AAIA Practice Test 13 — 15 Questions

Question 1

During an AI risk assessment, you notice that the 'Model Risk Management' (MRM) framework does not mandate independent validation for models categorized as 'High Impact'. What is the most significant consequence?

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

Accepted Answer

Increased probability of undetected systemic model bias or failure. Independent validation is a core pillar of model risk governance, especially for high-impact systems.

Answer

Inability to detect model drift in production environments

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Non-compliance with internal cybersecurity patching cycles

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Operational silos between the AI team and the IT department

Question 2

Which TWO are common 'AI Security' threats that require operational oversight?

Accepted Answer

Model poisoning during training. Model poisoning during training (C) is a core AI security threat because an adversary who can influence the training data or pipeline can inject malicious samples that corrupt the learned model, degrading accuracy or implanting backdoors, so it demands operational oversight of data provenance, training integrity, and model validation. Adversarial evasion attacks (D) are also a common AI security threat: crafted inputs at inference time (e.g., perturbed images or prompts) cause the deployed model to misclassify or bypass controls, requiring ongoing monitoring, input filtering, and robustness testing in production. By contrast, slow training time (A) and high memory usage (B) are performance and resource-efficiency concerns, not security threats, and team burnout (E) is a personnel/wellbeing issue rather than an AI security risk.

Answer

Slow training time

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High memory usage

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Team burnout

Question 3

Which THREE actions are appropriate when you find a significant 'Bias' finding in an AI audit?

Accepted Answer

Recommend retraining with re-sampled data. Option B is correct because retraining with re-sampled data directly addresses the root cause of bias by correcting class imbalance or underrepresentation in the training set, which is the standard remediation for a significant bias finding. Option C is correct because a significant bias finding represents a material risk to the organization, so management must be notified to enable informed decision-making and risk mitigation. Option D is correct because audit findings must be documented in the audit report to maintain an accurate, traceable record of the issue, its severity, and recommended actions. Option A is wrong because model speed is irrelevant to bias; ignoring a significant finding violates audit and governance principles. Option E is wrong because deleting audit evidence destroys the audit trail and is unethical and likely non-compliant with audit standards.

Answer

Ignore the finding if the model is fast

Answer

Delete the audit evidence

Question 4

When auditing an AI system for regulatory compliance, you find that data lineage is broken between the feature store and the training pipeline. Which control is most likely deficient?

Accepted Answer

Metadata logging and transformation documentation. Feature stores must maintain end-to-end traceability to ensure that data provenance is clear and auditable.

Answer

Access control lists for the model production environment

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Encryption of data at rest in the data warehouse

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Load balancing configuration for the inference API

Question 5

What is the purpose of an 'AI Policy' within an organization?

Accepted Answer

To provide a structured set of guidelines for the responsible development and use of AI. An AI policy sets the standard for responsible, secure, and compliant AI usage across the enterprise.

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To list the office coffee machine settings

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To define the company's dress code policy

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To describe the history of computer hardware

Question 6

When auditing a model deployment pipeline, which TWO aspects are critical to verify to ensure compliance with AI governance frameworks?

Accepted Answer

Validation of data processing against privacy policies. Option B is correct because AI governance frameworks require that data processing be validated against privacy policies, ensuring that personal data is collected, processed, and stored in line with regulations such as GDPR or CCPA and internal data-handling standards. Option C is correct because a complete audit trail of model lineage provides traceability from training data through model versions to deployment, which is essential for accountability, reproducibility, and regulatory audits under governance frameworks. The unmarked options do not belong: the choice of cloud provider (A) is a deployment detail, not a governance requirement; training job speed (D) is a performance metric unrelated to compliance; and using only proprietary algorithms (E) is neither required nor sufficient for AI governance compliance.

Answer

Whether the team uses a specific cloud provider

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The speed of the training job

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Usage of proprietary algorithms only

Question 7

When auditing model deployment, what is the primary purpose of 'Shadow Mode' testing?

Accepted Answer

To compare model performance against production traffic without impacting users. Shadow mode allows running the new model in production alongside the old one to compare outputs without affecting users.

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To encrypt model weights in memory

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To hide model artifacts from unauthorized users

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To automate hyperparameter optimization

Question 8

What is the primary function of a 'Version Control System' (like Git) in an MLOps team?

Accepted Answer

Managing code changes. To manage code changes, enable collaboration, and allow rollbacks.

Answer

Executing Python scripts

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Storing raw video

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Managing databases

Question 9

Which TWO of the following are primary benefits of implementing a Model Registry in an AI production environment?

Accepted Answer

Enables versioning and lifecycle management of models. Model registries ensure version control and facilitate collaboration.

Answer

Allows for direct modification of training data

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Increases the hardware utilization of GPUs

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Automatically deletes old training instances

Question 10

When an AI system utilizes a 'Feature Store', how does it ensure consistency between training and inference?

Accepted Answer

Serving features from a unified source. The feature store ensures the exact same logic and code are used to fetch features for both training and serving.

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Deleting training data after use

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Caching features on local disk

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Converting features to text

Question 11

What is the primary role of a 'Human-in-the-Loop' (HITL) audit requirement?

Accepted Answer

To review critical AI decisions. HITL ensures that sensitive or critical decisions made by AI are reviewed by human experts.

Answer

To automate model retraining

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To generate marketing reports

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To improve GPU performance

Question 12

Which TWO of the following are essential when performing an 'AI Risk Assessment'?

Accepted Answer

Assessing the likelihood and severity of identified risks. An assessment must identify both technical risks and potential impacts on stakeholders.

Answer

Listing the names of the team members' families

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Choosing the best coffee brand for the office

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Selecting the color scheme for the model's dashboard

Question 13

What is the primary function of an 'AI Ethics Board'?

Accepted Answer

Reviewing AI projects for alignment with corporate values and societal norms. The ethics board ensures that AI projects align with corporate values and societal standards.

Answer

Managing the HR payroll systems

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Fixing bugs in the server infrastructure

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Writing the code for all production models

Question 14

A company's AI governance policy requires 'Model explainability'. Which tool or technique best satisfies this requirement for complex deep learning models?

Accepted Answer

Using SHAP (SHapley Additive exPlanations) to interpret model features. SHAP (SHapley Additive exPlanations) or LIME are industry-standard techniques for explaining deep learning decisions.

Answer

Asking the model's developer to write a summary of their feelings about the model

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Printing the model's architecture as a JPEG file

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Reducing the number of parameters in the model to one

Question 15

What is the primary function of a 'Canary Deployment' in AI model release?

Accepted Answer

Gradual rollout to test stability. Releasing the new model to a small subset of traffic to monitor performance before full rollout.

Answer

To update documentation

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To delete old models

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To increase training performance

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To encrypt model weights