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| Section | Objectives |
|---|---|
| Topic 1: Operationalizing machine learning solutions | - Deployment and monitoring
|
| Topic 2: Design and implement generative AI solutions | - RAG (Retrieval Augmented Generation) solutions
|
| Topic 3: Plan and design AI solutions using Azure AI services | - Requirements gathering and solution architecture
|
| Topic 4: Implement secure and scalable AI systems | - Scalability and performance optimization
|
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NEW QUESTION # 27
-
A team is standardizing MLOps practices by using automated deployments.
The team requires infrastructure to be defined declaratively and deployed through automation pipelines.
You need to configure infrastructure deployment.
What should you configure for each requirement? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Deploy resources from a pipeline: Azure CLI Commands
Define Azure resources declaratively: Bicep templates
Azure CLI Commands should be used to deploy resources from an automation pipeline. Azure Machine Learning supports integration with CI/CD platforms such as Azure DevOps and GitHub Actions, and Microsoft documents the Azure CLI with the Machine Learning extension as a standard mechanism for automating resource provisioning, training pipelines, model deployment, and other MLOps operations. Azure CLI commands can be executed non-interactively within pipeline stages, making them appropriate for repeatable automated deployments.
Bicep templates should be used to define Azure infrastructure declaratively. Bicep is Microsoft ' s domain- specific Infrastructure-as-Code language for Azure Resource Manager. Instead of specifying individual imperative provisioning steps, a Bicep file describes the desired state of Azure resources. Azure Resource Manager then determines the deployment operations required to reach that state. This provides repeatable, version-controlled, consistent infrastructure across development, testing, and production environments.
Bicep deployments can also be invoked directly through Azure CLI commands such as az deployment group create, allowing the declarative infrastructure definition and automated deployment mechanism to work together in an MLOps CI/CD pipeline.
Study Guide Reference: Design and implement an MLOps infrastructure - Infrastructure as Code, Bicep, Azure CLI, automated deployment pipelines, and reproducible environment provisioning.
NEW QUESTION # 28
A team is building a Retrieval-Augmented Generation (RAG) system.
The team observes that the retrieved documents are often irrelevant or incomplete.
You need to improve retrieval accuracy.
What should you adjust?
Answer: B
Explanation:
To best improve retrieval accuracy when documents are irrelevant or incomplete, you should adjust Chunk size and overlap.
Fixes Irrelevant Retrievals: If your chunks are too large, they pack in multiple unrelated topics.
This adds noise, dilutes the semantic vector, and causes the system to pull in massive blocks of text where only a single sentence was actually relevant. Tuning the size ensures chunks stay highly specific.
Fixes Incomplete Retrievals: If your chunks are too small or lack sufficient overlap, vital context gets completely cut off at the boundaries. Adding an optimal overlap (typically 10%-20%) ensures that a sentence split across a boundary preserves its surrounding meaning, keeping the context whole.
Incorrect:
[Not B]
Temperature parameter: This is a generative LLM setting that dictates the creativity or randomness of the output text. It has absolutely zero control over the vector search database or the specific documents picked during the initial retrieval phase.
[Not C]¨
Token limits: This caps how much context you can pass into the prompt window or how much text the model can generate. While a higher limit lets you pass more documents, it does not magically fix the underlying data quality or prevent the retrieval algorithm from pulling in wrong files.
[Not D]
Embedding strategy: While choosing a stronger model or fine-tuning embeddings changes how text is numerically represented, it cannot overcome poorly structured data. Even the best embedding model cannot successfully capture semantic meaning if your data ingestion pipeline is feeding it randomly sliced, incomplete paragraphs Reference:
https://pmc.ncbi.nlm.nih.gov/articles/PMC12649634/
NEW QUESTION # 29
A team validates a generative AI application that produces free-form text responses by using Microsoft Foundry SDK.
The evaluation dataset is registered in the Microsoft Foundry environment.
You need to configure a safety evaluation pipeline that reliably evaluates model outputs for harmful content.
Which three actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.
Answer:
Explanation:
Explanation:
Correct sequence:
* Install the Foundry SDK project client locally.
* Configure safety evaluators.
* Submit an evaluation to the Microsoft Foundry project in the cloud.
The first step is to install and configure the Microsoft Foundry SDK project client . Microsoft documents the Foundry project client as the programmatic entry point for authenticating to a Foundry project and accessing its evaluation capabilities. The client typically uses DefaultAzureCredential, avoiding embedded credentials while enabling access to project resources.
Next, configure the safety evaluators that correspond to the risks that must be measured. Microsoft Foundry provides built-in safety evaluators for categories including Violence, Sexual content, Self-harm, and Hate
/Unfairness . These evaluators analyze generated responses and return structured safety assessments rather than relying on subjective manual review.
Finally, submit the evaluation to the Microsoft Foundry project in the cloud . Current Foundry evaluation workflows define the evaluator configuration, create an evaluation, and start an evaluation run in the project.
Results are persisted in Foundry for comparison, auditing, and CI/CD quality gates.
Uploading evaluation data is unnecessary because the scenario explicitly states that the dataset is already registered . Microsoft documentation specifically instructs users to skip dataset upload when a registered dataset already exists. Free-form text upload is also inappropriate because structured evaluation datasets use supported schemas such as JSONL or CSV.
Study Guide Reference: Implement generative AI quality assurance and observability - Foundry evaluations, safety evaluators, evaluation datasets, cloud evaluation runs, and harmful-content measurement.
NEW QUESTION # 30
-
You review the following Azure CLI command and the relevant Bicep excerpt.
(Non-relevant sections are omitted.)
You need to validate what the snippet will do before it is merged. 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:
The command deploys the resources into an existing resource group named rg-foundry-dev: Yes The system-assigned managed identity defined in the template will automatically be inherited by all Microsoft Foundry projects: No To deploy this template to a different subscription, you must modify the Bicep file to include a subscriptionId parameter: No The first statement is Yes . The command uses az deployment group create --resource-group rg-foundry-dev, which performs an Azure Resource Manager deployment at resource-group scope . Microsoft documents that the target resource group must already exist; if it does not, it must be created before running the resource- group deployment.
The second statement is No . The Bicep declaration assigns a system-assigned managed identity to the Foundry resource itself . A Microsoft Foundry project is a child resource and can have its own managed identity . Microsoft explicitly shows project creation with " identity " : { " type " : " SystemAssigned " } and separately discusses assigning permissions to a project ' s managed identity. Therefore, the parent ' s system- assigned identity is not automatically inherited as the identity of every project.
The third statement is No . Because targetScope = ' resourceGroup ' , the same Bicep template can be deployed to a resource group in another subscription without adding a subscriptionId parameter. Azure CLI supports the global --subscription argument, or the active subscription can be changed with az account set.
NEW QUESTION # 31
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 # 32
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