AAIA Practice Test 12 — 15 Questions

Question 1

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 2

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 3

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 4

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 5

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

Question 6

In SageMaker, what does 'Model Monitor' specifically detect?

Accepted Answer

Data and model drift. It monitors for data drift and model quality degradation in production.

Answer

Database uptime

Answer

Model size

Answer

Employee attendance

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

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

Question 9

What is the purpose of 'Performance Monitoring' in AI?

Accepted Answer

Ensuring SLA compliance. To detect if the system is meeting its latency, throughput, and accuracy targets.

Answer

Encrypting data

Answer

Changing model weights

Answer

Renaming models

Question 10

Which THREE of the following are essential components of an MLOps maturity model for ensuring operational stability?

Accepted Answer

Centralized model registry and artifact management. Maturity models focus on automation, monitoring, and governance.

Answer

Storing raw data directly in the model repository

Answer

Manual model validation processes

Question 11

An organization experiences 'Data Leakage' in a machine learning project. What is the most likely cause during the training process?

Accepted Answer

Using target data in training features. Using test/validation data during feature selection or training leads to leakage.

Answer

Using standard Python libraries

Answer

Using secure cloud storage

Answer

Using too many GPUs

Question 12

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 13

Which TWO of the following are primary benefits of implementing a Model Registry in an AI production environment?

Accepted Answer

Enables versioning and lifecycle management of models. Model registries ensure version control and facilitate collaboration.

Answer

Allows for direct modification of training data

Answer

Increases the hardware utilization of GPUs

Answer

Automatically deletes old training instances

Question 14

In SageMaker, what is the role of an 'Endpoint Configuration'?

Accepted Answer

Defines model variants and compute resources. Defines the model, instance type, and traffic distribution (variants) for the production endpoint.

Answer

Defines the database password

Answer

Defines the data cleaning logic

Answer

Defines the training budget

Question 15

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