AAISM AI Risk Management Practice Test 3 — 15 Questions

Question 1

When evaluating an AI model for production, what does the 'F1 Score' tell you from a risk management perspective?

Accepted Answer

The balance between model precision and recall.. The F1 score is the harmonic mean of precision and recall, providing a single metric that balances false positives and false negatives. From a risk management perspective, it helps assess whether a model is over-flagging (low precision) or missing critical cases (low recall), which directly maps to operational and compliance risk.

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The latency of the model in milliseconds.

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The total number of parameters in the model.

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The total cost of training the model.

Question 2

What is the primary risk associated with 'Overfitting' in an AI model?

Accepted Answer

Lack of generalization to unseen data.. Overfitting occurs when a model learns the training data too precisely, including its noise and idiosyncrasies, so it performs excellently on training data but poorly on new, unseen data. The primary risk is therefore a lack of generalization, meaning the model's predictions do not hold up in production where inputs differ from the training set.

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Increased latency in inference.

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The model is too simple to solve the problem.

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The model is too large to be deployed.

Question 3

You are assessing the risk of 'Data Poisoning'. Which stage of the machine learning lifecycle is most vulnerable to this attack?

Accepted Answer

Training data collection and ingestion.. Data poisoning attacks work by corrupting the data a model learns from, so the training data collection and ingestion stage is where an adversary can inject malicious, mislabeled, or biased samples that the model will internalize. Once poisoned data is ingested, the resulting model weights encode the attacker's influence, making this the earliest and most impactful point of compromise. Later stages like inference or monitoring cannot retroactively undo poisoned training data.

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The model design and architecture selection.

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Model monitoring and logging.

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Inference and deployment.

Question 4

What is the purpose of 'AI Model Transparency' in a risk management framework?

Accepted Answer

To enable stakeholders to evaluate model safety and performance.. AI Model Transparency enables stakeholders — auditors, regulators, business owners, and affected users — to understand how a model behaves, what data it uses, and how it performs, so they can evaluate its safety, fairness, and fitness for purpose. It is a governance enabler, not a performance optimization.

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To speed up the training process.

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To hide the model architecture from competitors.

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To minimize the number of API calls.

Question 5

A researcher is using 'Adversarial Training' to defend against evasion. How exactly does this work?

Accepted Answer

It trains the model on adversarial examples alongside clean data.. Adversarial training is a defense technique where the model is trained on a dataset that includes both clean examples and adversarial examples—inputs intentionally perturbed to fool the model. By learning from these adversarial examples, the model becomes more robust and can correctly classify them, thereby improving its resistance to evasion attacks.

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It uses a secondary model to detect malicious input.

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It restricts input to a whitelist of authorized users.

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It adds noise to the weights of the model.

Question 6

Which THREE components are critical for an 'AI Incident Response' plan?

Accepted Answer

Communication channels with data scientists and legal teams.. Option A is correct because an AI incident response plan must define communication channels that bring together data scientists (who understand model behavior and can diagnose failures) and legal teams (who handle regulatory, liability, and disclosure obligations), ensuring coordinated and compliant escalation. Option C is correct because procedures for model rollback or disablement are essential to quickly contain harm by reverting to a prior known-good model version or taking the model offline when it misbehaves. Option D is correct because identifying model-specific failure indicators — such as data drift, adversarial inputs, bias spikes, or anomalous confidence scores — enables early detection and triage of AI incidents that differ from traditional IT failures. Option B does not belong because publishing all internal vulnerabilities publicly is not a standard incident response requirement and could increase risk and violate disclosure norms. Option E does not belong because automated deletion of all logs destroys the forensic evidence needed for incident investigation, root-cause analysis, and compliance reporting.

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Requirements to publish all internal vulnerabilities publicly.

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Automated deletion of all logs.

Question 7

Which TWO factors contribute to 'Model Obsolescence' risk?

Accepted Answer

Availability of superior, more efficient alternatives.. Model obsolescence risk arises when a deployed model no longer delivers optimal value relative to what is available or what the environment demands. Option A is correct because the emergence of superior, more efficient alternatives (e.g., newer architectures, better-performing pretrained models, or more cost-effective algorithms) can render an existing model outdated even if it still functions correctly. Option C is correct because significant changes in production data distribution — known as data drift or concept drift — cause the model's learned patterns to no longer match real-world inputs, degrading performance and effectively making the model obsolete for its original purpose. Option B is incorrect because reaching a maximum iteration count is a training hyperparameter/stopping condition, not a cause of obsolescence; the model simply stops training. Option D is incorrect because encrypting training data is a security measure and has no bearing on whether a model becomes obsolete. Option E is incorrect because storing a model on a local server is a deployment/hosting choice and does not by itself contribute to obsolescence risk.

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The model has reached its maximum iteration count.

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The training data was encrypted.

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The model is stored on a local server.

Question 8

When assessing the risk of 'Prompt Leaking', what is the most important control to implement?

Accepted Answer

Strict output filtering and instruction separation.. Prompt leaking occurs when an attacker extracts the system prompt or confidential instructions embedded in an LLM application. The most effective control is strict output filtering combined with instruction separation — keeping system instructions isolated from user input and filtering responses that would reveal them. This directly addresses the exfiltration channel rather than the model's size, transport, or network location.

