Which of the following is a best practice when onboarding data and AI sources?

Sharing a single high-privilege account across all sources for simplicity

Storing API keys in plain text in configuration files for easy access

Using service accounts with least-privilege permissions for connectivity

Disabling encryption for faster data transfer

A security team is onboarding an AI source that uses Azure Foundry. They need to ensure that the AI source can access data from a specific Azure Storage account, but only for the duration of the model's execution. What should they configure?

Make the storage account public for the duration of the model execution.

Store the storage account key in the AI source's configuration.

Use a managed identity for the AI source and grant access to the storage account with a time-limited role assignment.

Use a shared access signature (SAS) token with long expiry to access the storage account.

What is the primary purpose of configuring data source settings such as API limits and polling intervals?

To encrypt data in transit between the source and the platform

To determine which users have access to the data source

To ensure data ingestion is efficient and does not overload the source system

To format the data into a standard schema for storage

When configuring a data source for ingestion, why is it important to map the source's fields to the platform's data model?

To increase the storage capacity of the platform

To ensure that the data is correctly interpreted and usable for analysis

To reduce the amount of data transferred from the source

To authenticate the data source with the platform

What is a recommended practice for monitoring the health of data and AI source integrations?

Disabling all alerts to reduce noise

Manually testing the integration once a year

Only checking the integration status when a problem is reported

Regularly reviewing integration logs and setting up alerts for failures

Palo Alto NetworksCertified Cloud Security Engineer

Domain 2Objective 2

2.2 Explain the Process of Onboarding and Configuring Data and AI Sources (e.g., Office 365, Databricks, Azure Foundry) CLOUD-SECURITY-ENGINEER Practice Questions (Page 3)

Part of the Integration domain, which accounts for 16% of the CLOUD-SECURITY-ENGINEER exam.

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Questions 11–15

11foundation · easy
Which of the following is a best practice when onboarding data and AI sources?
Select an answer first
12application · medium
A security team is onboarding an AI source that uses Azure Foundry. They need to ensure that the AI source can access data from a specific Azure Storage account, but only for the duration of the model's execution. What should they configure?
Select an answer first
13foundation · easy
What is the primary purpose of configuring data source settings such as API limits and polling intervals?
Select an answer first
14foundation · easy
When configuring a data source for ingestion, why is it important to map the source's fields to the platform's data model?
Select an answer first
15foundation · easy
What is a recommended practice for monitoring the health of data and AI source integrations?
Select an answer first

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