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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Design and implement an MLOps infrastructure | 15–20% | - Create and manage Machine Learning workspace resources and assets
|
| Topic 2: Design and implement a GenAIOps infrastructure | 20–25% | - Implement infrastructure for generative AI workloads
|
| Topic 3: Implement machine learning model lifecycle and operations | 25–30% | - Register, version, and package models
|
| Topic 4: Optimize generative AI systems and model performance | 15–20% | - Optimize model selection and configuration
|
| Topic 5: Implement generative AI quality assurance and observability | 10–15% | - Evaluate and test generative AI applications
|
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NEW QUESTION # 142
Hotspot Question
You manage an Azure Machine Learning workspace. You configure an automated machine learning regression training job by using the Azure Machine Learning Python SDK v2.
You configure the regression job by using the following script:
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:
Explanation:
Box 1: Yes
Yes, the automated machine learning (AutoML) training job will terminate early if the primary metric score stops improving after a specific number of iterations.
According to the official Microsoft Azure Machine Learning SDK v2 documentation, setting enable_early_termination = True activates the early stopping policy for the overall experiment.
How Early Termination Works in AutoML v2
The internal early stopping logic operates on a built-in schedule to avoid premature termination:
First 20 iterations: No early stopping takes place (these serve as landmarks).
From the 21st iteration onward: The early stopping window activates.
Termination trigger: The job will automatically stop if the primary evaluation score fails to improve across 10 consecutive iterations.
Box 2: Yes
Yes, a maximum of five trials can run at the same time.
The limit comes from the code settings.
The max_concurrent_trials = 5 line tells Azure to run up to five trials at once.
Box 3: No
No, a single AutoML trial cannot run for 60 minutes before it is terminated.
Parameter Breakdown
In the Azure Machine Learning Python SDK v2, the set_limits() method handles timeouts via two distinct parameters:timeout_minutes = 60: This sets the maximum duration for the entire AutoML job (the experiment as a whole), including data preparation, featurization, and all training trials combined.
trial_timeout_minutes: This parameter governs the maximum time allowed for an individual trial (a single model training run) before termination.
Conclusion
Because trial_timeout_minutes is omitted from your script, it defaults to its standard system value (which is typically 20 minutes for tabular datasets). Therefore, an individual trial will time out much earlier than 60 minutes. Additionally, since the entire job terminates at 60 minutes, it is physically impossible for a single trial to consume the full 60 minutes without forcing the termination of the remaining concurrent runs.
Box 4: No
No, the AutoML trial cannot take up to 1 month before it terminates.
Based on the script parameters, the job will terminate after a maximum of 60 minutes.
Reference:
https://learn.microsoft.com/en-us/python/api/azure-ai-ml/azure.ai.ml.automl.regressionjob?view=azure-python
NEW QUESTION # 143
A Retrieval-Augmented Generation (RAG) solution returns incomplete answers because relevant content is inconsistently retrieved from the knowledge source.
You need to improve RAG accuracy without changing the embedding model currently in use. You need to achieve this goal while minimizing operational costs.
Which two actions should you perform? Each correct answer presents part of the solution.
Choose two.
NOTE: Each correct selection is worth one point.
Answer: A,D
Explanation:
To improve Retrieval-Augmented Generation (RAG) accuracy, address inconsistent retrieval, and eliminate incomplete answers without changing the embedding model or increasing costs significantly, you must move beyond naive fixed-length chunking and implement a two-stage retrieval process.
Here is the targeted, low-cost strategy:
1. Tune Chunk Size and Overlap to Match Content Structure
Inconsistent retrieval often occurs because important information is split across chunk boundaries (breaking context) or chunks are too large, diluting the semantic meaning.
2. Implement an Optimized Re-ranker
The initial vector search often returns "noise"-chunks that are semantically close but not actually relevant. A re-ranker acts as a second, smarter, but more "expensive" step that works on a smaller subset of data, making it low-cost overall.
