Interview - Senior AI QA Engineer

1

🟢 EASY

0 / 2 points

What is the primary responsibility of an AI QA Engineer in IE (Intelligent Experiences) pods?

Testing UI functionality and visual design

Ensuring AI systems behave reliably, safely, and consistently under real-world conditions

Managing the team's backlog and sprint planning

Building continuous monitoring and evaluation strategies for AI components

Serving as the quality backbone for agentic workflows, retrieval systems, and multi-agent orchestration

2

🔵 MEDIUM

0 / 3 points

How do you validate data quality in RAG (Retrieval-Augmented Generation) pipelines?

Only check if files upload successfully

Validate data correctness, consistency, and completeness for all pipelines

Test embedding quality and retrieval accuracy

Verify ranking performance and precision metrics

Skip validation if the data source is trusted

3

🟠 HARD

0 / 4 points

Design an evaluation suite for testing multi-agent orchestration workflows. What components would you include?

Only test individual agents in isolation

Test agent-to-agent communication and handoff protocols

Validate coordination logic and state management across agents

Create scenarios testing failure recovery and graceful degradation

Test concurrency, race conditions, and deadlock scenarios

4

🟢 EASY

0 / 2 points

What does 'red-teaming' mean in the context of AI QA?

Testing code with red color highlighting

Simulating adversarial or high-risk scenarios to uncover system weaknesses

Creating structured tests for safety, compliance, and robustness

Only testing during production incidents

Validating handling of ambiguous inputs and malformed requests

5

🔵 MEDIUM

0 / 3 points

What evaluation frameworks and approaches would you use for LLM testing?

Only manual review by humans

Scenario-based evaluations with diverse test cases

Confusion tests and behavioral probes

Automated regression testing for model outputs

Testing can be fully automated without human oversight

6

🟠 HARD

0 / 4 points

How would you detect and prevent bias in production AI systems?

Bias detection is impossible in AI systems

Implement monitoring for disparate outcomes across demographic groups

Create evaluation datasets representing diverse populations

Set up alerts for bias metrics exceeding defined thresholds

Regular audits with fairness-focused test scenarios

7

🟢 EASY

0 / 2 points

What is model drift and why does it matter in AI systems?

When model performance changes randomly without reason

Gradual degradation in model performance over time as data distributions change

Detecting and escalating drift trends quickly is critical for reliability

It only affects traditional ML, not LLMs or agentic systems

Requires continuous monitoring to catch before it impacts production

8

🔵 MEDIUM

0 / 3 points

How do you approach testing nondeterministic AI systems?

Use traditional deterministic test cases only

Design tests that account for probabilistic behavior

Create evaluation suites with acceptable variance ranges

Test behavior patterns rather than exact outputs

Avoid testing since outputs are unpredictable

9

🔴 CRITICAL

0 / 5 points

Design an end-to-end QA strategy for a frontier agentic system handling complex multi-step workflows (e.g., financial analysis with 10+ agents). What key elements would you include?

Only unit tests for individual functions

Unified testing across UI, backend, retrieval, and AI components

Behavioral evaluations including edge cases and failure modes

Continuous monitoring with drift detection and automated alerts

Red-team exercises simulating adversarial scenarios and high-risk inputs

10

🔵 MEDIUM

0 / 3 points

What metrics would you track to monitor agent behavior reliability?

Only success/failure binary outcomes

Latency, cost, and error modes

Drift, bias, and degradation trends

Hallucination patterns and reasoning failures

Metrics are not needed for AI systems

11

🟠 HARD

0 / 4 points

Implement an automated regression testing strategy for agent workflows. What would you focus on?

Only test after major releases

Create baseline behavior snapshots for key scenarios

Automate comparison of current vs. baseline outputs

Track behavioral consistency across model updates

Flag significant deviations for human review

12

🔵 MEDIUM

0 / 3 points

How do you integrate AI testing into CI/CD pipelines?

Run tests manually after deployment

Build automated test harnesses for Python services and APIs

Integrate automated tests that catch regressions early

Set up continuous evaluation for model behavior

CI/CD is only for traditional software, not AI

13

🟠 HARD

0 / 4 points

Create a data lineage validation strategy for AI pipelines. What elements are essential?

Data lineage is not important for AI systems

Track data sources, transformations, and versions

Validate data consistency at each pipeline stage

Monitor for data quality issues and anomalies

Ensure traceability from raw data to model outputs

14

🔵 MEDIUM

0 / 3 points

What's the difference between hallucination and reasoning failure in LLMs?

They are the same thing

Hallucination: generating plausible but factually incorrect information

Reasoning failure: incorrect logical steps or conclusions

Both require different detection and mitigation strategies

Only hallucination needs to be tested

15

🔴 CRITICAL

0 / 5 points

Build a comprehensive observability framework for AI Ops. What infrastructure and metrics would you implement?

Basic logging is sufficient for AI systems

Real-time dashboards tracking latency, cost, accuracy, and error rates

Distributed tracing for multi-agent workflows and API calls

Alerting system for drift, bias, performance degradation, and anomalies

Integration with observability platforms (DataDog, Grafana, custom dashboards)

🔗 Technical Interview

📋 Candidate Information

📊 Final Score

🟢 Easy

🔵 Medium

🟠 Hard

🔴 Critical

📋 Interview Report