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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 (Q24-Q29):

NEW QUESTION # 24
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 Dataflow directed acyclic graph (DAG) coded in Python. Use Cloud Scheduler to schedule the code to run 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 Cloud Data Fusion and Wrangler flow. Schedule the flow to run once a week.
D. Create a Colab Enterprise notebook and use the bigframes.pandas library. Schedule the notebook to execute once a week.

Answer: A

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 # 25
Your organization sends IoT event data to a Pub/Sub topic. Subscriber applications read and perform transformations on the messages before storing them in the data warehouse. During particularly busy times when more data is being written to the topic, you notice that the subscriber applications are not acknowledging messages within the deadline. You need to modify your pipeline to handle these activity spikes and continue to process the messages. What should you do?

A. Forward unacknowledged messages to a dead-letter topic.
B. Seek back to the last acknowledged message.
C. Retry messages until they are acknowledged.
B Implement flow control on the subscribers

Answer: A

Explanation:
Implementingflow control on the subscribersallows the subscriber applications to manage message processing during activity spikes by controlling the rate at which messages are pulled and processed. This prevents overwhelming the subscribers and ensures that messages are acknowledged within the deadline. Flow control helps maintain the stability of your pipeline during high-traffic periods without dropping or delaying messages unnecessarily.

NEW QUESTION # 26
Your retail company 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 platforms

You are designing a data pipeline to extract this data. Which Google Cloud storage system(s) should you select for further analysis and ML model training?

A. 1. Online transactions: Cloud SQL for MySQL
2. Customer feedback: BigQuery
3. Social media activity: Cloud Storage
B. 1. Online transactions: Cloud Storage
2. Customer feedback: Cloud Storage
3. Social media activity: Cloud Storage
C. 1. Online transactions: Bigtable
2. Customer feedback: Cloud Storage
3. Social media activity: CloudSQL for MySQL
D. 1. Online transactions: BigQuery
2. Customer feedback: Cloud Storage
3. Social media activity: BigQuery

Answer: D

Explanation:
Online transactions:Storing the transactional data inBigQueryis ideal because BigQuery is a serverless data warehouse optimized for querying and analyzing structured data at scale. It supports SQL queries and is suitable for structured transactional data.
Customer feedback:Storing customer feedback inCloud Storageis appropriate as it allows you to store unstructured text files reliably and at a low cost. Cloud Storage also integrates well with data processing and ML tools for further analysis.
Social media activity:Storing real-time social media activity inBigQueryis optimal because BigQuery supports streaming inserts, enabling real-time ingestion and analysis of data. This allows immediate analysis and integration into dashboards or ML pipelines.

NEW QUESTION # 27
Your company has an on-premises file server with 5 TB of data that needs to be migrated to Google Cloud.
The network operations team has mandated that you can only use up to 250 Mbps of the total available bandwidth for the migration. You need to perform an online migration to Cloud Storage. What should you do?

A. Use the gcloud storage cp command to copy all files from on-premises to Cloud Storage using the -- daisy-chain option.
B. Use Storage Transfer Service to configure an agent-based transfer. Set the appropriate bandwidth limit for the agent pool.
C. Request a Transfer Appliance, copy the data to the appliance, and ship it back to Google Cloud.
D. Use the gcloud storage cp command to copy all files from on-premises to Cloud Storage using the --no- clobber option.

Answer: B

Explanation:
Comprehensive and Detailed in Depth Explanation:
Why A is correct:Storage Transfer Service with agent-based transfer allows for online migrations and provides the ability to set bandwidth limits.
Agents are installed on-premises and can be configured to respect network constraints.
Why other options are incorrect:B: The --daisy-chain option is not related to bandwidth control.
C: Transfer Appliance is for offline migrations and is not suitable for online transfers with bandwidth constraints.
D: The --no-clobber option prevents overwriting existing files but does not control bandwidth.

NEW QUESTION # 28
Your organization's website uses an on-premises MySQL as a backend database. You need to migrate the on- premises MySQL database to Google Cloud while maintaining MySQL features. You want to minimize administrative overhead and downtime. What should you do?

A. Use a Google-provided Dataflow template to replicate the MySQL database in BigQuery.
B. Use Database Migration Service to transfer the data to Cloud SQL for MySQL, and configure the on premises MySQL database as the source.
C. Install MySQL on a Compute Engine virtual machine. Export the database files using the mysqldump command. Upload the files to Cloud Storage, and import them into the MySQL instance on Compute Engine.
D. Export the database tables to CSV files, and upload the files to Cloud Storage. Convert the MySQL schema to a Spanner schema, create a JSON manifest file, and run a Google-provided Dataflow template to load the data into Spanner.

Answer: B

Explanation:
Comprehensive and Detailed in Depth Explanation:
Why B is correct:Database Migration Service (DMS) is designed for migrating databases to Cloud SQL with minimal downtime and administrative overhead.
Cloud SQL for MySQL is a fully managed MySQL service, which aligns with the requirement to minimize administrative overhead.
Why other options are incorrect:A: Installing MySQL on Compute Engine requires manual management of the database instance, which increases administrative overhead.
C: BigQuery is not a direct replacement for a relational MySQL database. It's an analytical data warehouse.
D: Spanner is a globally distributed, scalable database, but it requires schema conversion and is not a direct replacement for MySQL, and it is also much more complex than cloud SQL.

NEW QUESTION # 29
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Associate-Data-Practitioner Real Braindumps Materials are Definitely Valuable Acquisitions - NewPassLeader

Google Associate-Data-Practitioner Exam Syllabus Topics:

Test Associate-Data-Practitioner Quiz & Associate-Data-Practitioner Exam Questions Vce

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

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