Reference:
https://medium.com/@sthanikamsanthosh1994/how-to-improve-rag-retrieval-augmented- generation-performance-2a42303117f8
NEW QUESTION # 144
A team manages prompts used by a generative AI application built on Microsoft Foundry. Prompt updates are frequent, and prior changes have caused unexpected behavior in production.
The team requires that new prompt versions can be tested and reviewed without affecting production behavior.
You need to implement a source control practice that allows safe prompt experimentation.
What should you do?
Answer: B
Explanation:
Creating a feature Git branch for each prompt change is an excellent and highly recommended action.
Prompts are code. Managing them through a standard software development lifecycle prevents production failures and allows for safe experimentation.
Isolation: Changes stay completely separate from the working production version.
Collaboration: Teams can review prompt tweaks together via Pull Requests (PRs).
Traceability: You can easily see who changed a prompt, when, and why.
Rollbacks: If a prompt performs poorly, you can instantly revert to a prior Git commit.
Reference:
https://mirascope.com/blog/prompt-management-system
NEW QUESTION # 145
A data science team completes multiple training runs within an experiment by using MLflow.
The team wants to store a selected model in Azure Machine Learning so that it can be versioned and deployed later.
The model must be versioned centrally for reuse across environments.
You need to version the trained model.
Which two actions should you perform? Each correct answer presents part of the solution. NOTE: Each correct selection is worth one point. Choose two .
Answer: B,D
Explanation:
MLflow training runs produce model artifacts - the serialized model files, conda environment, and MLmodel specification - stored in the run ' s outputs folder. These artifacts are transient run outputs but are not yet a versioned, named model that can be deployed. To make the model a first-class, versioned, deployable artifact, you must explicitly register it. Locating artifacts from the run (action A) is necessary because you need the run ' s artifact URI, typically in the form runs:/run_id/model, to register from.
Registering in the AML workspace (action B) creates an entry in the model registry with a name and auto- incremented version, making the model discoverable, governable, and deployable across environments.
Tagging the experiment (option C) does not version the model. Exporting to local storage (option D) removes the model from Azure ML ' s managed infrastructure, losing lineage and governance.
Microsoft Learn Reference Topic: Register MLflow models in the Azure Machine Learning model registry
NEW QUESTION # 146
Hotspot Question
You manage an Azure Machine Learning workspace. You create an experiment named experiment1 by using the Azure Machine Learning Python SDK v2 and MLflow.
You are reviewing the results of experiment1 by using the following code segment:
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:
Explanation:
Box 1: No
Filter runs by status
You can also filter experiment by status. It becomes useful to find runs that are running, completed, canceled or failed.
Failed:The job/run has completed with errors.
KILLED: The job/run has been canceled or killed by the user/system.
The following example shows all the completed runs:
runs = mlflow.search_runs(experiment_ids=[ "1234-5678-90AB-CDEFG" ])
runs[runs.status == "FINISHED"]
Box 2: No
Ordering runs
By default, experiments are ordered descending by start_time, which is the time the experiment was queue in Azure Machine Learning.
However, here we have order_by=["start_time ASC"]. The five oldest experiments are returned.
Use the argument max_results from search_runs to limit the number of runs returned.
Box 3: No
Failed: The job/run has completed with errors.
KILLED: The job/run has been canceled or killed by the user/system.
Box 4: No
Getting metrics, parameters, artifacts and models
The method search_runs returns a Pandas Dataframe containing a limited amount of information by default. You can get Python objects if needed, which may be useful to get details about them.
Use the output_format parameter to control how output is returned:
runs = mlflow.search_runs(
experiment_ids=[ "1234-5678-90AB-CDEFG" ],
filter_string="params.num_boost_round='100'",
output_format="list",
)
Details can then be accessed from the info member. The following sample shows how to get the run_id:
last_run = runs[-1]
print("Last run ID:", last_run.info.run_id)
Reference:
https://learn.microsoft.com/en-us/azure/machine-learning/how-to-track-experiments-mlflow
NEW QUESTION # 147
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