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| Section | Objectives |
|---|---|
| Develop Generative AI Applications and Agents | - Azure OpenAI Service integration
|
| Knowledge Mining and Information Retrieval | - Azure AI Search configuration - Indexing and semantic search - RAG (Retrieval Augmented Generation) patterns |
| Implement Computer Vision Solutions | - OCR and document intelligence - Image classification and object detection |
| Implement Natural Language Processing Solutions | - Language understanding and intent recognition - Translation and multilingual support - Text analytics and summarization |
| Plan and Manage Azure AI Solutions | - Azure AI resource provisioning and configuration - Responsible AI principles and governance - Model selection and lifecycle management |
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NEW QUESTION # 104
You have 10,000 ASCII files and need to identify files containing specific phrases by using cosine similarity.
Answer: D
Explanation:
Comprehensive and Detailed 150 to 250 words of Explanation From Azure AI Apps and Agents Developer/Course Guide/topics:
text-embedding-ada-002 is an embedding model . Embedding models transform text into dense numerical vectors representing semantic characteristics of the input.
The application can generate an embedding for each relevant file or text chunk and store those vectors. When a user supplies a phrase, the application generates an embedding for that phrase by using the same embedding model. It can then calculate cosine similarity between the query vector and stored vectors.
Cosine similarity measures the angular similarity between vectors. Values representing semantically similar text will generally be closer even when the exact wording differs. This makes embeddings appropriate for semantic file retrieval, clustering, similarity search, and RAG.
GPT-4, GPT-35 Turbo, and GPT-4-32k are generative language models rather than dedicated vector- embedding models. They can reason about text but are not the appropriate options for producing the vector representations required by the question.
Although newer embedding-model generations exist in current Azure model catalogs, among the choices presented, text-embedding-ada-002 is the correct model family and answer.
Study Guide alignment: Build retrieval and grounding pipelines, configure vector search, and generate representations used for semantic retrieval . Microsoft Learn
NEW QUESTION # 105
You need to configure the model deployment for Agent1 to meet the technical requirements.
What should you configure? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Deployment type: Standard
Version update policy: Once the current version expires
The correct deployment type is Standard . The case study specifies that Project1 is deployed in an EU Azure region and that model-processed data must remain within the EU. It also requires scalable, high-throughput generative AI workloads that dynamically handle variable customer support traffic without reserved throughput capacity. In Microsoft Foundry Models, Standard is a pay-per-token deployment type that processes data in a single Azure region, while Global Standard can process requests across regions and Global Provisioned uses reserved provisioned throughput. Microsoft's deployment-type guidance identifies Standard as single-region, pay-per-token, whereas Global Provisioned is cross-region with reserved capacity.
The correct version update policy is Once the current version expires . This keeps Agent1 on the selected model version during its supported lifecycle, which supports stable and consistent responses, but still preserves continuity by automatically moving to a supported replacement when the current version is retired.
Microsoft's model versioning guidance states that this policy updates only when the current model version expires, while upgrading when a new default is available changes the deployment sooner and opting out can cause the deployment to stop working after retirement. Reference topics: deployment types, regional data processing, model versioning, throughput capacity, and stable production deployments.
NEW QUESTION # 106
You have an app named App1 that uses a Microsoft Foundry multimodal model deployment.
App1 runs optical character recognition (OCR) on uploaded images and appends the OCR output to the prompt as additional context.
Some uploaded images contain embedded text.
You need to prevent potentially malicious instructions from being processed by the model.
What should you use?
Answer: B
Explanation:
The correct answer is D. prompt shields for documents . The OCR text is extracted from uploaded images and appended as additional context, so it is third-party content rather than a direct trusted user instruction.
Microsoft Foundry Prompt Shields distinguish between user prompt attacks , which are malicious instructions directly supplied as user prompts, and document attacks , which are hidden or embedded instructions in third-party content such as documents, emails, webpages, or grounded data. Microsoft also notes that Prompt Shields analyze indirect attacks embedded in input documents or images.