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Increasing the model's parameter count.

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Encrypting the model output.

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Only allowing access from internal IP addresses.

Question 9

What is the key risk difference between 'Data Privacy' and 'Model Security' in AI?

Accepted Answer

Data privacy is about PII protection; model security is about system integrity.. Data privacy focuses on protecting personally identifiable information (PII) throughout collection, processing, and storage, addressing legal and ethical obligations to individuals. Model security, by contrast, focuses on protecting the integrity, availability, and behavior of the AI model itself—against tampering, poisoning, evasion, or theft. The key risk difference is the asset protected: personal data versus the model as a system artifact.

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Data privacy is only for training data, not inference data.

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There is no difference.

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Model security is only about physical hardware.

Question 10

An organization is evaluating 'Third-Party AI Models'. Which risk is most effectively addressed by a 'Model Evaluation Report'?

Accepted Answer

The risk of model unsuitability or unexpected behavior.. A Model Evaluation Report documents how a third-party AI model was tested for accuracy, robustness, bias, fairness, and behavior under edge cases, directly addressing the risk that the model is unsuitable for the intended use case or behaves unexpectedly in production. It provides evidence that the model's performance and limitations are understood before deployment, which is the core purpose of model evaluation in AI risk management.

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The risk of the vendor going bankrupt.

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The risk of poor UI/UX design.

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The risk of high API latency.

Question 11

Which THREE technical configurations help mitigate the risk of 'Model Exfiltration'?

Accepted Answer

Strict IAM policies limiting access to the model weight files.. Option B is correct because strict IAM policies that limit access to model weight files enforce least-privilege authorization, preventing unauthorized principals from reading or copying the stored weights that constitute the model itself. Option C is correct because API response truncation or fuzzing reduces the information returned by inference endpoints, making it harder for an attacker to reconstruct or extract internal weights through repeated querying (a model-stealing/extraction attack). Option D is correct because egress traffic filtering from the model inference environment blocks outbound connections, stopping an attacker or compromised workload from exfiltrating model artifacts or weights to external destinations. Option A is not correct because increasing the number of public endpoints expands the attack surface and makes exfiltration easier, not harder. Option E is not correct because clear-text logging of model data exposes sensitive information and increases the risk of leakage rather than mitigating exfiltration.

Answer

Increasing the number of public endpoints.

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Using clear text for all model logs.

Question 12

You are auditing an AI system for 'Model Explainability' (XAI). Why is XAI critical from a risk management perspective?

Accepted Answer

It provides transparency for accountability and bias detection.. Explainable AI (XAI) matters for risk management because it makes model decisions transparent and interpretable, which is essential for accountability (who is responsible and why a decision was made) and for detecting bias, discrimination, and unsafe behavior. Regulators and auditors increasingly require explanations for automated decisions affecting individuals, so XAI is a governance control rather than a performance optimization.

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It is required to make the model run faster.

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It encrypts the input data.

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It enables models to train without labels.

Question 13

What is the primary role of a 'Human-in-the-loop' (HITL) in AI risk management?

Accepted Answer

To provide oversight and intervention for critical model decisions.. Human-in-the-loop (HITL) in AI risk management ensures that a human reviews, approves, or overrides critical model decisions, providing oversight and accountability. This is essential for high-stakes domains such as healthcare, finance, and autonomous systems, where erroneous AI outputs can cause harm. HITL mitigates risks like bias, hallucination, and opaque decision-making by inserting human judgment at key decision points.

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To write the model code.

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To speed up the inference process.

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To handle technical server maintenance.

Question 14

Which TWO security controls are recommended for protecting 'Training Data'?

Accepted Answer

Encryption at rest for all dataset storage.. Option D is correct because encryption at rest protects training datasets on disk, object storage, or backups from unauthorized disclosure if the underlying storage media or snapshots are compromised, using mechanisms such as AES-256 or cloud KMS-managed keys. Option E is correct because strict role-based access control (RBAC) limits who can read, modify, or export training data, enforcing least privilege and reducing insider or accidental exposure through defined roles and permissions. Together these controls address both data confidentiality at the storage layer and authorized access at the identity layer, which are core recommendations for protecting sensitive training data. Option A is not appropriate because publicly hosting data on GitHub exposes it to anyone and destroys confidentiality, even if reproducibility is a goal. Option B is wrong because embedding passwords in training data creates a severe credential leak and violates basic secret-handling practices. Option C is irrelevant because image resolution has no bearing on the security of training data.

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Publicly hosting the data on GitHub for reproducibility.

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Storing all passwords in the training data.

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Using only high-resolution images.

Question 15

What is the purpose of 'AI Model Inventory'?

Accepted Answer

To provide a central list of all AI assets for risk oversight.. An AI Model Inventory is a centralized, governed catalog of all AI/ML models in an organization, capturing metadata such as owner, purpose, training data sources, version, risk classification, and deployment status. Its primary purpose is to enable risk oversight, compliance reporting, and lifecycle governance by giving stakeholders a single source of truth for what AI assets exist and how they are managed. This supports audit, incident response, and regulatory inquiries.

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To increase the speed of the training pipeline.

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To reduce the number of GPUs used.

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To automatically retrain all models.