AAISM Practice Test 10 — 10 Questions

Question 1

You are implementing 'AI Red Teaming'. What is the most effective way to test against prompt injection?

Accepted Answer

Execute a series of iterative adversarial prompt attempts against the production model.. Executing iterative adversarial prompt attempts against the production model is the most effective way to test for prompt injection because it directly exercises the attack surface — the model's input handling — under real conditions. Prompt injection is an input-layer vulnerability, so the only reliable test is to attempt injections and observe whether the model follows injected instructions, leaks system prompts, or bypasses guardrails. Iterative testing (red teaming) allows testers to refine payloads based on model responses.

Answer

Check for compliance with data privacy laws.

Answer

Review the training data logs for errors.

Answer

Scan the code repository for hardcoded secrets.

Question 2

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

Accepted Answer

Containment of the affected AI model. Option E is correct because identification and classification of the AI incident is the essential first phase of any incident response plan, establishing what happened, its severity, and the appropriate response track before other actions are taken. Option A is correct because containment of the affected AI model limits blast radius by isolating, quarantining, or disabling the model (e.g., revoking API keys, taking the endpoint offline, or rolling back to a known-good version) so the incident does not spread or cause further harm. Option C is correct because eradication of the threat source removes the root cause — such as poisoned training data, a compromised model artifact, a malicious prompt-injection vector, or an exploited vulnerability — so the incident cannot recur after recovery. Option B is not a response step; increasing inference speed is a performance-tuning activity unrelated to incident handling. Option D is not a standard response step; wholesale hardware replacement is a disproportionate, costly action that is not part of a typical AI incident response plan.

Answer

Increasing the model's inference speed

Answer

Replacing all hardware in the data center

Question 3

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.

Answer

The model has reached its maximum iteration count.

Answer

The training data was encrypted.

Answer

The model is stored on a local server.

Question 4

What is 'AI Shadowing' or 'Shadow AI' in an enterprise setting?

Accepted Answer

Employees using unapproved AI tools for work tasks.. Shadow AI refers to employees using unapproved AI tools — such as public LLM chatbots, browser extensions, or SaaS AI features — for work tasks without IT or security oversight. This creates data leakage, compliance, and intellectual property risks because sensitive information may be sent to third-party services outside the organization's governance.

Answer

The practice of monitoring AI usage in real-time.

Answer

The process of testing models before release.

Answer

The use of AI for internal cybersecurity tasks only.

Question 5

Which document should define the organization's stance on AI transparency and explainability?

Accepted Answer

The Corporate AI Policy.. The Corporate AI Policy is the governing document that defines the organization's principles, commitments, and requirements for AI transparency and explainability. It establishes the official stance that all AI systems must be documented, explainable to relevant stakeholders, and subject to disclosure rules. Other documents listed are either operational artifacts or external marketing materials that do not carry governance authority.

Answer

The employee breakroom notice.

Answer

The server configuration log.

Answer

The marketing brochure.

Question 6

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.

Answer

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 7

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 establishes a verifiable record of where training data originated, who handled it, and whether it was altered — this is foundational for trustworthiness, regulatory compliance, and detecting poisoned or tampered datasets. Without provenance, you cannot attest to model behavior, reproduce training runs, or defend against supply-chain attacks on the data pipeline.

Answer

To reduce the storage space of datasets

Answer

To automatically remove PII from datasets

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To optimize the training speed of the model

Question 8

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. SageMaker Model Registry provides versioned model groups with approval statuses (PendingManualApproval, Approved, Rejected), and deployment pipelines can be gated to only deploy models in the Approved state. This creates an auditable, policy-enforced promotion path from training to production.

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 9

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

Accepted Answer

Using robust statistical aggregation algorithms.. Robust statistical aggregation algorithms (such as trimmed mean, median, or Krum) detect and down-weight malicious updates from poisoned participants in collaborative/federated learning. Because poisoning manifests as outlier model updates, aggregation that is resistant to outliers neutralizes the attack. This is the most effective control because it operates at the point where poisoned contributions would otherwise be incorporated.

Answer

Changing the model to a non-collaborative one.

Answer

Removing all participants from the collaboration.

Answer

Using a faster internet connection.

Question 10

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). A CAPTCHA or similar bot detection challenge is the most effective way to mitigate non-human API traffic without blocking legitimate users. It presents a challenge that is easy for humans but difficult for automated scripts, thereby filtering out bots while allowing genuine users to continue accessing the service. This approach is targeted and minimally disruptive, aligning with the goal of not completely blocking the service.

Answer

Increase the model latency

Answer

Publicly release the model weights

Answer

Shutdown the entire API service