AAISM Practice Test 1 — 10 Questions

Question 1

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.

Answer

Requirements to publish all internal vulnerabilities publicly.

Answer

Automated deletion of all logs.

Question 2

What is 'Model Stealing' and why is it considered a security incident?

Accepted Answer

It is the theft of intellectual property via query analysis. Model stealing is the extraction of a model's functionality or intellectual property through repeated query analysis — an attacker probes the API with crafted inputs and uses the input-output pairs to train a surrogate model that approximates the original. It is a security incident because it compromises the owner's IP and can enable further attacks such as adversarial example transfer and evasion of the original model. The economic value of a trained model is directly eroded when it can be cloned via the inference API.

Answer

It is the slow down of the model due to heavy load

Answer

It is the unauthorized access to the server room

Answer

It is the accidental deletion of model files

Question 3

A technology company has developed an AI model for autonomous decision-making in its supply chain. The AI governance team is tasked with establishing a process for continuous monitoring of the model's performance and risk. Which of the following is the MOST critical element to include in the monitoring process to detect model drift?

Accepted Answer

Tracking changes in the statistical distribution of input data over time.. Tracking changes in input data distributions is the most critical element for detecting model drift because it directly identifies covariate shift, a primary cause of performance degradation. Other options focus on usage metrics, static historical performance, or code management, none of which provide early warning of drift in a live supply chain environment.

Answer

Ensuring the model's code repository is version-controlled and changes are logged.

Answer

Recording the number of API calls made to the model each day.

Answer

Reviewing the model's training loss and accuracy metrics on the original test set.

Question 4

When a model fails in production, what is the first step in the 'Incident Response' process?

Accepted Answer

Containment and impact assessment.. The first step in incident response, including AI model failures, is containment and impact assessment. Containment stops the failure from spreading or causing further harm (e.g., disabling the model endpoint, reverting to a safe version), while impact assessment determines the scope, affected users, and business damage. This aligns with standard IR frameworks like NIST SP 800-61, where containment follows detection and precedes eradication and recovery.

Answer

Writing a press release.

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Firing the developer.

Answer

Ignoring the error.

Question 5

An AI Security analyst is configuring Google Cloud Vertex AI Model Garden to ensure compliance with organization policies. Which IAM permission is strictly required to prevent unauthorized users from deploying a base model to a public endpoint?

Accepted Answer

aiplatform.endpoints.create. Deploying a model to a Vertex AI endpoint requires creating an endpoint resource, which is governed by the aiplatform.endpoints.create permission. Without this permission, a user cannot provision a public endpoint even if they can read metadata or upload models. Restricting this permission to authorized principals is the direct IAM control that prevents unauthorized public exposure of a base model.

Answer

aiplatform.metadata.read

Answer

aiplatform.jobs.create

Answer

aiplatform.models.upload

Question 6

Which TWO of the following are effective methods for monitoring and detecting AI security incidents in a cloud environment?

Accepted Answer

Ingesting model inference logs into a SIEM. Option C is correct because ingesting model inference logs into a SIEM centralizes AI telemetry, enabling correlation of prompts, responses, and metadata with other security events to detect prompt injection, data exfiltration, or model abuse in near real time. Option E is correct because configuring anomaly detection for API call patterns baselines normal invocation frequency, latency, token usage, and source IPs, then alerts on deviations that indicate credential theft, model extraction, or denial-of-wallet attacks against AI endpoints. Option A is wrong because disabling logging removes the audit trail needed for detection and forensics, and cost reduction is not a security monitoring method. Option B is wrong because standard database backup schedules address data durability and recovery, not real-time detection of AI security incidents. Option D is wrong because hardcoding API credentials in application code exposes secrets and increases compromise risk rather than helping monitor or detect incidents.

Answer

Disabling logging to reduce cloud spend

Answer

Using standard database backup schedules

Answer

Hardcoding API credentials in application code

Question 7

What is the primary function of 'IAM' in an AI-driven cloud environment?

Accepted Answer

Manage identities and access to resources. Identity and Access Management (IAM) in an AI-driven cloud environment governs who (users, services, applications) can access which resources (models, datasets, compute, APIs) and under what conditions. Its primary function is authentication, authorization, and policy enforcement across the cloud environment, ensuring least-privilege access to AI assets.

Answer

Monitor the health of GPU clusters

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Automate data cleaning tasks

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Increase the training speed of models

Question 8

When conducting an AI audit, what should be the primary focus of the 'Data Governance' review?

Accepted Answer

Data quality, lineage, and access controls.. The primary focus of a Data Governance review in an AI audit is to ensure that the data used for training, validation, and inference is reliable, traceable, and properly protected. Data quality ensures the model's outputs are accurate and unbiased; lineage provides an audit trail from source to model; and access controls enforce security and privacy. These elements are foundational to trustworthy AI systems and are explicitly covered in governance frameworks like the NIST AI RMF and ISO/IEC 42001.

Answer

The model's marketing reach.

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The color of the UI.

Answer

The model creator's salary.

Question 9

A multinational bank's AI governance committee is drafting its charter. The General Counsel insists that the charter explicitly state who bears ultimate responsibility for the organization's AI-related legal and regulatory outcomes, and that this be documented and approved at the highest level. Which element of the AI governance charter is the General Counsel requiring?

Accepted Answer

A defined AI accountability structure that assigns ultimate ownership of AI outcomes to a named executive body. The General Counsel is asking for governance ownership, not operational controls. An AI governance charter must name the executive or board-level body that ultimately owns AI outcomes, including legal and regulatory consequences. Intake processes, monitoring standards, and control baselines support that ownership but do not establish it.

Answer

A model performance monitoring standard defining drift thresholds and retraining triggers

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A technical control baseline specifying minimum encryption and access controls for AI model endpoints

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An AI use-case intake process requiring business units to register proposed AI tools before deployment

Question 10

You are securing a PyTorch model and want to implement differential privacy to protect the training data. Which library is specifically designed to work with PyTorch for this purpose?

Accepted Answer

Opacus. Opacus is a library developed by Meta specifically for training PyTorch models with differential privacy. It provides a PrivacyEngine that wraps standard PyTorch optimizers and DataLoaders to add per-sample gradient clipping and calibrated Gaussian noise, implementing the DP-SGD algorithm natively within the PyTorch autograd graph. This tight integration means you can make an existing PyTorch training loop differentially private with only a few lines of code.

Answer

TensorFlow Privacy

Answer

Hugging Face Evaluate

Answer

Adversarial Robustness Toolbox (ART)