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Databricks Data Engineer Professional exam dumps: questions 201 to 300 of 312

Page 3 of the free Databricks Data Engineer Professional question bank for the Databricks Certified Data Engineer Professional exam. Questions 201 to 300 are listed below, the first 5 in full with answers and explanations. Back to page 1 for the exam overview and FAQ.

Question bank last updated January 2025

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Free Databricks Data Engineer Professional practice questions

Questions 201 to 205 of 312

Pick an answer before you open the explanation. Each question also has its own page with a permalink.

Databricks Data Engineer Professional Question 201

Select 3

You are configuring a Databricks workspace for a financial services company that requires fine-grained access control to meet regulatory compliance. The organization wants to ensure that sensitive data is only accessible to authorized users and that all access is auditable. Which of the following configurations and features should you implement to meet these requirements?

A
Enable Unity Catalog and configure table permissions using attribute-based access control (ABAC).
B
Use cluster-scoped access control to restrict access to sensitive clusters.
C
Configure data masking policies in Unity Catalog to obfuscate sensitive data for unauthorized users.
D
Rely on workspace-level access controls only, as they provide sufficient security for sensitive data.
E
Enable audit logs in Unity Catalog to track data access and modifications.

Show answer and explanation

Correct answers: A, C, E

Explanation

To meet regulatory compliance for fine-grained access control and auditability, you should implement Unity Catalog, which supports granular data permissions through ABAC and data masking. Additionally, enabling audit logs ensures that all access and modifications to sensitive data are tracked. Workspace-level and cluster-scoped access controls alone are not sufficient to fulfill these requirements.

A. Correct.
Correct: Unity Catalog provides fine-grained access control at the table, column, and row levels. Attribute-based access control (ABAC) is an advanced feature that enables dynamic access control based on user attributes.
B. Incorrect.
Incorrect: Cluster-scoped access control restricts who can access and manage clusters but does not address fine-grained data access requirements for regulatory compliance.
C. Correct.
Correct: Data masking in Unity Catalog allows sensitive data to be obfuscated for users without proper permissions, meeting the requirement for securing sensitive data.
D. Incorrect.
Incorrect: Relying solely on workspace-level access controls is insufficient for regulatory compliance, as these controls do not provide the necessary granularity for data access management.
E. Correct.
Correct: Enabling audit logs in Unity Catalog allows tracking of data access and modifications, ensuring compliance with regulatory requirements for auditable access.

Databricks Data Engineer Professional Question 202

Single answer

A data engineering team is tasked with securing sensitive data stored in a Databricks workspace. They want to limit access to specific tables based on user roles while ensuring auditability of access events. What is the most appropriate approach to achieve this in Databricks?

A
Implement table ACLs (Access Control Lists) in Unity Catalog and enable audit logging.
B
Use workspace object permissions to restrict access to Delta tables.
C
Encrypt the Delta tables using a custom encryption algorithm and distribute decryption keys to users.
D
Store sensitive data in plain text files in a private S3 bucket and manage access using AWS IAM policies.

Show answer and explanation

Correct answer: A

Explanation

Unity Catalog is the recommended solution for securing sensitive data in Databricks. It provides fine-grained access control through table ACLs, which allow permissions to be granted at the table, schema, or catalog level. Additionally, Unity Catalog supports audit logging, ensuring that access events can be tracked for compliance and governance purposes. Other approaches, such as workspace object permissions or custom encryption, either lack necessary functionality or introduce unnecessary complexity.

A. Correct.
This is correct because Unity Catalog provides fine-grained access control via table ACLs, along with audit logging for tracking access events. This is the recommended approach for securing sensitive data in Databricks.
B. Incorrect.
This is incorrect because workspace object permissions do not provide the fine-grained access control needed for table-level security. Additionally, they are not used for managing access to Delta tables specifically.
C. Incorrect.
This is incorrect as custom encryption algorithms introduce complexity and potential security risks. Distributing decryption keys securely is also challenging and not aligned with Databricks' security best practices.
D. Incorrect.
This is incorrect because storing sensitive data as plain text files lacks the necessary fine-grained access control. While AWS IAM policies can control access at the bucket level, they do not offer the table-level permissions or audit logging required for robust data governance.

Databricks Data Engineer Professional Question 203

Select 2

A data engineering team is tasked with setting up access control for a Databricks environment to ensure that only specific users can access and modify production data. They also need to ensure that audit logs are captured for compliance purposes. Which combination of actions should the team take to achieve these requirements?

A
Enable cluster-level access control and assign users to appropriate groups based on their roles.
B
Configure Table Access Control (TAC) and assign permissions using Databricks SQL grants.
C
Set up an external logging destination to capture Databricks audit logs for long-term storage.
D
Disable Personal Access Tokens (PATs) for all users to enhance security.
E
Configure Databricks Repos to enforce version control on production data.

