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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.

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Google Cloud Associate Data Practitioner Sample Questions (Q24-Q29):

NEW QUESTION # 24
You work for a global financial services company that trades stocks 24/7. You have a Cloud SGL for PostgreSQL user database. You need to identify a solution that ensures that the database is continuously operational, minimizes downtime, and will not lose any data in the event of a zonal outage. What should you do?

A. Configure and create a high-availability Cloud SQL instance with the primary instance in zone A and a secondary instance in any zone other than zone A.
B. Continuously back up the Cloud SGL instance to Cloud Storage. Create a Compute Engine instance with PostgreSCL in a different region. Restore the backup in the Compute Engine instance if a failure occurs.
C. Create a read replica in another region. Promote the replica to primary if a failure occurs.
D. Create a read replica in the same region but in a different zone.

Answer: A

Explanation:
Configuring a high-availability (HA) Cloud SQL instance ensures continuous operation, minimizes downtime, and prevents data loss in the event of a zonal outage. In this setup, the primary instance is located in one zone (e.g., zone A), and a synchronous secondary instance is located in a different zone within the same region. This configuration ensures that all data is replicated to the secondary instance in real-time. In the event of a failure in the primary zone, the system automatically promotes the secondary instance to primary, ensuring seamless failover with no data loss and minimal downtime. This is the recommended approach for mission-critical, highly available databases.

NEW QUESTION # 25
You work for a financial organization that stores transaction data in BigQuery. Your organization has a regulatory requirement to retain data for a minimum of seven years for auditing purposes. You need to ensure that the data is retained for seven years using an efficient and cost-optimized approach. What should you do?

A. Set the table-level retention policy in BigQuery to seven years.
B. Set the dataset-level retention policy in BigQuery to seven years.
C. Export the BigQuery tables to Cloud Storage daily, and enforce a lifecycle management policy that has a seven-year retention rule.
D. Create a partition by transaction date, and set the partition expiration policy to seven years.

Answer: A

Explanation:
Setting a table-level retention policy in BigQuery to seven years is the most efficient and cost-optimized solution to meet the regulatory requirement. A table-level retention policy ensures that the data cannot be deleted or overwritten before the specified retention period expires, providing compliance with auditing requirements while keeping the data within BigQuery for easy access and analysis. This approach avoids the complexity and additional costs of exporting data to Cloud Storage.

NEW QUESTION # 26
You are working on a project that requires analyzing daily social media data. You have 100 GB of JSON formatted data stored in Cloud Storage that keeps growing.
You need to transform and load this data into BigQuery for analysis. You want to follow the Google- recommended approach. What should you do?

A. Use Cloud Data Fusion to transfer the data into BigQuery raw tables, and use SQL to transform it.
B. Use Dataflow to transform the data and write the transformed data to BigQuery.
C. Manually download the data from Cloud Storage. Use a Python script to transform and upload the data into BigQuery.
D. Use Cloud Run functions to transform and load the data into BigQuery.

Answer: B

Explanation:
Comprehensive and Detailed in Depth Explanation:
Why C is correct:Dataflow is a fully managed service for transforming and enriching data in both batch and streaming modes.
Dataflow is googles recomended way to transform large datasets.
It is designed for parallel processing, making it suitable for large datasets.
Why other options are incorrect:A: Manual downloading and scripting is not scalable or efficient.
B: Cloud Run functions are for stateless applications, not large data transformations.
D: While Cloud Data fusion could work, Dataflow is more optimized for large scale data transformation.

NEW QUESTION # 27
You are developing a data ingestion pipeline to load small CSV files into BigQuery from Cloud Storage. You want to load these files upon arrival to minimize data latency. You want to accomplish this with minimal cost and maintenance. What should you do?

A. Create a Cloud Run function to load the data into BigQuery that is triggered when data arrives in Cloud Storage.
B. Use the bq command-line tool within a Cloud Shell instance to load the data into BigQuery.
C. Create a Cloud Composer pipeline to load new files from Cloud Storage to BigQuery and schedule it to run every 10 minutes.
D. Create a Dataproc cluster to pull CSV files from Cloud Storage, process them using Spark, and write the results to BigQuery.

Answer: A

Explanation:
Using a Cloud Run function triggered by Cloud Storage to load the data into BigQuery is the best solution because it minimizes both cost and maintenance while providing low-latency data ingestion. Cloud Run is a serverless platform that automatically scales based on the workload, ensuring efficient use of resources without requiring a dedicated instance or cluster. It integrates seamlessly with Cloud Storage event notifications, enabling real-time processing of incoming files and loading them into BigQuery. This approach is cost-effective, scalable, and easy to manage.

NEW QUESTION # 28
You are using your own data to demonstrate the capabilities of BigQuery to your organization's leadership team. You need to perform a one- time load of the files stored on your local machine into BigQuery using as little effort as possible. What should you do?

A. Create a Dataflow job using the Apache Beam FileIO and BigQueryIO connectors with a local runner.
B. Execute the bq load command on your local machine.
C. Create a Dataproc cluster, copy the files to Cloud Storage, and write an Apache Spark job using the spark-bigquery-connector.
D. Write and execute a Python script using the BigQuery Storage Write API library.

Answer: B

Explanation:
Using the bq load command is the simplest and most efficient way to perform a one-time load of files from your local machine into BigQuery. This command-line tool is easy to use, requires minimal setup, and supports direct uploads from local files to BigQuery tables. It meets the requirement for minimal effort while allowing you to quickly demonstrate BigQuery's capabilities to your organization's leadership team.

NEW QUESTION # 29
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Associate-Data-Practitioner Actual Test & Associate-Data-Practitioner Exam Simulations

Google Associate-Data-Practitioner Exam Syllabus Topics:

Associate-Data-Practitioner Exam Simulations - Associate-Data-Practitioner Printable PDF

Google Cloud Associate Data Practitioner Sample Questions (Q24-Q29):

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