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Google Cloud Associate Data Practitioner Sample Questions (Q50-Q55):

NEW QUESTION # 50
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. Convert each DAG to a Cloud Workflow and automate the execution with Cloud Scheduler.
B. Create a Cloud Data Fusion instance. For each DAG, create a Cloud Data Fusion pipeline.
C. Create a Google Kubernetes Engine (GKE) standard cluster and deploy Airflow as a workload. Migrate all DAGs to the new Airflow environment.
D. Create a new Cloud Composer environment and copy DAGs to the Cloud Composer dags/ folder.

Answer: D

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 # 51
You work for an ecommerce company that has a BigQuery dataset that contains customer purchase history, demographics, and website interactions. You need to build a machine learning (ML) model to predict which customers are most likely to make a purchase in the next month. You have limited engineering resources and need to minimize the ML expertise required for the solution. What should you do?

A. Export the data to Cloud Storage, and use AutoML Tables to build a classification model for purchase prediction.
B. Use BigQuery ML to create a logistic regression model for purchase prediction.
C. Use Colab Enterprise to develop a custom model for purchase prediction.
D. Use Vertex AI Workbench to develop a custom model for purchase prediction.

Answer: B

NEW QUESTION # 52
You are designing a pipeline to process data files that arrive in Cloud Storage by 3:00 am each day. Data processing is performed in stages, where the output of one stage becomes the input of the next. Each stage takes a long time to run. Occasionally a stage fails, and you have to address the problem. You need to ensure that the final output is generated as quickly as possible. What should you do?

A. Design the pipeline as a set of PTransforms in Dataflow. Restart the pipeline after correcting any stage output data errors.
B. Design the processing as a directed acyclic graph (DAG) in Cloud Composer. Clear the state of the failed task after correcting any stage output data errors.
C. Design the workflow as a Cloud Workflow instance. Code the workflow to jump to a given stage based on an input parameter. Rerun the workflow after correcting any stage output data errors.
D. Design a Spark program that runs under Dataproc. Code the program to wait for user input when an error is detected. Rerun the last action after correcting any stage output data errors.

Answer: B

Explanation:
Using Cloud Composer to design the processing pipeline as a Directed Acyclic Graph (DAG) is the most suitable approach because:
Fault tolerance: Cloud Composer (based on Apache Airflow) allows for handling failures at specific stages. You can clear the state of a failed task and rerun it without reprocessing the entire pipeline.
Stage-based processing: DAGs are ideal for workflows with interdependent stages where the output of one stage serves as input to the next.
Efficiency: This approach minimizes downtime and ensures that only failed stages are rerun, leading to faster final output generation.

NEW QUESTION # 53
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 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.
B. Use Cloud Run functions to subscribe to the Pub/Sub topic, process the data, and write it to BigQuery using the streaming API.
C. 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.
D. Use Dataproc to create an Apache Spark streaming job that reads the data from Pub/Sub, processes the data, and writes it to BigQuery.

Answer: A

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 # 54
Your organization needs to implement near real-time analytics for thousands of events arriving each second in Pub/Sub. The incoming messages require transformations. You need to configure a pipeline that processes, transforms, and loads the data into BigQuery while minimizing development time. What should you do?

A. Use a Google-provided Dataflow template to process the Pub/Sub messages, perform transformations, and write the results to BigQuery.
B. Create a Cloud Data Fusion instance and configure Pub/Sub as a source. Use Data Fusion to process the Pub/Sub messages, perform transformations, and write the results to BigQuery.
C. Use Cloud Run functions to process the Pub/Sub messages, perform transformations, and write the results to BigQuery.
D. Load the data from Pub/Sub into Cloud Storage using a Cloud Storage subscription. Create a Dataproc cluster, use PySpark to perform transformations in Cloud Storage, and write the results to BigQuery.

Answer: A

Explanation:
Using a Google-provided Dataflow template is the most efficient and development-friendly approach to implement near real-time analytics for Pub/Sub messages. Dataflow templates are pre-built and optimized for processing streaming data, allowing you to quickly configure and deploy a pipeline with minimal development effort. These templates can handle message ingestion from Pub/Sub, perform necessary transformations, and load the processed data into BigQuery, ensuring scalability and low latency for near real-time analytics.

NEW QUESTION # 55
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