27 questions · Generative Ai Quality Assurance And Observability topic · All types, answers revealed
When configuring observability for an AI application, which TWO telemetry types should you collect to analyze both performance and quality?
Essential quality metric.
Why this answer
Performance metrics (latency) and quality/safety flags are essential for observability.
You are monitoring an Azure OpenAI deployment and need to identify if a model is outputting content that violates safety policies. Which Azure AI Content Safety feature should you enable to categorize harmful content?
The Content Safety API allows for scanning text against predefined safety categories.
Why this answer
The Content Safety service provides classification categories such as Hate, Self-Harm, Sexual, and Violence to filter model outputs.
You are tracking LLM performance. Which metric is most critical to monitor if your cost-per-request is increasing unexpectedly?
Token consumption is the primary driver of costs.
Why this answer
Token usage directly correlates with cost in most LLM provider pricing models.
In an LLM evaluation workflow, what does the 'Coherence' metric measure?
Coherence is specifically about the logical quality of the output.
Why this answer
Coherence measures how well a model-generated answer makes sense and flows logically as a human-like response.
You are setting up an evaluation suite for your LLM. Which THREE metrics are commonly provided by the 'Built-in' evaluators in Azure AI Prompt Flow?
Built-in metric.
Why this answer
The standard built-in metrics in Prompt Flow include Groundedness, Relevance, and Coherence.
You are designing a quality assurance gate for your model. If a model output has a 'Violence' score of 0.8 according to Azure AI Content Safety, what is the best practice to handle it?
Blocking unsafe content is the industry standard for content safety.
Why this answer
A high score indicates a potential violation, and the output should be blocked or sanitized before reaching the end user.
When designing a content safety policy, which THREE categories are explicitly supported by the Azure AI Content Safety API?
Supported category.
Why this answer
Azure AI Content Safety supports Hate, Sexual, Violence, and Self-Harm categories.
You want to evaluate how well your model adheres to specific brand guidelines. Which evaluation method is best suited for this?
A custom evaluator can be prompted to check for specific brand style guidelines.
Why this answer
Custom evaluation using a judge model (LLM-as-a-judge) configured with a rubric allows for checking specific style or brand compliance.
You are auditing your model's safety logs and notice several 'jailbreak' attempts. Where can you find these logs in the Azure ecosystem?
Log Analytics stores the data captured by Content Safety for auditing.
Why this answer
Azure AI Content Safety logs, when integrated with Azure Monitor/Log Analytics, allow for auditing and analysis of safety events.
Which of the following is a key component of an observability strategy for Generative AI applications?
Comprehensive observability covers both system health and output quality.
Why this answer
Observability requires tracking both technical metrics (latency, errors) and quality metrics (groundedness, safety).
Which THREE tools in Azure can be used to monitor the health and performance of your GenAI infrastructure?
Standard for log aggregation.
Why this answer
Application Insights, Log Analytics, and Azure Monitor are core observability tools.
When using Prompt Flow for GenAI, where should you store your evaluation results to visualize them over time?
The workspace provides the infrastructure for run tracking and analysis.
Why this answer
Prompt Flow logs evaluation runs to the Azure Machine Learning workspace, where they can be viewed in the Runs tab.
You are evaluating an LLM application using Prompt Flow. You want to measure the 'Groundedness' of the model response relative to the retrieved context. Which evaluator should you configure?
Groundedness specifically measures factual consistency with source context.
Why this answer
The Groundedness evaluator in Prompt Flow checks if the response is supported by the context provided.
You want to automate the evaluation of your LLM application using a 'Golden Dataset'. What is the primary purpose of this dataset in an MLOps pipeline?
Comparing current outputs against a verified set allows for regression testing.
Why this answer
A Golden Dataset serves as the ground truth to compare model outputs against during automated evaluation runs.
You are using Azure AI Studio to evaluate your model. Which tool allows you to perform batch testing on a large dataset of prompts?
The evaluation tab is designed for batch testing and scoring.
Why this answer
The 'Evaluation' feature in Azure AI Studio allows for running batch tests using built-in metrics.
In an LLM pipeline, what is the primary risk of relying solely on automated 'Groundedness' evaluators?
No automated evaluator is perfect; it can miss complex or subtle hallucinations.
Why this answer
Automated evaluators (LLM-as-a-judge) may have their own biases or may incorrectly classify outputs, leading to false negatives/positives.
Which TWO of the following are recommended methods for identifying 'hallucinations' in a RAG system?
Comparing against a reference answer is a standard approach.
Why this answer
Comparing output to source context (groundedness) and using a reference-based evaluation are standard methods.
You are deploying a GenAI app to production and want to track the 'Token Usage' and 'Latency' metrics per user session. Which service should you integrate with your application?
Application Insights allows custom tracking of request duration and metadata.
Why this answer
Application Insights is the standard observability tool for Azure, enabling custom event tracking for tokens and latency.
You are building a RAG application and notice that the model sometimes hallucinates information not present in the retrieved documents. Which evaluation metric should you prioritize to mitigate this?
Groundedness verifies the response is based on the source context.
Why this answer
Groundedness specifically assesses whether the generated response is derived from the retrieved documents.
You want to measure 'Relevance' in a RAG application. The relevance evaluator detects how well the response answers the user query. If the model provides a factually correct answer that does not address the prompt, which metric will capture this failure?
Relevance measures the alignment of the answer to the question.
Why this answer
Relevance specifically measures if the response directly addresses the user's intent.
Which TWO metrics are most useful for evaluating the 'User Experience' in a conversational agent?
An irrelevant answer is a poor user experience.
Why this answer
Latency and the quality of the answer (Relevance) are the primary drivers of user satisfaction.
Which THREE actions are essential when managing a 'Golden Dataset' for LLM evaluation?
Versioning ensures reproducibility.
Why this answer
Curating high-quality data, versioning it, and periodically updating it are key management activities.
You need to detect 'jailbreak' attempts in your RAG application. You are implementing a custom evaluation pipeline. Which technique is most effective for identifying adversarial inputs designed to bypass system instructions?
Using a judge model to classify prompt intent is the recommended approach for detecting jailbreaks.
Why this answer
Adversarial evaluation using a 'jailbreak' detection model or prompt-based evaluation is the standard approach to identify bypass attempts.
What is the primary function of a 'Prompt Template' in an Azure AI Prompt Flow?
Templates provide a repeatable structure for LLM interaction.
Why this answer
A prompt template allows users to define the structure of the prompt while injecting dynamic variables.
What is the primary benefit of 'Prompt Versioning' in an MLOps lifecycle?
Versioning ensures you can track which prompt produced which output.
Why this answer
Versioning prompts allows for tracking changes, reverting to previous versions, and comparing performance over time.
Your team wants to perform 'Red Teaming' on your application. Which activity describes this process correctly?
Red teaming is specifically about finding vulnerabilities through adversarial simulation.
Why this answer
Red Teaming involves adversarial testing to find edge cases, safety flaws, and security vulnerabilities.
When implementing LLM-as-a-judge for evaluation, which TWO factors can influence the reliability of your results?
The judge's system prompt dictates evaluation logic.
Why this answer
The choice of judge model and the design of the system prompt for the judge significantly impact the result quality.
Ready to test yourself?
Try a timed practice session using only Generative Ai Quality Assurance And Observability questions.