Show answer and explanation

Correct answers: B, C

Explanation

To ensure proper access control, the team should use Table Access Control (TAC) to define permissions for production data. Additionally, configuring an external logging destination for audit logs satisfies the compliance requirement. Other options, while useful for security or workflow management, do not directly fulfill the requirements specified in the scenario.

A. Incorrect.
Cluster-level access control manages who can use specific clusters but does not directly address data access control or audit logging, which are the requirements in this scenario.
B. Correct.
Table Access Control (TAC) allows fine-grained permissions on tables and views, ensuring that only authorized users can access production data, making it essential for meeting the access control requirement.
C. Correct.
Setting up an external logging destination ensures that audit logs are captured and stored securely for compliance purposes, addressing the requirement for audit logging.
D. Incorrect.
Disabling Personal Access Tokens (PATs) can enhance security, but it is not directly related to the requirements of data access control or audit logging in this scenario.
E. Incorrect.
Configuring Databricks Repos enforces version control but does not address data access control or audit logging requirements.

Databricks Data Engineer Professional Question 204

Select 3

A data engineering team is tasked with securing access to sensitive data stored in Delta tables. They need to ensure that only specific groups of users can read or write to the data while maintaining compliance with privacy regulations. Which of the following steps should the team take to implement secure access control in Databricks?

A
Configure table-level access permissions using Unity Catalog
B
Use Databricks secrets to store credentials and share them with all users
C
Set up fine-grained access control for Delta tables via SQL GRANT statements in Unity Catalog
D
Assign workspace-wide admin permissions to all team members to simplify access management
E
Enable column-level security to restrict access to sensitive columns in the Delta table

Show answer and explanation

Correct answers: A, C, E

Explanation

To secure access to sensitive data in Delta tables, the team should leverage Unity Catalog for table-level and column-level access controls, as well as use GRANT statements to define fine-grained permissions. These approaches ensure compliance with privacy regulations and follow best practices for data security, such as the principle of least privilege. Databricks secrets can be used for credential management but should not be shared broadly, and assigning admin permissions to all users poses significant security risks.

A. Correct.
Correct. Unity Catalog provides table-level access controls, allowing you to define permissions for specific user groups or roles.
B. Incorrect.
Incorrect. While Databricks secrets are useful for managing sensitive credentials, sharing them with all users compromises security and does not directly provide data access control.
C. Correct.
Correct. Unity Catalog supports fine-grained access control via SQL GRANT statements, enabling precise control over who can access or modify data.
D. Incorrect.
Incorrect. Assigning workspace-wide admin permissions to all team members is not a secure practice and violates the principle of least privilege.
E. Correct.
Correct. Column-level security is a feature that allows you to restrict access to specific columns within a Delta table, ensuring sensitive data is not exposed unnecessarily.

Databricks Data Engineer Professional Question 205

Select 3

A data engineering team needs to create a dynamic view in Databricks to mask sensitive customer data such as Social Security Numbers (SSNs) based on the user's role. Users with the 'admin' role should see the full SSN, while others should see only the last four digits of the SSN with the rest of the digits masked. Which of the following approaches should the team use to implement this requirement?

A
Use a CASE statement in the view definition to conditionally display the masked or unmasked SSN based on the user's role.
B
Store the user roles and permissions in a table, and join it with the data table in the dynamic view to determine the appropriate masking logic.
C
Use Databricks secrets to store SSNs securely and dynamically retrieve them in the view for authorized users.
D
Grant SELECT permission on the dynamic view to specific users, and use Databricks ACLs to enforce data masking.
E
Leverage the is_member function within the view definition to check the user's group membership and apply masking logic.

Show answer and explanation

Correct answers: A, B, E

Explanation

Dynamic views in Databricks allow data masking by applying conditional logic within the view definition. Using a combination of the CASE statement, user role data, and the is_member function enables dynamic and scalable masking based on user roles or group memberships. Databricks secrets and ACLs are useful for other security purposes but are not directly applicable for implementing dynamic data masking in this scenario.

A. Correct.
This is correct. A CASE statement can be used within the dynamic view to apply conditional logic to show masked or unmasked data based on user roles.
B. Correct.
This is correct. Joining with a table containing user roles and permissions allows dynamic role-based decisions within the view, enabling appropriate masking.
C. Incorrect.
This is incorrect. Databricks secrets are used for securely storing credentials, not for dynamically masking sensitive data in views.
D. Incorrect.
This is incorrect. While ACLs can manage access at the table or view level, they do not handle row- or column-level masking logic within views.
E. Correct.
This is correct. The is_member function is commonly used to check group membership and can be included in the view definition to dynamically apply masking logic.

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Databricks Data Engineer Professional practice questions 201 to 300 of 312

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