This scenario is an indirect prompt injection pattern: an uploaded screenshot may contain text that says to ignore the system prompt or perform unauthorized actions. Because the OCR output is being passed to the multimodal model as contextual content, the appropriate protection is Prompt Shields for documents, which scans externally sourced context for malicious embedded instructions before generation. Protected material text detects known copyrighted text in model outputs, not prompt injection. Image moderation detects harmful visual content categories, not malicious instructions in extracted context. Prompt Shields for user prompts are less precise here because the risk comes from document-like content derived from the uploaded image.
Reference topics: Prompt Shields, document attacks, indirect prompt injection, multimodal safety, OCR- derived context, and Foundry guardrails.
NEW QUESTION # 107
Note: This section contains one or more sets of questions with the same scenario and problem. Each question presents a unique solution to the problem. You must determine whether the solution meets the stated goals. More than one solution in the set might solve the problem. It is also possible that none of the solutions in the set solve the problem.
After you answer a question in this section, you will NOT be able to return. As a result, these questions do not appear on the Review Screen.
You have a Microsoft Foundry project that contains an agent. The agent generates summaries from retrieved policy documents.
Users report that some responses omit required regulatory clauses, even when the clauses are present in the retrieved content.
You need to improve response completeness.
Solution: You add a reflection pass that regenerates the response if the required clauses are missing.
Does this meet the goal?
Answer: A
Explanation:
Yes, the solution meets the goal. The problem is not retrieval availability, because the required regulatory clauses are already present in the retrieved policy documents. The failure occurs during generation: the agent produces a summary that omits required content. A reflection pass is the correct application-level control because it adds a verification step before the response is returned. The pass can compare the draft answer against the retrieved clauses, detect missing mandatory content, and trigger regeneration or revision until the summary includes the required clauses.
This aligns with Microsoft Foundry's evaluation and observability model, where generated responses are assessed for reliability, groundedness, relevance, and quality throughout the AI application lifecycle. Foundry observability guidance describes evaluation as a mechanism for measuring response quality and improving AI outputs across development and production workflows. The Azure AI evaluation SDK also defines completeness as the extent to which a generated response contains all necessary and relevant information with respect to the provided ground truth. Reflection operationalizes that quality check inside the application flow, rather than merely reporting the defect after the fact. Reference topics: model reflection, response completeness, RAG generation quality, retrieved context verification, and agent response optimization.
NEW QUESTION # 108
You have a Python application that uses Azure OpenAt structured outputs to extract fields from unstructured receipt text and support ticket text. The application uses the following schema.
Answer:
Explanation:
Explanation:
* The schema requires that a receipt has a discount_code value - No
* The schema can be used as is for Azure OpenAI structured outputs - No
* The generated JSON object will preserve the property order merchant, order_number, discount_code, line_items, and total - Yes The discount_code definition permits a nullable result. Consequently, the output can contain " discount_code
" : null when the source receipt contains no discount code. The property can be structurally present without containing a substantive discount-code value; therefore, the first statement is No .
The schema cannot be submitted unchanged. Azure OpenAI structured outputs support only a defined subset of JSON Schema. Every property declared in an object must be included in that object's required array.
Optional information must instead be represented as a required property whose type includes null. Each object must also specify " additionalProperties " : false. Furthermore, although nested anyOf constructs are supported, an anyOf construct cannot be used as the root schema. A schema that violates any of these restrictions must be revised before use, making the second statement No .
The third statement is Yes . Azure OpenAI structured outputs preserve keys in the order in which the properties are defined in the supplied schema. Therefore, the receipt object will emit merchant, order_number, discount_code, line_items, and total in that sequence. This behavior supports predictable parsing and downstream integration. The relevant curriculum area is Use Azure OpenAI in Foundry Models to generate content , including application integration and structured model responses.
NEW QUESTION # 109
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