AAIA AI Operations Practice Test 5 — 15 Questions

Question 1

What is the most effective way to address 'Adversarial Input' during inference?

Accepted Answer

Adversarial training and input filtering. Implementing input validation/sanitization and adversarial training (training on adversarial examples) improves robustness.

Answer

Reducing training time

Answer

Increasing model depth

Answer

Ignoring the input

Question 2

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 3

If an AI pipeline fails, which component provides the best 'log' to diagnose the failure?

Accepted Answer

Orchestrator task execution logs. The pipeline orchestrator's task logs indicate specifically which step failed and why.

Answer

Model documentation

Answer

Company mission statement

Answer

Developer's email

Question 4

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 5

When auditing an AI pipeline, where should 'Environment Variables' be stored to ensure security?

Accepted Answer

Secrets Manager. Environment variables frequently hold credentials, API keys, and connection strings, so they must be stored in a dedicated secrets management service such as Secrets Manager. This provides encryption at rest, access control, auditing, and rotation, which are the security properties an audit expects. Storing them anywhere else exposes them to leakage.

Answer

Git repository

Answer

Hardcoded in script

Answer

Publicly accessible folder

Question 6

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 7

Which operational process is required to ensure 'Data Privacy' when using user-generated data for model retraining?

Accepted Answer

Data anonymization. Anonymization or pseudonymization must be performed before training.

Answer

Increasing compute power

Answer

Model quantization

Answer

Removing the model

Question 8

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 9

Which THREE items should be tracked in a 'Model Registry' for audit readiness?

Accepted Answer

Training dataset lineage. A is correct because training dataset lineage documents which data was used to train a model, which is essential for audit trails, reproducibility, and compliance with data governance requirements. B is correct because a source code reference such as a Git commit uniquely identifies the exact code version that produced the model, enabling auditors to trace and reproduce the training pipeline. E is correct because a model artifact hash provides cryptographic integrity verification, ensuring the deployed model has not been altered and matches the audited version. C is not appropriate because a cloud provider logo is merely branding and carries no audit or traceability value. D is not appropriate because employee birthday is personal data unrelated to model governance and would raise privacy concerns rather than support audit readiness.

Answer

Cloud provider logo

Answer

Employee birthday

Question 10

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 11

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 12

What is the risk of 'Feature Skew' in an AI pipeline?

Accepted Answer

Incorrect model predictions. Feature skew (also called training-serving skew) occurs when the feature values used at training time differ from those computed at inference time — for example, different preprocessing, aggregation windows, or data sources. Because the model learned relationships based on the training distribution, mismatched inputs at serving time cause it to produce incorrect predictions. This is a data/feature consistency problem, not an infrastructure failure.

Answer

Database corruption

Answer

Increased hardware costs

Answer

System crash

Question 13

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 14

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 15

Which TWO are key 'AI Operational' documentation artifacts?

Accepted Answer

Pipeline Architecture Diagram. Option C, the Pipeline Architecture Diagram, is a key AI Operational artifact because it documents the end-to-end data and model workflow — ingestion, preprocessing, training, deployment, and monitoring stages — enabling teams to operate, troubleshoot, and maintain the ML system reliably. Option D, the Model Card, is also a key AI Operational artifact because it provides standardized documentation of a model's intended use, training data, performance metrics, limitations, and ethical considerations, which is essential for responsible deployment and ongoing governance. The unmarked options do not belong: A (Personal employee diary) is private personal documentation unrelated to AI operations, B (Restaurant menu) is a food-service document with no bearing on AI systems, and E (Grocery shopping list) is an everyday personal list irrelevant to AI operational documentation.

Answer

Personal employee diary

Answer

Restaurant menu

Answer

Grocery shopping list