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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Implement generative AI and agentic solutions | 30–35% | - Design and implement intelligent agents
|
| Topic 2: Implement computer vision solutions | 10–15% | - Implement image analysis and processing
|
| Topic 3: Implement information extraction and knowledge mining | 10–15% | - Extract structured data from documents
|
| Topic 4: Implement text and speech analysis solutions | 10–15% | - Implement natural language processing
|
| Topic 5: Plan and manage Azure AI solutions | 25–30% | - Design Azure AI infrastructure
|
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NEW QUESTION # 101
A custom NER model uses one ContactInfo entity for phone numbers, email addresses, and social-media handles. Precision is low.
What should you do before retraining?
Answer: D
Explanation:
Comprehensive and Detailed 150 to 250 words of Explanation From Azure AI Apps and Agents Developer/Course Guide/topics:
The existing ContactInfo entity combines several distinct entity patterns into one heterogeneous label. Phone numbers, email addresses, and social-media handles have substantially different syntax, contextual clues, and boundary patterns.
Splitting the broad entity into Phone , Email , and SocialMedia gives the custom NER model a clearer and more internally consistent training signal. Every existing tagged span should then be relabeled using the correct specialized entity class before retraining.
This can improve precision because the model no longer needs to learn one extremely broad decision boundary that covers unrelated forms of contact data. More precise labels also help the model learn the correct start and end boundaries of each entity, addressing the reported false positives in which adjacent text is included.
Adding more examples under the same ambiguous ContactInfo schema may reinforce the underlying modeling ambiguity. Auto-labeling reduces annotation effort but does not correct the schema problem.
Lowering a confidence threshold would generally cause more uncertain predictions to be accepted, tending to lower rather than improve precision.
Study Guide alignment: implement solutions that extract entities and customize language-model outputs for domain-specific extraction tasks . Microsoft Learn
NEW QUESTION # 102
In Azure AI Studio, you use Completions playground with the GPT-35 Turbo model.
You have a prompt that contains the following code.
function F(n)
{
var f = [0, 1];
for (var i = 2; i < n; i++) f[i] = f[i-1] + f[i-2];
return f;
}
You need the model to create an explanation of the code. The solution must minimize costs.
What should you do?
Answer: A
Explanation:
By adding a comment like // what does function F do? to the prompt, you are instructing the GPT model to provide an explanation of the function's behavior. This approach directly asks the model to explain the code, which will help generate an explanation without requiring changes to the model or additional resource-intensive configurations.
NEW QUESTION # 103
You have a Microsoft Foundry project that ingests scanned PDF invoices stored in Azure Blob Storage. Each invoice contains printed line items and has a table-based layout.
Extracted results are stored as structured JSON and used as grounding data for an agent in a Retrieval Augmented Generation (RAG) solution.
You need to create a single analyzer that meets the following requirements:
* Extracts the invoice number, invoice date, vendor name, and total amount across varying templates
* Returns confidence scores so that results with confidence below 0.80 can be routed for supervisor review What should you use?
Answer: B
Explanation:
The correct answer is C because the requirement is structured field extraction from invoices across varying templates, not only OCR or layout preservation. Azure Content Understanding analyzers are reusable configurations that combine content extraction, AI-powered analysis, and structured data output, and Microsoft states that custom analyzers can be created for specific extraction needs. In this case, the analyzer schema should define fields such as invoice number, invoice date, vendor name, and total amount so the output can be returned as structured JSON for downstream RAG grounding.
The confidence-routing requirement also points to Content Understanding field confidence scores. Microsoft documentation states that every field can include a confidence score from 0 to 1, and that confidence scores can be used to automate high-confidence results while routing low-confidence results for human review. A threshold such as 0.80 is therefore an application routing rule based on the returned field confidence. The prebuilt-layout analyzer preserves layout but does not define invoice-specific business fields. Groundedness guardrails evaluate generated answers, not invoice field extraction. Azure AI Search search.score measures retrieval relevance, not extraction confidence. Reference topics: Content Understanding custom analyzers, document field extraction, structured JSON output, confidence scoring, and RAG grounding.
NEW QUESTION # 104
You have a Microsoft Foundry project that contains an internal Q & A agent.
Users report the following issues when they ask the agent questions:
* An increase in the following response: "No relevant information found"
* Periodic HTTP 429 rate limit exceeded errors during peak hours
You need to identify whether each issue is caused by model unavailability, resource limits, or inference failures.
What should you do? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Metrics to enable: Model Availability Rate and Provisioned Utilization
Diagnostic log to collect: trace
The correct metrics are Model Availability Rate and Provisioned Utilization . Model Availability Rate identifies whether failures are caused by service-side model unavailability because it is calculated from total calls minus server errors. Provisioned Utilization identifies resource-limit conditions because Microsoft states that when utilization reaches or exceeds 100%, calls are throttled and HTTP 429 errors are returned. This directly maps to the reported peak-hour rate-limit errors.
The correct diagnostic log is trace . For an internal Q & A scenario, trace logging is required to analyze question-answering behavior, including unanswered questions. Microsoft's custom question answering analytics guidance states that diagnostic logging stores telemetry and chat logs and instructs enabling Trace in addition to Audit, RequestResponse, and AllMetrics. The sample Kusto queries for unanswered questions inspect the answer, question, score, and knowledge base ID, and filter unanswered results by a score of zero.
RequestResponse is useful for request status and latency, and Audit is for administrative operations, but neither is the best diagnostic category for analyzing Q & A inference behavior such as "No relevant information found." Reference topics: Foundry monitoring, model availability, provisioned utilization, diagnostic logging, custom question answering analytics, and trace logs.
NEW QUESTION # 105
Drag and Drop Question
You have a Microsoft Foundry project that uses Azure Content Understanding in Foundry Tools to analyze marketing videos.
Video segmentation is enabled.
You need to configure an analyzer to output a generated JSON field that describes the color scheme of each video segment.
How should you configure the analyzer? To answer, drag the appropriate values to the correct targets. Each value may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Box 1: string
The Type (string): While you want the output schema to contain a description of the color palette or style, Azure Content Understanding schemas map the underlying data types using fundamental types like string, number, or array. The field will output the final result in the JSON response under a key like "valueString" Box 2: generate The Method (generate): The extract method is only used for pulling literal text exactly as it appears in content (such as speech-to-text transcripts or OCR). Because analyzing visual aesthetics and synthesizing descriptive text about a "color scheme" requires multimodal AI reasoning, you must use the generate method. This instructs the underlying model to analyze the segment and synthesize a completely new descriptive insight.
References:
https://learn.microsoft.com/en-us/azure/ai-services/content-understanding/video/elements
NEW QUESTION # 106
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