AAISM Practice Test 10 — 10 Questions

Question 1

What is a 'Side-Channel Attack' against an AI model?

Accepted Answer

An attack that exploits timing or power consumption patterns.. Side-channel attacks exploit information leaked through indirect channels like power consumption, timing, or memory access patterns to infer sensitive model information.

Answer

An attack that uses a phishing email.

Answer

An attack that uses a VPN.

Answer

An attack that involves physical tampering.

Question 2

Which TWO techniques are effective for detecting and mitigating 'Model Inversion' attacks where an adversary attempts to reconstruct training data from model outputs?

Accepted Answer

Rate limiting and monitoring query frequency. Model inversion is mitigated by reducing the information content of model outputs and limiting query volume.

Answer

Disabling all model API documentation

Answer

Increasing the precision of model output scores

Answer

Using larger batch sizes during inference

Question 3

Which control is most effective against 'Data Poisoning' in a collaborative learning environment?

Accepted Answer

Using robust statistical aggregation algorithms.. By using robust statistical aggregation techniques, the aggregator can filter out malicious/outlier updates that could poison the model.

Answer

Changing the model to a non-collaborative one.

Answer

Removing all participants from the collaboration.

Answer

Using a faster internet connection.

Question 4

You are reviewing logs for an AI service. You notice a high frequency of '429 Too Many Requests' errors. Which security control is likely being triggered to protect the model from potential automated extraction or DoS?

Accepted Answer

API Rate Limiting. Rate limiting is a standard control to prevent resource exhaustion and model scraping.

Answer

Model Drift Monitoring

Answer

Data Encryption at Rest

Answer

Input Sanitization

Question 5

You need to ensure that a model deployed on AWS ECS is only accessible by authorized microservices. What is the most appropriate network security control?

Accepted Answer

Use AWS Security Groups. Security Groups provide stateful packet filtering for ECS containers, restricting who can communicate with the model endpoint.

Answer

Increase the container memory limit

Answer

Enable AWS WAF for all internal traffic

Answer

Use public routing for all endpoints

Question 6

When training an AI model, why is it critical to ensure 'Data Provenance'?

Accepted Answer

To verify the source and integrity of training data. Data provenance allows auditors to trace the origin of data, ensuring no poisoned or copyrighted data entered the training pipeline.

Answer

To reduce the storage space of datasets

Answer

To automatically remove PII from datasets

Answer

To optimize the training speed of the model

Question 7

You are managing a model's lifecycle in SageMaker. How do you ensure that only approved model versions are deployed to production?

Accepted Answer

Use 'SageMaker Model Registry' approval workflows. The SageMaker Model Registry allows for version tracking, metadata management, and approval states to control deployment.

Answer

Use manual file copying to the production server

Answer

Delete all non-production model versions

Answer

Set every model version to 'Production' by default

Question 8

When reviewing an AI architecture, what does the 'Attack Surface' refer to?

Accepted Answer

The set of points where an attacker can interact with the system. The attack surface includes all points where an adversary can enter or extract data from the system, including API endpoints and training interfaces.

Answer

The speed of the model's inference

Answer

The number of users allowed to access the system

Answer

The memory usage of the model during training

Question 9

You detect an unusual spike in API calls that are clearly non-human. What is the most effective way to address this without completely blocking the service?

Accepted Answer

Implement a bot detection challenge (e.g., CAPTCHA). Implementing a CAPTCHA or sophisticated WAF challenge for high-volume users is a standard way to differentiate human traffic from bots.

Answer

Increase the model latency

Answer

Publicly release the model weights

Answer

Shutdown the entire API service

Question 10

You are using the Adversarial Robustness Toolbox (ART) to assess a classifier. Which attack technique would you simulate to test the model's vulnerability to 'Evasion' attacks?

Accepted Answer

Fast Gradient Sign Method (FGSM). The Fast Gradient Sign Method (FGSM) is a classic white-box attack technique used to test evasion vulnerability by introducing imperceptible noise.

Answer

Membership Inference

Answer

Model Inversion

Answer

Data Poisoning via label flipping