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| Section | Objectives |
|---|---|
| Topic 1: Implement Computer Vision Solutions | - OCR and document intelligence - Image classification and object detection |
| Topic 2: Knowledge Mining and Information Retrieval | - Azure AI Search configuration - RAG (Retrieval Augmented Generation) patterns - Indexing and semantic search |
| Topic 3: Develop Generative AI Applications and Agents | - Azure OpenAI Service integration
|
| Topic 4: Plan and Manage Azure AI Solutions | - Azure AI resource provisioning and configuration - Responsible AI principles and governance - Model selection and lifecycle management |
| Topic 5: Implement Natural Language Processing Solutions | - Text analytics and summarization - Translation and multilingual support - Language understanding and intent recognition |
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NEW QUESTION # 67
A legal team must extract clauses and inferred fields from unstructured contracts. One required field is the contract end date, which is not stated explicitly and must be derived from the start date plus the term. The team has no labelled training data. Which tool best fits?
Answer: A
Explanation:
Azure Content Understanding handles unstructured documents, works zero-shot without labelled data, and can infer fields that are not explicitly present, such as deriving a contract end date from the start date and term. That matches every requirement in the scenario.
NEW QUESTION # 68
You have an application named App1 that uses Azure Speech in Foundry Tools to transcribe live calls.
Transcript segments often contain both English and Spanish. App1 sends each segment to Azure Translator in Foundry Tools to translate to another language.
Sometimes, mixed-language segments result in incomplete or incorrect translations.
You need to reduce translation errors. The solution must ensure that the entire transcript is translated successfully.
What should you do before sending the segments to Translator?
Answer: C
Explanation:
To fix incomplete or incorrect translations from mixed-language transcripts, you must identify and isolate the languages at the sentence or phrase level before sending the text to Azure Translator.
Azure Translator performs best when a single request contains only one source language. When it receives a mixed-language segment under a single source language code, it often fails to parse the secondary language correctly.
Here is the step-by-step pipeline you should implement inside your application before hitting the Translator API.
1. Split Segments into Sentences
Live call transcript segments can contain multiple sentences. Do not send the raw, multi-sentence segment directly to the translator if it contains mixed languages.Break the segment into individual sentences.Use regex punctuation rules or a lightweight sentence-splitting library.
2. Run Language Detection Per Sentence
Azure Translator has a built-in Detect API, but for mixed-language live calls, executing a dedicated detection step per sentence provides better control.
Reference:
https://medium.com/neural-engineer/azure-ai-speech-to-text-real-time-transcription-58bfd5fd1a28
NEW QUESTION # 69
You have an app that uses Azure Al Custom Vision and a custom trained classifier to identify products in images. You need to add new products to the classifier. The solution must meet the following requirements:
* Minimize how long it takes to add the products.
* Minimize development effort.
Which five 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:
Because the existing classifier is an Azure AI Custom Vision project, the lowest-effort workflow is performed directly in the Custom Vision portal , not Azure Machine Learning studio or Vision Studio. Open the existing project so that the new product classes can be incorporated into its current training dataset.
Next, upload representative sample images of each new product. The images should include sufficient variation in viewing angle, lighting, background, size, and presentation to support reliable classification.
Label each image with the appropriate new product tag. Custom Vision uses these tags as the target classes during supervised training. Microsoft's portal workflow explicitly supports uploading and manually tagging images without requiring application code.
Retrain the classifier after the labeled images have been added. Training creates a new iteration based on all current training images and tags, including the newly introduced product categories.
Finally, publish the trained iteration to the prediction resource. Publishing makes that iteration available through the Custom Vision Prediction API used by the application.
Study Guide alignment: create, train, evaluate, retrain, publish, and consume custom image- classification models .
NEW QUESTION # 70
You have an application that processes support case notes by using Azure OpenAi in Microsoft Foundry Models. The application has the following request definitions.
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:
request_a calls the embeddings endpoint and specifies text-embedding-ada-002. An embedding request converts the supplied source_text into a numeric vector representation, so the first statement is Yes . Such vectors are used for semantic similarity, vector search, clustering, and Retrieval Augmented Generation.
request_b calls Chat Completions and includes a response_format based on a JSON schema named case_summary. Its schema defines summary, keyRisks, and nextActions, so the requested response is structured JSON. Therefore, the second statement is Yes .
The third statement is No . Although keyRisks is defined as an array and is required in the output shape, an array can legitimately be empty. If the support case contains no relevant risk, the structurally valid value may be " keyRisks " : []. The schema ensures the property exists; it does not guarantee the source content contains a substantive risk.
For current strict structured-output implementations, Microsoft also requires schemas to comply with the supported JSON Schema subset, including appropriate strict object definitions.
Study Guide alignment: integrate generative models into applications, implement vector embeddings, and generate structured JSON outputs . Microsoft Learn
NEW QUESTION # 71
You have a Microsoft Foundry resource named Al1 that hosts three deployments of the GPT 3.5 model. Each deployment is optimized for a unique workload.
You plan to deploy three apps. Each app will access AM by using the REST API and will use the deployment that was optimized for the apps intended workload.
You need to provide each app with access to All and the appropriate deployment. The solution must ensure that only the apps can access AH.
What should you use to provide access to AI1. and what should each app use to connect to its appropriate deployment? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Use Microsoft Entra ID authentication and pass an OAuth 2.0 bearer token in the REST request's Authorization header. Assign the required inference role, such as Cognitive Services User , only to each application's managed identity or service principal. This authenticates the applications as distinct security principals and supports resource-scoped RBAC, auditing, token expiration, and immediate access revocation.
Microsoft recommends Entra ID for production workloads because API keys are static, resource-wide secrets that cannot identify individual callers or provide granular authorization.
Each application must specify its appropriate deployment name when making an inference request. Foundry deployments assign a unique name and configuration to each deployed model. In the deployment-level REST API, that name appears in the request route:
/openai/deployments/{deployment-name}/chat/completions
The resource endpoint and authentication mechanism can remain common across all three applications, while the deployment name directs each request to the GPT-3.5 deployment optimized for that application's workload. A deployment type describes capacity or provisioning characteristics and does not select a particular deployed model.
Study Guide alignment: configure authentication, managed identities, RBAC, model deployments, and REST-based model consumption.
NEW QUESTION # 72
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