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Pass Guaranteed Quiz 2025 The Best Google Exam Associate-Data-Practitioner Experience

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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 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 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.

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Google Cloud Associate Data Practitioner Sample Questions (Q92-Q97):

NEW QUESTION # 92
Following a recent company acquisition, you inherited an on-premises data infrastructure that needs to move to Google Cloud. The acquired system has 250 Apache Airflow directed acyclic graphs (DAGs) orchestrating data pipelines. You need to migrate the pipelines to a Google Cloud managed service with minimal effort.
What should you do?

A. Create a Cloud Data Fusion instance. For each DAG, create a Cloud Data Fusion pipeline.
B. Convert each DAG to a Cloud Workflow and automate the execution with Cloud Scheduler.
C. Create a new Cloud Composer environment and copy DAGs to the Cloud Composer dags/ folder.
D. Create a Google Kubernetes Engine (GKE) standard cluster and deploy Airflow as a workload. Migrate all DAGs to the new Airflow environment.

Answer: C

Explanation:
Comprehensive and Detailed in Depth Explanation:
Why B is correct:Cloud Composer is a managed Apache Airflow service that provides a seamless migration path for existing Airflow DAGs.
Simply copying the DAGs to the Cloud Composer folder allows them to run directly on Google Cloud.
Why other options are incorrect:A: Cloud Workflows is a different orchestration tool, not compatible with Airflow DAGs.
C: GKE deployment requires setting up and managing a Kubernetes cluster, which is more complex.
D: Cloud Data Fusion is a data integration tool, not suitable for orchestrating existing pipelines.

NEW QUESTION # 93
Your organization needs to store historical customer order dat
a. The data will only be accessed once a month for analysis and must be readily available within a few seconds when it is accessed. You need to choose a storage class that minimizes storage costs while ensuring that the data can be retrieved quickly. What should you do?

A. Store the data in Cloud Storage using Archive storage.
B. Store the data in Cloud Storaqe usinq Coldline storaqe.
C. Store the data in Cloud Storaqe usinq Nearline storaqe.
D. Store the data in Cloud Storage using Standard storage.

Answer: C

Explanation:
Using Nearline storage in Cloud Storage is the best option for data that is accessed infrequently (such as once a month) but must be readily available within seconds when needed. Nearline offers a balance between low storage costs and quick retrieval times, making it ideal for scenarios like monthly analysis of historical data. It is specifically designed for infrequent access patterns while avoiding the higher retrieval costs and longer access times of Coldline or Archive storage.

NEW QUESTION # 94
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 Cloud Data Fusion and Wrangler flow. Schedule the flow to run once a week.
B. Create a Colab Enterprise notebook and use the bigframes.pandas library. Schedule the notebook to execute once a week.
C. Create a Dataflow directed acyclic graph (DAG) coded in Python. Use Cloud Scheduler to schedule the code to run once a week.
D. Create a Cloud Run function that uses NumPy. Use Cloud Scheduler to schedule the function to run once a week.

Answer: C

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 # 95
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. Use Cloud Scheduler to schedule the jobs to run.
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. Create directed acyclic graphs (DAGs) in Cloud Composer. Use the appropriate operators to connect to Cloud Storage, Spark, and BigQuery.

Answer: D

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 # 96
You work for a gaming company that collects real-time player activity data. This data is streamed into Pub
/Sub and needs to be processed and loaded into BigQuery for analysis. The processing involves filtering, enriching, and aggregating the data before loading it into partitioned BigQuery tables. Youneed to design a pipeline that ensures low latency and high throughput while following a Google-recommended approach.
What should you do?

A. Use Cloud Run functions to subscribe to the Pub/Sub topic, process the data, and write it to BigQuery using the streaming API.
B. Use Dataproc to create an Apache Spark streaming job that reads the data from Pub/Sub, processes the data, and writes it to BigQuery.
C. Use Dataflow to create a streaming pipeline that reads the data from Pub/Sub, processes the data, and writes it to BigQuery using the streaming API.
D. Use Cloud Composer to orchestrate a workflow that reads the data from Pub/Sub, processes the data using a Python script, and writes it to BigQuery.

Answer: C

Explanation:
Comprehensive and Detailed in Depth Explanation:
Why C is correct:Dataflow is the recommended service for real-time stream processing on Google Cloud.
It provides scalable and reliable processing with low latency and high throughput.
Dataflow's streaming API is optimized for Pub/Sub integration and BigQuery streaming inserts.
Why other options are incorrect:A: Cloud Composer is for batch orchestration, not real-time streaming.
B: Dataproc and Spark streaming are more complex and not as efficient as Dataflow for this task.
D: Cloud Run functions are for stateless, event-driven applications, not continuous stream processing.

NEW QUESTION # 97
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