AAIA Practice Test 5 — 25 Questions

Question 1

Which TWO are key 'AI Operational' documentation artifacts?

Accepted Answer

Pipeline Architecture Diagram. Governance requires documentation of models and pipelines.

Answer

Personal employee diary

Answer

Restaurant menu

Answer

Grocery shopping list

Question 2

Which THREE items must be included in a 'Model Card' to comply with regulatory model risk management standards?

Accepted Answer

Intended use cases. Model cards summarize intended use, limitations, and performance characteristics.

Answer

Internal employee passwords

Answer

Cloud service provider revenue

Question 3

When auditing an ML pipeline, what does the 'DAG' (Directed Acyclic Graph) represent?

Accepted Answer

Pipeline task dependencies. The DAG defines the sequence and dependency of operational steps in the pipeline.

Answer

Model accuracy curve

Answer

The database schema

Answer

The network architecture

Question 4

Which TWO are common 'AI Security' threats that require operational oversight?

Accepted Answer

Model poisoning during training. Adversarial attacks and model poisoning are primary security concerns.

Answer

Slow training time

Answer

High memory usage

Answer

Team burnout

Question 5

What is the role of an 'A/B Test' in AI production?

Accepted Answer

Comparing two models in production. A/B testing allows comparing the performance of a new model against an existing one with real user traffic.

Answer

Encrypting data

Answer

Reducing training time

Answer

Deleting unused models

Question 6

What does a 'Model Approval' workflow ensure?

Accepted Answer

Human oversight of deployment. That a human reviewer has vetted the model against compliance and quality standards before production deployment.

Answer

Faster training

Answer

Reduced cloud costs

Answer

Automatic deletion

Question 7

An organization uses MLflow for model tracking. To ensure auditability of model lineage, what must be captured in the 'mlflow.log_artifact' call?

Accepted Answer

Git commit hash and data URI. Capturing the specific code version (git hash) and data snapshot ID ensures reproducibility.

Answer

Environment variable list

Answer

User IP address

Answer

Compute cluster name

Question 8

An auditor notices high 'latency' in an online model deployment. Which component should be inspected first?

Accepted Answer

Inference service resource limits. The inference container resource allocation or the request preprocessing overhead is the common bottleneck.

Answer

Developer laptop specs

Answer

Model documentation

Answer

Data labeling tool settings

Answer

Git repository history

Question 9

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

Accepted Answer

Data source connectivity. Availability depends on infrastructure health and task success.

Answer

Company stock price

Answer

Developer social media activity

Question 10

When deploying an AI model on a public cloud, what is the best practice for 'Secret Management' of API keys?

Accepted Answer

Using a dedicated Secret Vault service. Secrets must be stored in a dedicated vault (e.g., AWS Secrets Manager, Azure Key Vault) and injected at runtime.

Answer

Printing to logs

Answer

Hardcoding in the script

Answer

Storing in a Git repo

Question 11

What is the purpose of 'Containerization' (e.g., Docker) in MLOps?

Accepted Answer

Ensuring environment consistency. Ensures the environment (dependencies, OS) is consistent across development, testing, and production.

Answer

Encrypting the model

Answer

Reducing data size

Answer

Increasing model accuracy

Question 12

Which AWS SageMaker feature allows auditors to verify that a model was trained on encrypted data?

Accepted Answer

KMS Key Usage in CloudTrail. SageMaker Training Jobs support 'Data Encryption' via KMS keys, which is logged in CloudTrail.

Answer

Instance type selection

Answer

Model registry labels

Answer

Notebook instance tags

Question 13

If a model's performance drops, and the input data distribution has changed, what operational process must be triggered?

Accepted Answer

Model Retraining. Data distribution change triggers model retraining using updated data samples.

Answer

Security audit

Answer

Model Deletion

Answer

Hardware Upgrade

Answer

Code Refactoring

Question 14

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 15

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 16

Which of the following is a key component of 'MLOps Maturity'?

Accepted Answer

Automated pipeline triggers. High maturity involves full CI/CD, automated testing, and active model monitoring.

Answer

Manual data entry

Answer

Single engineer silo

Answer

No documentation

Question 17

What is the primary function of a 'Version Control System' (like Git) in an MLOps team?

Accepted Answer

Managing code changes. To manage code changes, enable collaboration, and allow rollbacks.

Answer

Executing Python scripts

Answer

Storing raw video

Answer

Managing databases

Question 18

What is the function of a 'Feature Store' in an MLOps pipeline?

Accepted Answer

Centralized feature serving and storage. A Feature Store provides a centralized repository for standardized feature definitions and storage for both training and inference.

Answer

Storing raw video files

Answer

Compressing model weights

Answer

Monitoring compute costs

Question 19

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 20

Which of the following is an example of an 'operational metric' for an AI system?

Accepted Answer

Request latency. Operational metrics (system health) include latency, error rate, and throughput.

Answer

F1 Score

Answer

ROC AUC

Answer

Training accuracy