL in BigQuery Studio. Export the results to Google Sheets, and use cell data in the worksheets to create charts.
C. Export the storm event dataset as a CSV file. Import the file to Google Sheets, and use cell data in the worksheets to create charts.
D. Copy the storm event dataset into your BigQuery project. Use BigQuery Studio to query and visualize the data in Looker Studio.

Answer: D

Explanation:
Copying the storm event dataset into your BigQuery project and using BigQuery Studio to query and visualize the data in Looker Studio is the best approach. This solution allows you to create reusable and automated workflows for generating risk reports. BigQuery handles the querying efficiently, and Looker Studio provides powerful tools for creating and sharing dynamic charts and dashboards. This setup ensures that reports can be easily re-run with updated data, minimizing manual effort and providing a scalable, interactive solution for visualizing risk reports.

NEW QUESTION # 84
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Associate-Data-Practitioner Latest Test Question - Associate-Data-Practitioner Testking

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Google Associate-Data-Practitioner Exam Syllabus Topics:

Google Cloud Associate Data Practitioner Sample Questions (Q79-Q84):

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