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| Section | Objectives |
|---|---|
| Optimize generative AI systems and model performance | - Implement cost management and scaling strategies for GenAI workloads - Tune prompts, system messages, and grounding strategies - Optimize inference performance, caching, and throughput - Fine-tune and distill models for specific use cases |
| Implement machine learning model lifecycle and operations | - Train, register, and version models using Azure Machine Learning - Retrain, update, and manage model versions in production - Monitor model performance, data drift, and operational health - Deploy models to real-time and batch endpoints |
| Design and implement a GenAIOps infrastructure | - Manage API keys, rate limits, and responsible AI guardrails - Configure prompt orchestration, prompt flows, and agent frameworks - Implement RAG (Retrieval-Augmented Generation) pipelines and vector search - Set up Microsoft Foundry and Azure AI services for generative AI workloads |
| Design and implement an MLOps infrastructure | - Set up Azure Machine Learning workspace and compute targets - Implement security, governance, and compliance for MLOps - Manage environments, data stores, and model registries - Configure source control, CI/CD pipelines, and automation for ML workflows |
| Implement generative AI quality assurance and observability | - Implement logging, tracing, and telemetry for GenAI applications - Evaluate generative AI outputs for quality, safety, and grounding - Conduct red teaming, adversarial testing, and content filtering - Monitor latency, token usage, cost, and error rates |
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NEW QUESTION # 151
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.
Answer:
Explanation:
Explanation:
NEW QUESTION # 152
Hotspot Question
You have an Azure Machine Learning workspace and a collection of image files stored in two Azure Blob Storage accounts.
You need to configure data asset properties.
Which values should you use in your configuration? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Box 1: uri_folder
The best data asset type for this scenario is a File data asset (uri_file or uri_folder).
In Azure Machine Learning, image files used for training computer vision models are best managed as file-based data assets because machine learning frameworks read individual images directly from storage paths rather than tabular rows.
Preserves Formats: Keeps images in their native formats (PNG, JPEG).Direct Access: Allows training scripts to mount or download files easily.
*-> Folder Mapping: A uri_folder references the entire directory containing your images.
Efficiency: Avoids the overhead of parsing unstructured binary data into a table.
Box 2: azureml
To point a uri_folder data asset to your Azure Blob Storage locations in Azure Machine Learning, you should use the azureml:// URI scheme.
This is the recommended, modern standard that leverages Azure ML datastores for secure tokenless access.
Alternatively, you can use direct Azure Storage URI schemes depending on your configuration Recommended Scheme: Azure ML Datastore This scheme abstracts the storage credentials by referencing an Azure ML Datastore.
Format: azureml://datastores/<datastore_name>/paths/<path_to_folder>/
Example: azureml://datastores/myblobdatastore/paths/images/training_set/ Incorrect:
[not wasbs]
Azure Blob Storage (wasbs)
Used when connecting via the legacy Windows Azure Storage Blob driver.
Format:
wasbs://<container_name>@<storage_account_name>.blob.core.windows.net/<path_to_folder>/
[Not abfss]
Azure Data Lake Storage Gen2 (abfss)
Used if your Blob Storage accounts have the hierarchical namespace enabled.
Format:
abfss://<container_name>@<storage_account_name>.dfs.core.windows.net/<path_to_folder>/ Reference:
https://learn.microsoft.com/en-us/azure/machine-learning/how-to-create-data-assets
NEW QUESTION # 153
You create an Azure Machine Learning workspace. The workspace contains a dataset named sample.dataset, a compute instance, and a compute cluster. You must create a two-stage pipeline that will prepare data in the dataset and then train and register a model based on the prepared data. The first stage of the pipeline contains the following code:
You need to identify the location containing the output of the first stage of the script that you can use as input for the second stage. Which storage location should you use?
Answer: B
NEW QUESTION # 154
You have a deployment of an Azure OpenAI Service base model.
You plan to fine-tune the model.
You need to prepare a file that contains training data.
Which file format should you use?
Answer: D
Explanation:
Azure OpenAI Service fine-tuning requires training data in JSON Lines (JSONL) format - a text file where each line is a self-contained, valid JSON object representing one training example. For chat fine-tuning, each line contains a messages array with system, user, and assistant turns. JSONL is ideal for machine-learning datasets because it is streamable, easy to generate programmatically, and directly supported by the Azure OpenAI fine-tuning API. CSV (option A) and TSV (option B) are tabular formats that cannot natively represent the nested, multi-turn conversation structure required by chat models. Plain JSON (option D) would require loading the entire file as a single object, which is impractical for large datasets and is not accepted by the Azure OpenAI fine-tuning endpoint. Always use JSONL with UTF-8 encoding for Azure OpenAI training files.
Microsoft Learn Reference Topic: Prepare training data for Azure OpenAI fine-tuning - JSONL file format
NEW QUESTION # 155
An organization uses Microsoft Foundry to develop generative AI projects that access shared Azure resources such as storage accounts and vector databases.
The organization s security policy requires eliminating secret key-based authentication and enforcing least- privilege access.
You must configure identity and access so that:
Services authenticate without stored credentials.
Permissions are scoped appropriately across projects and shared resources.
You need to configure the appropriate identity or access mechanism for each requirement.
What should you configure in Microsoft Foundry to meet each requirement? To answer, move the appropriate configuration mechanisms to the correct requirements. You may use each configuration mechanism once, more than once, or not at all. You may need to move the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Managed Identity is Azure ' s solution for eliminating stored secrets and API keys from application configurations. When a Microsoft Foundry resource or its dependent compute has a system-assigned or user- assigned managed identity, it can authenticate to Azure services such as Storage, Key Vault, and Cognitive Services using Azure Entra ID tokens that are automatically issued and rotated by Azure. There are no keys to store, rotate, or accidentally expose in code or configuration files, directly satisfying the requirement to authenticate without stored credentials. Role-Based Access Control (RBAC) is the Azure governance mechanism for scoping permissions. By assigning specific built-in roles to the managed identity or to user and group principals at the correct scope - subscription, resource group, resource, or project level - the organization can enforce least-privilege access across different projects and shared resources.
Microsoft Learn Reference Topic: Configure managed identities and RBAC for Microsoft Azure AI Foundry resources
NEW QUESTION # 156
......
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