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Google Cloud Associate Data Practitioner Sample Questions (Q69-Q74):

NEW QUESTION # 69
You manage an ecommerce website that has a diverse range of products. You need to forecast future product demand accurately to ensure that your company has sufficient inventory to meet customer needs and avoid stockouts. Your company's historical sales data is stored in a BigQuery table. You need to create a scalable solution that takes into account the seasonality and historical data to predict product demand. What should you do?

A. Use the historical sales data to train and create a BigQuery ML linear regression model. Use the ML.
PREDICT function call to output the predictions into a new BigQuery table.
B. Use the historical sales data to train and create a BigQuery ML time series model. Use the ML.
FORECAST function call to output the predictions into a new BigQuery table.
C. Use the historical sales data to train and create a BigQuery ML logistic regression model. Use the ML.PREDICT function call to output the predictions into a new BigQuery table.
D. Use Colab Enterprise to create a Jupyter notebook. Use the historical sales data to train a custom prediction model in Python.

Answer: B

Explanation:
Comprehensive and Detailed In-Depth Explanation:
Forecasting product demand with seasonality requires a time series model, and BigQuery ML offers a scalable, serverless solution. Let's analyze:
* Option A: BigQuery ML's time series models (e.g., ARIMA_PLUS) are designed for forecasting with seasonality and trends. The ML.FORECAST function generates predictions based on historical data, storing them in a table. This is scalable (no infrastructure) and integrates natively with BigQuery, ideal for ecommerce demand prediction.
* Option B: Colab Enterprise with a custom Python model (e.g., Prophet) is flexible but requires coding, maintenance, and potentially exporting data, reducing scalability compared to BigQuery ML's in-place processing.
* Option C: Linear regression predicts continuous values but doesn't handle seasonality or time series patterns effectively, making it unsuitable for demand forecasting.

NEW QUESTION # 70
Your company has several retail locations. Your company tracks the total number of sales made at each location each day. You want to use SQL to calculate the weekly moving average of sales by location to identify trends for each store. Which query should you use?

Answer: B

Explanation:
To calculate the weekly moving average of sales by location:
The query must group by store_id (partitioning the calculation by each store).
The ORDER BY date ensures the sales are evaluated chronologically.
The ROWS BETWEEN 6 PRECEDING AND CURRENT ROW specifies a rolling window of 7 rows (1 week if each row represents daily data).
The AVG(total_sales) computes the average sales over the defined rolling window.
Chosen query meets these requirements:

NEW QUESTION # 71
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. Create directed acyclic graphs (DAGs) in Cloud Composer. Use the appropriate operators to connect to Cloud Storage, Spark, and BigQuery.
B. 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.
C. Use Cloud Tasks to schedule and run the jobs asynchronously.
D. Use Cloud Scheduler to schedule the jobs to run.

Answer: A

Explanation:
UsingCloud Composerto 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.
Extract from Google Documentation: From "Cloud Composer Overview" (https://cloud.google.com
/composer/docs):"Cloud Composer is a fully managed workflow orchestration service built on Apache Airflow, enabling you to schedule and automate complex data pipelines with dependencies across Google Cloud services like Cloud Storage, Dataproc, and BigQuery."

NEW QUESTION # 72
You have a Dataproc cluster that performs batch processing on data stored in Cloud Storage. You need to schedule a daily Spark job to generate a report that will be emailed to stakeholders. You need a fully-managed solution that is easy to implement and minimizes complexity. What should you do?

A. Use Cloud Composer to orchestrate the Spark job and email the report.
B. Use Dataproc workflow templates to define and schedule the Spark job, and to email the report.
C. Use Cloud Run functions to trigger the Spark job and email the report.
D. Use Cloud Scheduler to trigger the Spark job. and use Cloud Run functions to email the report.

Answer: B

Explanation:
Using Dataproc workflow templates is a fully-managed and straightforward solution for defining and scheduling your Spark job on a Dataproc cluster. Workflow templates allow you to automate the execution of Spark jobs with predefined steps, including data processing and report generation. You can integrate email notifications by adding a step to the workflow that sends the report using tools like a Cloud Function or external email service. This approach minimizes complexity while leveraging Dataproc's managed capabilities for batch processing.

NEW QUESTION # 73
You are storing data in Cloud Storage for a machine learning project. The data is frequently accessed during the model training phase, minimally accessed after 30 days, and unlikely to be accessed after 90 days. You need to choose the appropriate storage class for the different stages of the project to minimize cost. What should you do?

A. Store the data in Standard storage during the model training phase. Transition the data to Nearline storage 30 days after model deployment, and to Coldline storage 90 days after model deployment.
B. Store the data in Nearline storage during the model training phase. Transition the data to Coldline storage 30 days after model deployment, and to Archive storage 90 days after model deployment.
C. Store the data in Nearline storage during the model training phase. Transition the data to Archive storage 30 days after model deployment, and to Coldline storage 90 days after model deployment.
D. Store the data in Standard storage during the model training phase. Transition the data to Durable Reduced Availability (DRA) storage 30 days after model deployment, and to Coldline storage 90 days after model deployment.

Answer: A

Explanation:
Comprehensive and Detailed In-Depth Explanation:
Cost minimization requires matching storage classes to access patterns using lifecycle rules. Let's assess:
* Option A: Nearline during training (frequent access) incurs high retrieval costs and latency, unsuitable for ML workloads. Coldline after 30 days and Archive after 90 days are reasonable but misaligned initially.
* Option B: Standard storage (no retrieval fees, low latency) is ideal for frequent access during training.
Transitioning to Nearline (30-day minimum, low access) after 30 days and Coldline (90-day minimum, rare access) after 90 days matches the pattern and minimizes costs effectively.
* Option C: Nearline during training is costly for frequent access, and Archive to Coldline is illogical (Archive is cheaper than Coldline).

NEW QUESTION # 74
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Google Cloud Associate Data Practitioner Sample Questions (Q69-Q74):

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