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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Design and implement a GenAIOps infrastructure | 20–25% | - Implement infrastructure for generative AI workloads
|
| Topic 2: Design and implement an MLOps infrastructure | 15–20% | - Implement infrastructure as code for Machine Learning
|
| Topic 3: Optimize generative AI systems and model performance | 15–20% | - Improve efficiency and cost-effectiveness
|
| Topic 4: Implement machine learning model lifecycle and operations | 25–30% | - Orchestrate model training and experimentation
|
| Topic 5: Implement generative AI quality assurance and observability | 10–15% | - Monitor generative AI systems
|
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NEW QUESTION # 18
Hotspot Question
A team manages an Azure Machine Learning workspace to train and register machine learning models.
Previous model versions must be retained for audit and rollback purposes but must not be used for new deployments.
You need to manage model versions.
What should you do for each requirement? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
NEW QUESTION # 19
An organization validates generative AI applications during CI/CD Microsoft Foundry.
Evaluation must run automatically and block releases when quality thresholds are NOT met.
Manual evaluation is no longer acceptable.
Evaluation must use both predefined quality metrics and custom safety checks.
You need to implement an automated evaluation workflow that supports both built-in and custom metrics.
What should you do?
Answer: C
Explanation:
To implement an automated evaluation step in GitHub Actions for Microsoft Foundry AI, you can use the Microsoft Foundry Evaluation GitHub Action (or the Azure AI Evaluation SDK).
This setup allows you to run both built-in metrics (like groundedness or coherence) and custom safety checks, then fail the build if scores fall below your defined thresholds.
Implementation Steps
1. Define Your Evaluators
You need to configure which metrics to use. Microsoft Foundry supports two main types:
Built-in Metrics: Pre-trained models that score quality (coherence, fluency) and safety (hate, violence, self-harm).
Custom Metrics: Python-based evaluators you define to check domain-specific requirements.
2. Configure the GitHub Actions Workflow
Create a .yml file in your .github/workflows directory. This workflow will:
Trigger on a pull request or commit.
Authenticate with Azure/Foundry.
Run Evaluation using the microsoft/ai-agent-evals action.
Enforce Thresholds to block the release if quality is insufficient.
Key Components for "Block Release" Logic
To ensure the release is blocked, your workflow must include a gating step that interprets the evaluation results:
Reference:
https://learn.microsoft.com/en-us/training/modules/automated-evaluation-genaiops
NEW QUESTION # 20
Hotspot Question
You download a .csv file from a notebook in an Azure Machine Learning workspace to a data/sample.csv folder on a compute instance. The file contains 10,000 records.
You must generate the summary statistics for the data in the file. The statistics must include the following for each numerical column:
- number of non-empty values
- average value
- standard deviation
- minimum and maximum values
- 25th, 50th, and 75th percentiles
You need to complete the Python code that will generate the summary statistics.
Which code segments should you use? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Box 1: pandas
Pandas read_csv() - How to read a csv file in Python
Most of the data is available in a tabular format of CSV files. It is very popular. You can convert them to a pandas DataFrame using the read_csv function. The pandas.read_csv is used to load a CSV file as a pandas dataframe.
Step 1: pandas
import pandas as pd
Step 2: Read the CSV
# Read the csv file
df = pd.read_csv("data1.csv")
Box 2: describe
The pandas.describe function is used to get a descriptive statistics summary of a given dataframe. This includes mean, count, std deviation, percentiles, and min-max values of all the features.
Pandas Describe function on DataFrame
On applying pandas describe function to a dataframe, the result is also returned as a dataframe .
This dataframe will consist of a statistics summary for all the numeric features of the dataframe.
# Create a dataframe
df = pd.DataFrame({
'Subject_1_Marks': [14, 42, 21, 12, 45],
'Subject_2_Marks': [32, 43, 23, 50, 21],
'Subject_3_Marks': [45.0, 34.0, 23.0, 8.0, 21.0],
'Names': ['Saksham', 'Ayushi', 'Abhishek', 'Saksham', 'Saumya']
}
)
# Apply describe function
df.describe()
Incorrect:
* rank
Reference:
https://www.machinelearningplus.com/pandas/pandas-read_csv-completed/
https://www.machinelearningplus.com/pandas/pandas-describe/
NEW QUESTION # 21
A team develops multiple AI applications in Microsoft Foundry that rely on shared prompt templates.
The team requires a centralized way to track, version, and reuse prompt content across projects.
You need to recommend a solution to track and reuse prompt content.
Which approach should you recommend?
Answer: B
Explanation:
In Microsoft Foundry, centralizing prompt templates in a Git repository (such as GitHub) is a recommended GenAIOps practice to ensure consistency, versioning, and reuse across multiple AI applications.
Storing prompts as versioned files in a Git repository is an excellent approach for Microsoft Foundry applications. It treats prompts like code (PromptOps), enabling clear audit trails, peer reviews via Pull Requests, and easy rollbacks.
To make this centralized system effective, you should consider these three components:
Centralized Repository: Use a single "Prompt Bank" repo where templates are stored in a structured format like JSON or YAML. Include metadata like model version, temperature, and input variables.
Prompt Registry / SDK: Develop a small internal utility (or use a tool like Azure AI Studio's Prompt Flow) that allows applications to fetch the "latest" or a "tagged" version of a prompt via API or as a Git submodule.
CI/CD Integration: Automate testing so that when a prompt file is updated, it triggers a "prompt evaluation" pipeline to ensure the change doesn't degrade AI performance across the dependent projects.
Reference:
https://www.getmaxim.ai/articles/best-practices-for-prompt-management-in-ai-applications
NEW QUESTION # 22
A team schedules weekly retraining of a model using Azure ML pipelines. They also want retraining triggered automatically when production data significantly deviates from training data distribution, without duplicating pipeline logic. What should they implement?
Answer: C
Explanation:
Using a single pipeline triggered by both a schedule and data drift alerts ensures consistent retraining logic and avoids duplication. This approach minimizes operational overhead and maintenance complexity. Creating multiple pipelines can lead to inconsistencies, duplicated code, and increased effort when updating retraining logic or dependencies.
NEW QUESTION # 23
......
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