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| Section | Objectives |
|---|---|
| Topic 1: Plan and design AI solutions using Azure AI services | - Requirements gathering and solution architecture
|
| Topic 2: Design and implement generative AI solutions | - RAG (Retrieval Augmented Generation) solutions
|
| Topic 3: Operationalizing machine learning solutions | - Deployment and monitoring
|
| Topic 4: Implement secure and scalable AI systems | - Security and governance
|
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16. Frage
Fabrikam Inc. needs to improve the performance of a GPT-5 model based on the stated technical requirements.
Which action should you perform first?
Antwort: C
Begründung:
The first action should be to evaluate the model output . Fabrikam already has evaluation data containing manually defined expected responses, and the technical requirements explicitly state that the organization must use its existing evaluation datasets based on real data with input-output pairs . Microsoft Foundry supports evaluating a model against an existing curated dataset to establish measurable baseline performance before optimization. Evaluation results can identify weaknesses in accuracy, relevance, groundedness, safety, or other quality dimensions and provide objective evidence for subsequent changes.
This sequencing is critical because Fabrikam also requires that advanced fine-tuning be applied only when prompt engineering is insufficient . Therefore, immediately fine-tuning the model would bypass the required baseline evaluation and optimization process. Fine-tuning should be driven by demonstrated performance gaps, not applied automatically.
Deploying directly to production is also inappropriate because leadership requires low operational risk and gradual rollout. Production traffic should not be used as the first evaluation mechanism when curated evaluation data already exists. Synthetic interaction data is useful when evaluation coverage is insufficient, but Microsoft specifically positions synthetic generation as an option when an adequate test dataset is unavailable. Fabrikam already has suitable evaluation data.
Study Guide Reference: Optimize generative AI systems and model performance - evaluate model performance, benchmark against curated datasets, iterate on prompts, and apply advanced fine-tuning when justified.
17. Frage
You have an Azure Machine Learning workspace named Workspace 1 Workspace! has a registered Mlflow model named model 1 with PyFunc flavor You plan to deploy model1 to an online endpoint named endpoint1 without egress connectivity by using Azure Machine learning Python SDK vl You have the following code:
You need to add a parameter to the ManagedOnllneDeployment object to ensure the model deploys successfully Solution: Add the scoring_script parameter.
Does the solution meet the goal?
Antwort: A
18. Frage
Hotspot Question
You manage a Microsoft Foundry project.
You plan to create a search index by using the Microsoft Foundry SDK.
You need to configure the Content field as a prioritized field for semantic ranking. The field is already set to be searchable.
How should you complete the item code segment? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Antwort:
Begründung:
Explanation:
Box 1: SemanticConfiguration
In the Azure AI Search / Microsoft Foundry SDK, the SemanticConfiguration class defines a specific semantic configuration (such as the name and the prioritized fields) used to optimize semantic ranking.
Box 2: SemanticField
The SemanticField class is specifically used inside the SemanticPrioritizedFields object to reference the exact name of the field (in this case, "content") that you want to prioritize for titles, keywords, or content.
Reference:
https://learn.microsoft.com/en-us/azure/search/search-get-started-semantic
19. Frage
You manage an Azure Machine Learning workspace named Workspace1 and an Azure Blob Storage accessed by using the URL https://storage1.blob.core.wmdows.net/data1.
You plan to create an Azure Blob datastore in Workspace1. The datastore must target the Blob Storage by using Azure Machine Learning Python SDK v2. Access authorization to the datastore must be limited to a specific amount of time.
You need to select the parameters of the Azure Blob Datastore class that will point to the target datastore and authorize access to it.
Which parameters should you use? To answer, select the appropriate options in the answer area NOTE: Each correct selection is worth one point.
Antwort:
Begründung:
Explanation:
20. Frage
You manage a Microsoft Foundry project.
You are developing a solution to generate content based on text and images. The solution requires the ability to manage high-volume processing and avoid disruptions to the online workloads.
You need to deploy the solution.
Which deployment type and large language model (LLM) should you use? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Antwort:
Begründung:
Explanation:
LLM model: GPT-4o
Deployment type: Global batch
GPT-4o is the correct model because it is a multimodal model capable of processing both text and image inputs while generating text-based responses. This directly satisfies the requirement to generate content based on a combination of text and images. DALL-E is primarily designed for image generation rather than general multimodal language processing, while Whisper is designed for speech recognition and audio transcription.
Global batch is the correct deployment type because Microsoft specifically positions it for large-scale, high- volume asynchronous processing . Global Batch accepts large groups of requests and processes them using a separate enqueued-token quota. Critically, Microsoft states that this separate quota helps ensure that batch processing does not disrupt online workloads , which directly matches the scenario. It also provides a target turnaround of approximately 24 hours and is optimized for use cases such as large-scale content generation.
Standard deployments are better suited to interactive or lower-volume inference. Global provisioned deployments provide reserved predictable throughput, but they do not provide the specific workload isolation benefit described for asynchronous Global Batch processing.
Study Guide Reference: Design and implement a GenAIOps infrastructure - multimodal model selection, Azure OpenAI deployment types, batch inference, throughput management, and workload isolation.
21. Frage
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