AIF-C01 Question 208
Select 4As a machine learning engineer, you are designing a data pipeline for a financial institution to process sensitive customer data. Which of the following actions should you take to ensure secure data engineering practices?
- A
Implement encryption for data at rest and in transit.
- B
Grant all users in the organization full access to the data for flexibility.
- C
Use AWS Identity and Access Management (IAM) policies to enforce least privilege access.
- D
Regularly monitor and audit data access logs.
- E
Disable data validation checks to speed up processing.
- F
Utilize privacy-enhancing technologies like differential privacy for sensitive data.
Show answer and explanation
Correct answers: A, C, D, F
Explanation
The best practices for secure data engineering include implementing encryption, enforcing least privilege access through IAM policies, monitoring and auditing access logs, and using privacy-enhancing technologies. These practices ensure data confidentiality, integrity, and quality while minimizing risks associated with unauthorized access or data misuse.
- A. Correct.
Implementing encryption for data at rest and in transit is a critical security best practice to protect sensitive data from unauthorized access during storage or transmission.
- B. Incorrect.
Granting all users full access violates the principle of least privilege and increases the risk of data breaches or misuse.
- C. Correct.
Using IAM policies to enforce least privilege ensures that users and services only have access to the resources they absolutely need, which enhances security.
- D. Correct.
Regularly monitoring and auditing data access logs helps detect unauthorized access or suspicious activity, reinforcing data security.
- E. Incorrect.
Disabling data validation checks is not advisable as it compromises data quality and integrity, which are essential for secure and accurate data engineering.
- F. Correct.
Privacy-enhancing technologies like differential privacy add an additional layer of protection for sensitive data by minimizing the risk of exposing individual data points.