AAIA Multiple Select — 10 Questions

Question 1

Which TWO of the following are examples of 'Data Leakage' that an auditor should look for in a pipeline?

Accepted Answer

Training on test data. Option B is correct because training on test data is a classic form of data leakage: information from the evaluation set contaminates the training process, so the model's reported performance is optimistically biased and does not reflect true generalization to unseen data. Option E is correct because including target-related features that are not available at inference time (e.g., using the label or a post-outcome variable as an input) leaks future/target information into training, producing a model that cannot be reproduced or used in production. Option A is not leakage per se; using cloud-based storage is an infrastructure choice that may raise other security or compliance concerns but does not itself contaminate training with evaluation or target information. Option C is not leakage; documenting model parameters is a governance and reproducibility practice. Option D is not leakage; using open-source libraries is a normal engineering decision that may introduce supply-chain risk but does not constitute data leakage.

Answer

Using cloud-based storage

Answer

Documenting model parameters

Answer

Using open-source libraries

Question 2

Which TWO are common 'AI Operational' challenges?

Accepted Answer

Data quality management. B (Data quality management) is a core AI operational challenge because production models depend on clean, consistent, and representative training and inference data; poor data quality causes drift, bias, and degraded accuracy that must be continuously monitored and remediated. E (Model maintenance) is also correct because deployed models require ongoing retraining, versioning, performance monitoring, and updates as data distributions and business requirements change over time. The remaining options are not AI operational challenges: A (Selecting a font) is a design/typography decision, C (Writing poetry) is a creative content task rather than an operational concern, and D (Buying new office chairs) is a facilities/procurement matter unrelated to running AI systems.

Answer

Selecting a font

Answer

Writing poetry

Answer

Buying new office chairs

Question 3

Which TWO techniques should an auditor employ to detect bias in a model where the training data has imbalanced demographic representation?

Accepted Answer

Implementation of fairness-aware loss functions. Fairness-aware loss functions (B) are correct because they explicitly incorporate group-fairness constraints or penalties (e.g., equalized odds, demographic parity regularization) into the model's objective during training, directly counteracting bias caused by imbalanced demographic representation. Stratified cross-validation (E) is correct because it preserves the proportional class and demographic distribution in each fold, ensuring that performance and bias metrics are evaluated reliably across underrepresented groups rather than being masked by skewed splits. The remaining options do not belong: updating the corporate logo (A) and changing the font size in the UI (C) are cosmetic and have no effect on model training or bias, while increasing the number of project meetings (D) is a process activity that does not alter data imbalance or model fairness.

Answer

Updating the corporate logo

Answer

Changing the font size in the UI

Answer

Increasing the number of project meetings

Question 4

Which TWO of the following are essential for ensuring 'Model Observability' in an enterprise production environment?

Accepted Answer

System latency monitoring. Observability requires both performance metrics and input monitoring (drift).

Answer

Manual code refactoring

Answer

Database backup frequency

Answer

Developer salary audits

Question 5

Which TWO items should be included in an automated 'Deployment Log'?

Accepted Answer

Deployed model version/ID. Option A (Deployed model version/ID) is correct because a deployment log must record exactly which artifact was released so that any running model can be traced back to a specific version for rollback, auditing, and reproducibility. Option E (Deployment timestamp) is correct because recording when the deployment occurred establishes a chronological audit trail, enabling correlation with incidents, monitoring metrics, and change-management records. The remaining options do not belong: office temperature (B) is an environmental reading irrelevant to release tracking, team member home address (C) is personal data with no deployment significance and would raise privacy concerns, and favorite programming language (D) is a personal preference that has no bearing on what was deployed or when.

Answer

Office temperature

Answer

Team member home address

Answer

Favorite programming language

Question 6

Which TWO are common types of 'AI Model Drift'?

Accepted Answer

Concept drift. Concept drift (A) is a correct answer because it describes the situation where the statistical relationship between input features and the target variable changes over time, so a model's learned mapping becomes stale and its predictions degrade. Data drift (B) is also correct because it refers to a change in the distribution of the input features (also called covariate shift) relative to the training data, which is one of the most common forms of model drift monitored in production ML systems. The remaining options are not recognized categories of AI model drift: temperature drift (C) is not an ML drift type (temperature is a sampling parameter in LLMs, not a drift class), while CPU drift (D) and memory drift (E) are hardware/infrastructure phenomena unrelated to changes in data or concept distributions.

Answer

Temperature drift

Answer

CPU drift

Answer

Memory drift

Question 7

When scoping an audit, which TWO stakeholders should the auditor interview?

Accepted Answer

Model Owners. Model Owners (A) are correct because they hold accountability for the model's purpose, risk classification, and governance decisions, making them essential for defining the audit's scope and objectives. Data Scientists (D) are correct because they possess the technical detail on data sources, feature engineering, training pipelines, and model behavior needed to bound the audit's technical scope. The lobby receptionist (B), external marketing agency (C), and office cleaning staff (E) are not relevant stakeholders for scoping an audit, as they have no ownership, development, or operational responsibility for the model or its data.

Answer

The lobby receptionist

Answer

The company's external marketing agency

Answer

Office cleaning staff

Question 8

Which THREE factors influence 'AI Pipeline' latency?

Accepted Answer

Model parameter size. Option B (Model parameter size) is correct because larger models require more computation per inference, increasing processing time and thus pipeline latency. Option C (Network transfer speed) is correct because AI pipelines often move data between storage, preprocessing, and inference services, and slower bandwidth or higher latency links directly add to end-to-end delay. Option D (Data preprocessing complexity) is correct because heavy operations such as tokenization, resizing, normalization, or feature engineering consume CPU/GPU cycles before inference, extending total pipeline latency. Options A (Company CEO name) and E (Office building height) are irrelevant metadata that have no technical effect on compute, transfer, or processing time in an AI pipeline.

Answer

Company CEO name

Answer

Office building height

Question 9

Which THREE of the following are potential risks associated with AI model 'Drift' that an auditor must account for?

Accepted Answer

Bias against specific demographic groups that emerged post-deployment. Drift impacts operational accuracy, fairness, and compliance, making it a critical governance risk.

Answer

Increased electricity consumption of servers

Answer

Inability to access the source code of the model

Question 10

Which THREE tasks are required for a 'Model Decommissioning' workflow?

Accepted Answer

Terminate serving infrastructure. Decommissioning requires archival, notification, and resource cleanup.

Answer

Ask developer for coffee

Answer

Delete all company records