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Google Cloud Associate Data Practitioner Sample Questions (Q49-Q54):

NEW QUESTION # 49
Your company is setting up an enterprise business intelligence platform. You need to limit data access between many different teams while following the Google-recommended approach. What should you do first?

A. Create a Looker (Google Cloud core) instance, and create a separate dashboard for each team.
B. Create a separate Looker Studio report for each team, and share each report with the individuals within each team.
C. Create one Looker Studio report with multiple pages, and add each team's data as a separate data source to the report.
D. Create a Looker (Google Cloud core) instance, and configure different Looker groups for each team.

Answer: D

Explanation:
Comprehensive and Detailed In-Depth Explanation:
For an enterprise BI platform with data access control across teams, Google recommends Looker (Google Cloud core) over Looker Studio for its robust access management. The "first" step focuses on setting up the foundation.
* Option A: Looker Studio reports are lightweight but lack granular access control beyond sharing.
Creating separate reports per team is inefficient and unscalable.
* Option B: One Looker Studio report with multiple pages and data sources doesn't enforce team-level access control natively-users could access all pages/data.
* Option C: Creating a Looker instance with separate dashboards per team is a step forward but skips the foundational access control setup (groups), reducing scalability.

NEW QUESTION # 50
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. Use Colab Enterprise to develop a custom model for purchase prediction.
B. Use BigQuery ML to create a logistic regression model for purchase prediction.
C. Export the data to Cloud Storage, and use AutoML Tables to build a classification model for purchase prediction.
D. Use Vertex AI Workbench to develop a custom model for purchase prediction.

Answer: B

NEW QUESTION # 51
Your organization has decided to move their on-premises Apache Spark-based workload to Google Cloud.
You want to be able to manage the code without needing to provision and manage your own cluster. What should you do?

A. Configure a Google Kubernetes Engine cluster with Spark operators, and deploy the Spark jobs.
B. Migrate the Spark jobs to Dataproc on Google Kubernetes Engine.
C. Migrate the Spark jobs to Dataproc on Compute Engine.
D. Migrate the Spark jobs to Dataproc Serverless.

Answer: D

Explanation:
Migrating the Spark jobs toDataproc Serverlessis the best approach because it allows you to run Spark workloads without the need to provision or manage clusters. Dataproc Serverless automatically scales resources based on workload requirements, simplifying operations and reducing administrative overhead. This solution is ideal for organizations that want to focus on managing their Spark code without worrying about the underlying infrastructure. It is cost-effective and fully managed, aligning well with the goal of minimizing cluster management.

NEW QUESTION # 52
Your organization has several datasets in BigQuery. The datasets need to be shared with your external partners so that they can run SQL queries without needing to copy the data to their own projects. You have organized each partner's data in its own BigQuery dataset. Each partner should be able to access only their data. You want to share the data while following Google-recommended practices. What should you do?

A. Export the BigQuery data to a Cloud Storage bucket. Grant the partners the storage.objectUser IAM role on the bucket.
B. Use Analytics Hub to create a listing on a private data exchange for each partner dataset. Allow each partner to subscribe to their respective listings.
C. Grant the partners the bigquery.user IAM role on the BigQuery project.
D. Create a Dataflow job that reads from each BigQuery dataset and pushes the data into a dedicated Pub
/Sub topic for each partner. Grant each partner the pubsub. subscriber IAM role.

Answer: B

Explanation:
Using Analytics Hub to create a listing on a private data exchange for each partner dataset is the Google- recommended practice for securely sharing BigQuery data with external partners. Analytics Hub allows you to manage data sharing at scale, enabling partners to query datasets directly without needing to copy the data into their own projects. By creating separate listings for each partner dataset and allowing only the respective partner to subscribe, you ensure that partners can access only their specific data, adhering to the principle of least privilege. This approach is secure, efficient, and designed for scenarios involving external data sharing.

NEW QUESTION # 53
Your company uses Looker as its primary business intelligence platform. You want to use LookML to visualize the profit margin for each of your company's products in your Looker Explores and dashboards. You need to implement a solution quickly and efficiently. What should you do?

A. Create a derived table that pre-calculates the profit margin for each product, and include it in the Looker model.
B. Apply a filter to only show products with a positive profit margin.
C. Define a new measure that calculates the profit margin by using the existing revenue and cost fields.
D. Create a new dimension that categorizes products based on their profit margin ranges (e.g., high, medium, low).

Answer: C

Explanation:
Comprehensive and Detailed in Depth Explanation:
Why B is correct:Defining a new measure in LookML is the most efficient and direct way to calculate and visualize aggregated metrics like profit margin.
Measures are designed for calculations based on existing fields.
Why other options are incorrect:A: Filtering doesn't calculate or visualize the profit margin itself.
C: Dimensions are for categorizing data, not calculating aggregated metrics.
D: Derived tables are more complex and unnecessary for a simple calculation like profit margin, which can be done using a measure.

NEW QUESTION # 54
......

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