AAIA Practice Test 13 — 15 Questions

Question 1

Which THREE documents are typically required for an AI Audit trail?

Accepted Answer

Model training logs. Audit trail requires evidence of development, testing, and deployment.

Answer

Employee vacation calendar

Answer

Cafeteria lunch receipts

Question 2

In a GCP Vertex AI pipeline, you want to ensure that every model artifact is associated with the specific training data and code version used. Which component provides this lineage tracking?

Accepted Answer

Vertex AI Metadata. Vertex AI Metadata tracks the lineage of artifacts throughout the lifecycle.

Answer

Feature Store

Answer

Vertex AI Pipelines

Answer

Vertex AI Experiments

Question 3

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Model degradation due to outdated data. If the pipeline is not refreshed, it trains on outdated data, leading to model degradation.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 4

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

Accepted Answer

Deployed model version/ID. Logs must provide the 'who, when, and what' of the deployment.

Answer

Office temperature

Answer

Team member home address

Answer

Favorite programming language

Question 5

What does a 'Backtesting' report verify in an AI Audit?

Accepted Answer

Historical model performance. Backtesting uses historical data to verify how the model *would* have performed, ensuring reliability.

Answer

Future accuracy

Answer

Cloud service costs

Answer

Developer salary

Question 6

What is the role of a 'Deployment Pipeline' in MLOps?

Accepted Answer

Automating deployment steps. To automate the packaging, testing, and deployment of a model.

Answer

Storing logs

Answer

Writing code

Answer

Labeling data

Question 7

You are configuring a CI/CD pipeline for ML using GitHub Actions to deploy to a Kubernetes cluster. To ensure sensitive credentials for your container registry are not exposed in logs, what is the best practice?

Accepted Answer

Store credentials in GitHub Secrets. Using GitHub Secrets is the standard way to inject sensitive data into CI/CD pipelines securely.

Answer

Use environment variables in the workflow file

Answer

Hardcode credentials in the Dockerfile

Answer

Commit a .env file to the repository

Question 8

What is the purpose of an 'AI Audit trail'?

Accepted Answer

Providing transparency and accountability. To provide a transparent, verifiable record of all actions taken on an AI system.

Answer

Reducing cloud latency

Answer

Increasing model performance

Answer

Changing model parameters

Question 9

What is the primary function of an 'AI Model Gateway'?

Accepted Answer

Centralized request management and security. A gateway acts as a security and management layer for AI requests, providing throttling, logging, and authentication.

Answer

Storing model weights

Answer

Training the model

Answer

Labeling data

Question 10

A company uses MLflow for model registry and tracking. You need to transition a model from the 'Staging' environment to 'Production' only after it passes an automated integration test. How is this state transition managed in MLflow?

Accepted Answer

Using the 'transition_model_version_stage' API. Transitioning model versions in the MLflow Model Registry is done via the 'transition_model_version_stage' API or UI.

Answer

Updating the 'version' tag in the git repo

Answer

Deleting the model in Staging and re-uploading to Production

Answer

Manually copying files in the S3 bucket

Question 11

What is the function of a 'Circuit Breaker' in a microservice-based AI architecture?

Accepted Answer

Preventing failure propagation. To stop requests to a failing AI service to prevent system-wide collapse.

Answer

Increasing latency

Answer

Training the model

Answer

Managing databases

Question 12

Which THREE factors influence 'AI Pipeline' latency?

Accepted Answer

Model parameter size. Latency is affected by data processing, model size, and compute overhead.

Answer

Company CEO name

Answer

Office building height

Question 13

When auditing an AI system, how do you verify 'Model Reproducibility'?

Accepted Answer

Verifying pipeline run with identical inputs. Verify that you can re-run the training pipeline with the same code version and data snapshot to get the same model weights.

Answer

Asking the team

Answer

Checking cloud bills

Answer

Reading the documentation

Question 14

When setting up a containerized deployment of an AI model using Docker, what is the primary purpose of a 'multi-stage build'?

Accepted Answer

To reduce the final image size and attack surface. Multi-stage builds allow for the creation of smaller, more secure images by separating build-time dependencies from the runtime environment.

Answer

To run multiple versions of the model simultaneously

Answer

To train the model during the container startup

Answer

To automatically scale the deployment based on traffic

Question 15

When auditing an Azure Machine Learning pipeline, which functionality within 'Responsible AI dashboard' is required to perform counterfactual analysis on model predictions?

Accepted Answer

Counterfactuals component. The 'Counterfactuals' component in the Azure Responsible AI dashboard allows auditors to see how input changes impact predictions.

Answer

Causal Inference component

Answer

Model Overview component

Answer

Error Analysis component