AAIA AI Operations Practice Test 3 — 15 Questions

Question 1

Which THREE components are critical to monitor in an AI pipeline to ensure operational availability?

Accepted Answer

Data source connectivity. Data source connectivity (A) is critical because if the pipeline cannot reach its upstream data sources, ingestion fails and the pipeline becomes unavailable regardless of downstream health. Pipeline task execution status (B) is essential to detect failed, stalled, or retrying tasks so operators can remediate breaks in the workflow and maintain availability. Resource utilization (CPU/GPU) (D) must be monitored because saturation or exhaustion of compute resources causes task failures, timeouts, and degraded throughput that directly impact operational availability. Company stock price (C) and developer social media activity (E) are unrelated to the technical health or availability of an AI pipeline and therefore are not monitoring components for operational availability.

Answer

Company stock price

Answer

Developer social media activity

Question 2

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.

Answer

Deleting training data after use

Answer

Caching features on local disk

Answer

Converting features to text

Question 3

What is 'Model Versioning' used for in an AI project?

Accepted Answer

Ensuring reproducibility and rollback. It allows tracking changes, rolling back to previous versions, and ensuring reproducibility.

Answer

Reducing model size

Answer

Improving training time

Answer

Increasing security

Question 4

In an AI Audit, what does 'Model Fairness' assessment typically involve?

Accepted Answer

Disparity analysis across segments. Checking for bias across protected groups (e.g., race, gender) in model performance metrics.

Answer

Counting model parameters

Answer

Checking model speed

Answer

Verifying code documentation

Question 5

Which metric would best detect 'Label Drift' in a production model?

Accepted Answer

Target distribution shift (PSI). Label drift occurs when the output (target) distribution changes significantly from training.

Answer

Model latency

Answer

CPU utilization

Answer

Number of users

Question 6

What is the consequence of 'Pipeline Staleness' in an AI system?

Accepted Answer

Model degradation due to outdated data. If the pipeline is not refreshed, it trains on outdated data, leading to model degradation.

Answer

Unauthorized access

Answer

Increased hardware costs

Answer

System crash

Question 7

When auditing a Kubernetes-based AI deployment, what does a 'Pod Disruption Budget' (PDB) ensure?

Accepted Answer

Minimum availability during maintenance. Ensures that a minimum number of replicas are available during voluntary disruptions (e.g., node upgrades).

Answer

The model version

Answer

Maximum allowed CPU usage

Answer

The cost of the cluster

Question 8

In the context of AI regulatory compliance, what is the 'Model Risk Management' (MRM) framework mainly concerned with?

Accepted Answer

Governance and validation controls. MRM manages the risks associated with model usage, including governance, validation, and control.

Answer

Reducing cloud expenses

Answer

Increasing developer productivity

Answer

Marketing model features

Question 9

What is the primary objective of a 'Model Inventory' in an enterprise AI audit?

Accepted Answer

Centralized visibility of all models. To have a centralized, authoritative list of all models in production for compliance and risk tracking.

Answer

To log user feedback

Answer

To optimize GPU usage

Answer

To store model weights

Question 10

Which of these is a valid reason to 'Roll Back' a model deployment?

Accepted Answer

Detected production performance issues. If the model in production starts showing unexpected errors or performance drops, rolling back is the immediate safety action.

Answer

To save storage space

Answer

To change the model name

Answer

Developer preference

Question 11

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 12

Which THREE criteria are essential when selecting an AI deployment strategy?

Accepted Answer

Service Level Agreements (SLAs). Option C (Service Level Agreements) is essential because SLAs define the required availability, latency, throughput, and uptime guarantees the AI deployment must meet, directly shaping whether a cloud, on-premises, or hybrid strategy is viable. Option D (Risk tolerance) is essential because it determines acceptable exposure to data privacy, regulatory compliance, model accuracy, and vendor lock-in risks, which dictates how much control and isolation the deployment environment must provide. Option E (Compute resource availability) is essential because AI workloads—especially training and inference—demand specific GPU/TPU capacity, memory, and scaling characteristics that constrain which deployment strategy can actually run the model. Options A (UI color) and B (number of coffee machines) are purely cosmetic or unrelated workplace factors and have no bearing on the technical, operational, or compliance requirements of an AI deployment strategy.

Answer

Color of the UI

Answer

Number of coffee machines

Question 13

What is the main benefit of 'Automated Retraining'?

Accepted Answer

Keeps models updated with new data. It ensures the model adapts to new data patterns without manual intervention.

Answer

Eliminates the need for humans

Answer

Reduces electricity costs

Answer

Speeds up inference

Question 14

In an AI Audit, what does an 'Input Feature Schema' define?

Accepted Answer

Expected data format and constraints. It defines the expected data types, shapes, and constraints for input features, ensuring integration stability.

Answer

The training duration

Answer

The database name

Answer

The model architecture

Question 15

What is 'Shadow Deployment' in AI MLOps?

Accepted Answer

Running a model in parallel without serving results. Running the new model alongside the production model, but without the new model's predictions influencing the final result, to validate performance.

Answer

Running only on local machine

Answer

Deleting the model

Answer

Replacing the production model