Manual Prompt Evaluation Quiz
5 questions Pass: 70% +25 pts (50 with Pro)
Quiz covering Prompt Engineering and Prompt Flow
What you'll be asked
- When manually evaluating a prompt's output, what is the primary purpose of using a 'ground truth' reference?
- You are evaluating a prompt for a customer support bot. The bot correctly answers the user's question but uses a tone that is too casual for your brand guidelines. Which evaluation metric should you prioritize?
- When performing manual evaluation of prompt outputs, what is the 'Chain of Thought' (CoT) technique intended to reveal?
- Which of the following is a common pitfall when manually evaluating prompts using a small, non-diverse dataset?
- During a manual evaluation, you notice the model exhibits 'hallucination' on a specific prompt variation. Which strategy is most effective to resolve this during the iterative prompt engineering cycle?
Answers and explanations come after you submit.
Course Content
- Automated Machine Learning for Tabular Data
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- Automated Machine Learning for Computer Vision
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- Automated Machine Learning for NLP
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- Training Options Preprocessing and Algorithms
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- Evaluating AutoML Runs with Responsible AI
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- Configuring a Compute Instance Terminal
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- Accessing and Wrangling Data in Notebooks
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- Using Synapse Spark Pools and Serverless Spark
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- Retrieving Features from a Feature Store
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- Tracking Model Training with MLflow
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- Evaluating Models with Responsible AI Guidelines
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- Consuming Data in a Job
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- Configuring Compute for a Job Run
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- Configuring an Environment for a Job Run
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- Tracking Model Training with MLflow in Jobs
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- Defining Parameters for a Job
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- Running a Script as a Job
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- Using Logs to Troubleshoot Job Errors
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- Configuring Settings for Online Deployment
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- Deploying a Model to an Online Endpoint
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- Testing an Online Deployed Service
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- Configuring Compute for Batch Deployment
- Batch Compute Configuration Quiz5q
- Deploying a Model to a Batch Endpoint
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- Invoking Batch Endpoint for Scoring Jobs
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- Preparing Data for Fine-Tuning
- Fine-Tuning Data Preparation Quiz5q
- Selecting an Appropriate Base Model
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- Running a Fine-Tuning Job
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- Evaluating Your Fine-Tuned Model
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- Deploying Fine-Tuned Models
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- Understanding AI Model Evaluation Metrics
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- Implementing Content Safety Filters
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- Managing Model Bias and Fairness
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- Model Interpretability and Explainability
- Model Interpretability Quiz5q
- Monitoring Model Performance in Production
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- Data Drift Detection and Handling
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- Azure AI Content Safety Integration
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- Model Versioning and Lineage Tracking
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- A/B Testing for Model Deployment
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- Cost Optimization for ML Workloads
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- Security Best Practices for ML Models
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