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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 (Q83-Q88):

NEW QUESTION # 83
Your team is building several data pipelines that contain a collection of complex tasks and dependencies that you want to execute on a schedule, in a specific order. The tasks and dependencies consist of files in Cloud Storage, Apache Spark jobs, and data in BigQuery. You need to design a system that can schedule and automate these data processing tasks using a fully managed approach. What should you do?

A. Use Cloud Tasks to schedule and run the jobs asynchronously.
B. Create directed acyclic graphs (DAGs) in Cloud Composer. Use the appropriate operators to connect to Cloud Storage, Spark, and BigQuery.
C. Create directed acyclic graphs (DAGs) in Apache Airflow deployed on Google Kubernetes Engine. Use the appropriate operators to connect to Cloud Storage, Spark, and BigQuery.
D. Use Cloud Scheduler to schedule the jobs to run.

Answer: B

Explanation:
Using Cloud Composer to create Directed Acyclic Graphs (DAGs) is the best solution because it is a fully managed, scalable workflow orchestration service based on Apache Airflow. Cloud Composer allows you to define complex task dependencies and schedules while integrating seamlessly with Google Cloud services such as Cloud Storage, BigQuery, and Dataproc for Apache Spark jobs. This approach minimizes operational overhead, supports scheduling and automation, and provides an efficient and fully managed way to orchestrate your data pipelines.

NEW QUESTION # 84
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 --no- clobber option.
B. Request a Transfer Appliance, copy the data to the appliance, and ship it back to Google Cloud.
C. Use the gcloud storage cp command to copy all files from on-premises to Cloud Storage using the -- daisy-chain option.
D. Use Storage Transfer Service to configure an agent-based transfer. Set the appropriate bandwidth limit for the agent pool.

Answer: D

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 # 85
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 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.
D. 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.

Answer: C

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 # 86
Your retail company collects customer data from various sources:
You are designing a data pipeline to extract this dat
a. Which Google Cloud storage system(s) should you select for further analysis and ML model training?

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

Answer: C

Explanation:
Online transactions: Storing the transactional data in BigQuery is 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 in Cloud Storage is 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 in BigQuery is 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 # 87
Your organization has several datasets in their data warehouse in BigQuery. Several analyst teams in different departments use the datasets to run queries. Your organization is concerned about the variability of their monthly BigQuery costs. You need to identify a solution that creates a fixed budget for costs associated with the queries run by each department. What should you do?

A. Create a custom quota for each analyst in BigQuery.
B. Assign each analyst to a separate project associated with their department. Create a single reservation by using BigQuery editions. Assign all projects to the reservation.
C. Create a single reservation by using BigQuery editions. Assign all analysts to the reservation.
D. Assign each analyst to a separate project associated with their department. Create a single reservation for each department by using BigQuery editions. Create assignments for each project in the appropriate reservation.

Answer: D

Explanation:
Assigning each analyst to a separate project associated with their department and creating a single reservation for each department using BigQuery editions allows for precise cost management. By assigning each project to its department's reservation, you can allocate fixed compute resources and budgets for each department, ensuring that their query costs are predictable and controlled. This approach aligns with your organization's goal of creating a fixed budget for query costs while maintaining departmental separation and accountability.

NEW QUESTION # 88
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