AAISM Practice Test 16 — 15 Questions

Question 1

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?

Choose an answer to begin — your selection is scored in the full session.

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

Answer

SageMaker Experiments

Question 2

Which THREE strategies are effective for defending against model poisoning in an ML pipeline?

Accepted Answer

Implementing model validation against a clean, golden dataset. Defending against poisoning requires cleaning the training data and using robust learning algorithms.

Answer

Increasing the model's learning rate to 'burn off' poison

Answer

Regularly changing the model's activation functions

Question 3

In the context of 'Adversarial Machine Learning', what is a 'Blind Spot' attack?

Accepted Answer

Exploiting low-confidence regions to force incorrect predictions. A blind spot attack exploits areas in the input space where the model's confidence is high, but its performance is actually incorrect due to poor training coverage.

Answer

Bypassing authentication on the server

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Increasing the training time of the model

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Deleting the model's weights from memory

Question 4

When conducting a risk assessment for an AI system, which TWO of the following are considered 'AI System' components that require individual threat assessment?

Accepted Answer

The training and inference data pipelines.. NIST and ISO standards suggest assessing both the data pipeline and the model environment (inference and training).

Answer

The physical office building security.

Answer

The personal devices of the developers.

Answer

The internal organizational culture.

Question 5

During an adversarial training cycle, you notice the model's accuracy on the validation set drops significantly despite high training accuracy. Which technique should you apply to mitigate potential model poisoning?

Accepted Answer

Implement Differential Privacy (DP-SGD). Differential privacy helps ensure that the model does not memorize specific training data samples, which is a common target for poisoning attacks aimed at steering model output.

Answer

Increase the learning rate

Answer

Enable Model Versioning in MLflow

Answer

Switch to a pre-trained Transformer architecture

Question 6

You are managing a PyTorch model and suspect data poisoning during the training phase. Which practice is the most effective technical control to verify training data integrity in an MLOps pipeline?

Accepted Answer

Implementing 'Data Lineage' with checksum validation. Data lineage and cryptographic hashing ensure that the training dataset has not been altered maliciously.

Answer

Increasing model quantization

Answer

Reducing the number of training epochs

Answer

Enabling 'Multi-Factor Authentication' on the training cluster

Question 7

Which THREE of the following are potential indicators of a 'Model Poisoning' attack occurring during the training phase?

Accepted Answer

Inconsistent model convergence during training. Poisoning often results in degraded accuracy for specific targets (backdoors) or broader performance degradation that is detectable through validation testing.

Answer

Increased latency for API requests

Answer

Increase in storage costs for the model weights

Question 8

Which of the following is a 'Control' for mitigating the risk of 'Prompt Injection'?

Accepted Answer

Sandboxed execution environment for agents. A 'Sandboxed Execution Environment' for agents prevents the AI from accessing the host file system or network, limiting the impact of an injection.

Answer

Using a larger language model

Answer

Providing the agent with full root access

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Disabling output validation

Question 9

Which TWO actions should be taken to enforce Least Privilege access for developers interacting with SageMaker Model Monitor?

Accepted Answer

Restrict access to the 'sagemaker:CreateModelMonitor' action. Access to Model Monitor requires permissions to both the infrastructure (S3) and the specific model monitoring job definitions.

Answer

Allow all 'iam:PassRole' actions

Answer

Assign the 'SageMakerFullAccess' managed policy

Answer

Enable root access to the underlying EC2 instance

Question 10

You are configuring AWS Bedrock Guardrails to prevent the model from returning PII. Which specific configuration setting should be enabled to ensure PII filtering is applied to both prompt and response?

Accepted Answer

Enable PII filtering in the Policy Settings for both 'input' and 'output' content flows.. AWS Bedrock Guardrails allow for PII entity redaction or blocking; the configuration must be set to filter both incoming prompts and outgoing model responses to ensure comprehensive protection.

Answer

Use the prompt engineering instruction 'Do not output PII' in the System Prompt field.

Answer

Set the content moderation filter level to 'High' in the model configuration.

Answer

Apply an IAM policy to the Bedrock execution role restricting access to PII-containing datasets.

Question 11

You are auditing access controls for a Microsoft Azure OpenAI deployment. Which TWO of the following configurations are required to ensure identity-based access and logging for model inference?

Accepted Answer

Enable 'Diagnostic settings' to route logs to a Log Analytics workspace.. Azure OpenAI requires Entra ID (Azure AD) for authentication and Azure Monitor/Log Analytics for auditing request/response patterns.

Answer

Enable the 'Prompt Injection' toggle in the Azure Portal global settings.

Answer

Create a shared API key for all users to simplify service account management.

Answer

Set the model to 'Public Access' in the Networking tab for easier debugging.

Question 12

Your organization uses Azure OpenAI. You need to ensure that no sensitive data (e.g., credit card numbers) is sent to the model. Which specific service feature should be enabled to intercept and mask this data?

Accepted Answer

Azure OpenAI Content Safety. Azure OpenAI Content Safety provides text filtering and PII masking.

Answer

Azure Active Directory

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Azure Monitor

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Azure Key Vault

Question 13

In the context of poisoning attacks, which technique is most effective for detecting data contamination in an automated ML training pipeline?

Accepted Answer

Statistical outlier detection on training feature sets. Outlier detection (or anomaly detection) is the primary mechanism to identify malicious, poisoned data points that deviate from the expected distribution of the training set.

Answer

Increasing training epochs

Answer

Differential Privacy

Answer

Model quantization

Question 14

Which cryptographic tool is used to ensure the authenticity of a model file stored in an S3 bucket?

Accepted Answer

Digital signatures. Digital signatures (using tools like Cosign or AWS KMS) verify that the model has not been tampered with since being signed.

Answer

Data masking

Answer

Model quantization

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Base64 encoding

Question 15

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

Answer

Providing all intermediate model outputs