AAISM AI Technologies And Controls Practice Test 4 — 15 Questions

Question 1

What is the goal of 'Data Sanitization' before training?

Accepted Answer

Remove PII and malicious data from the training set. Data sanitization removes sensitive information (PII/PHI) and malicious patterns, ensuring that the model does not learn or leak private info.

Answer

Change the file format of the dataset

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Reduce the cloud storage costs

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Increase the number of images in the dataset

Question 2

Which THREE of the following are examples of AI system 'Access Controls'?

Accepted Answer

API Key authentication. Access controls include authentication (IAM), network restrictions (VPC/Security Groups), and fine-grained permissions for API operations.

Answer

Disabling logging services

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Increasing model training epochs

Question 3

Which tool is best for managing 'Secrets' (like database passwords or API keys) used by your AI training pipeline?

Accepted Answer

HashiCorp Vault. Secrets management services (AWS Secrets Manager, HashiCorp Vault) are the secure way to manage credentials.

Answer

Hardcoded variables in the training script

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A public Github repository

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An Excel spreadsheet on a shared drive

Question 4

In the context of 'Model Poisoning', what is a 'Backdoor' attack?

Accepted Answer

Embedded trigger causing specific model behavior. A backdoor is a trigger (a specific pattern) embedded into the model during training so that it behaves normally until the trigger is present, at which point it performs a malicious action.

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Deleting the model's training documentation

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Increasing the model's complexity

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Bypassing authentication servers

Question 5

Which THREE of the following are common steps in an 'AI Incident Response Plan'?

Accepted Answer

Containment of the affected AI model. Incident response involves identification (detection), containment (isolation), and eradication/recovery, followed by post-incident learning.

Answer

Increasing the model's inference speed

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Replacing all hardware in the data center

Question 6

What is the primary role of 'Data Encryption at Rest' for AI datasets?

Accepted Answer

Protect data from unauthorized access on storage media. Encryption at rest ensures that even if the storage media is stolen or accessed unauthorized, the data remains unreadable.

Answer

Increase the training speed of the model

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Detect potential poisoning attacks

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Automatically patch model software

Question 7

You are configuring a 'Firewall' for an AI API. Which rule should you prioritize to prevent unauthorized exfiltration of model training data?

Accepted Answer

Configure strict egress (outbound) traffic rules. Egress filtering (outbound rules) ensures that the model environment cannot communicate with unauthorized external servers, preventing data exfiltration.

Answer

Disable all firewall logging

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Increase the number of API endpoints

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Allow all outbound traffic to the internet

Question 8

What is the 'Membership Inference' attack in machine learning?

Accepted Answer

Determining if a specific record was in the training set. This attack determines whether a specific data point was included in the training set, potentially compromising privacy if the data is sensitive.

Answer

Brute forcing the user password

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Adding noise to the output

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Stealing the model's entire weights file

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

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Publicly release the model weights

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Shutdown the entire API service

Question 10

Why is it important to perform 'Model Validation' before production deployment?

Accepted Answer

To verify performance and safety against benchmarks. Model validation ensures that the model meets performance and safety requirements, minimizing the risk of unexpected behavior.

Answer

To automatically remove users from the system

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To encrypt the model weights

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To reduce the cost of GPU hardware

Question 11

Which THREE of the following are technical controls to prevent 'Model Extraction' attacks?

Accepted Answer

API Rate Limiting. Model extraction is mitigated by rate limiting, output perturbation (noise), and limiting the detail/granularity of the output returned to the API.

Answer

Increasing the model learning rate

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Providing all intermediate model outputs

Question 12

What is the role of 'Homomorphic Encryption' in secure AI? (Theoretical concept)

Accepted Answer

Allows computation on encrypted data. Homomorphic encryption allows computation on encrypted data, enabling a provider to train or run a model on data without ever decrypting it.

Answer

Detects malicious prompts

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Manages API user identities

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Compresses the model size

Question 13

An AI administrator is using Azure AI Content Safety to detect jailbreak attempts in prompt engineering. Which configuration must be enabled to proactively block prompt injection attacks?

Accepted Answer

Configure Prompt Shield. The Prompt Shield feature in Azure AI Content Safety specifically targets user-to-model prompt injection and jailbreak attacks.

Answer

Apply Customer Managed Keys (CMK)

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Enable Protected Material Detection

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Implement Jailbreak Mitigation via Language Detection

Question 14

You are managing a model deployed in AWS SageMaker and observe drift in the ground truth data. Which SageMaker feature should be configured to automatically monitor and alert on this statistical deviation?

Accepted Answer

SageMaker Model Monitor. SageMaker Model Monitor is designed to detect data drift and concept drift by comparing real-time inference data against the baseline training statistics.

Answer

SageMaker Clarify for Bias Detection

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SageMaker Feature Store

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SageMaker Experiments

Question 15

To mitigate adversarial evasion attacks against a vision model, you decide to implement defensive distillation on your TensorFlow model. What is the primary operational trade-off you must evaluate?

Accepted Answer

Reduced model sensitivity to noise and potential drop in clean accuracy. Defensive distillation increases the model's robustness against small adversarial perturbations but can lead to reduced accuracy on clean data or increased training complexity.

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Requirement for GPU-based training clusters only

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Incompatibility with gradient-based optimizers

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Increased latency in inference requests