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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Optimize generative AI systems and model performance | 15–20% | - Optimize model selection and configuration
|
| Topic 2: Design and implement an MLOps infrastructure | 15–20% | - Create and manage Machine Learning workspace resources and assets
|
| Topic 3: Implement machine learning model lifecycle and operations | 25–30% | - Register, version, and package models
|
| Topic 4: Implement generative AI quality assurance and observability | 10–15% | - Monitor generative AI systems
|
| Topic 5: Design and implement a GenAIOps infrastructure | 20–25% | - Implement infrastructure for generative AI workloads
|
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NEW QUESTION # 162
Hotspot Question
You train a model in Azure Machine Learning.
You plan to capture experiment details for later comparison. The training code must log parameters and metrics for each run.
You review the following training script.
You need to verify whether the training script meets the experiment tracking requirement. For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
NEW QUESTION # 163
Drag and Drop Question
A team is developing a generative AI assistant. The team is experimenting with two prompt variants to improve performance before rolling out changes to production.
The team observes the following prompt results:
- PromptA variant generates longer responses and may be more expensive
to operate.
- PromptB may produce lower-quality answers.
The team must control operating costs while still selecting the better-performing prompt.
You need to identify cost drivers and compare the output quality of the two prompt variances to make an informed decision.
Which actions should you perform? To answer, move the appropriate actions to the correct requirements. You may use each action once, more than once, or not at all. You may need to move the split bar between panes or scroll to view content.
Answer:
Explanation:
NEW QUESTION # 164
You plan to filter your traces to identify issues while observing how the application is responding. The solution must not use an external knowledge base.
You need to select an evaluation metric.
Which built-in evaluator should you use?
Answer: D
Explanation:
A multi-turn chatbot application intermittently produces responses that are grammatically correct and on-topic but contradict earlier turns in the conversation, creating a confusing user experience. CoherenceEvaluator measures exactly this: whether the flow of ideas across a multi-turn conversation is logically consistent and non-contradictory without requiring an external knowledge base. RelevanceEvaluator (option A) measures whether responses are topically on-point but often requires a reference context or knowledge base.
SimilarityEvaluator (option B) requires a reference answer for comparison. QAEvaluator (option C) is a composite evaluator for question-answering tasks that requires a ground-truth context document.
CoherenceEvaluator is the only option that works purely from the conversation history itself with no external knowledge base, perfectly matching the stated constraint and the multi-turn chatbot use case.
Microsoft Learn Reference Topic: Evaluate conversational AI applications in Microsoft Foundry - CoherenceEvaluator for multi-turn chatbots
NEW QUESTION # 165
A data science team plans to evaluate multiple hyperparameter values automatically while training a model in Azure Machine Learning.
The tuning process must run multiple training trials without manually modifying the training script for each run.
You need to automate hyperparameter tuning for the training job.
What should you do?
Answer: C
Explanation:
Correct:
* Create a tuning job that runs multiple trials with different parameter values To best automate hyperparameter tuning in Azure Machine Learning, you should create a Sweep Job (in SDK v2) or a HyperDrive experiment (in SDK v1).
This creates a tuning job that automatically launches multiple training trials (child runs) using your single base training script without requiring manual code modifications for each run.
Key Components to Automate the Job
To set up this job successfully using the Azure Machine Learning Python SDK v2, you will define:
Parameterized Training Script: Write your code to accept hyperparameters as command-line arguments (e.g., using Python's argparse), allowing the tuning job to pass different values to each trial.
Search Space: Define the range or specific discrete/continuous choices for the values you want to test (e.g., learning rates, batch sizes).Sampling Algorithm: Choose how Azure ML should navigate your search space. Options include Random sampling, Grid sampling (testing every possible combination), or Bayesian sampling (using previous trial results to pick the next best values).Primary Metric: Specify the performance metric your script logs (like accuracy or loss) so Azure ML knows which target to optimize.
Early Termination Policy: Optional policy (like a Bandit Policy) to automatically cancel poorly performing trials early, saving you compute time and cost.
Incorrect:
* Adjust hyperparameters after model deployment.
* Duplicate the training script for each parameter combination.
* Manually change hyperparameter values between training runs.
* Run a single training job with fixed hyperparameters.
* Select hyperparameters based only on default model settings.
Reference:
https://learn.microsoft.com/en-us/python/api/azureml-train-core/azureml.train.hyperdrive
NEW QUESTION # 166
You manage an Azure Machine Learning workspace by using the Python SDK v2.
You must create a compute cluster in the workspace. The compute cluster must run workloads and properly handle interruptions. You start by calculating the maximum amount of compute resources required by the workloads and size the cluster to match the calculations.
The cluster definition includes the following properties and values:
* name= " mlcluster1''
* size= " STANDARD.DS3.v2 "
* min_instances=1
* maxjnstances=4
* tier= " dedicated "
The cost of the compute resources must be minimized when a workload is active Of idle. Cluster property changes must not affect the maximum amount of compute resources available to the workloads run on the cluster.
You need to modify the cluster properties to minimize the cost of compute resources.
Which properties should you modify? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
NEW QUESTION # 167
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