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| Section | Weight | Objectives |
|---|---|---|
| Implement text and speech analysis solutions | 10–15% | - Implement natural language processing
|
| Plan and manage Azure AI solutions | 25–30% | - Manage AI solution development lifecycle
|
| Implement information extraction and knowledge mining | 10–15% | - Build knowledge bases and search solutions
|
| Implement generative AI and agentic solutions | 30–35% | - Design and implement intelligent agents
|
| Implement computer vision solutions | 10–15% | - Build multimodal solutions
|
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NEW QUESTION # 97
You have a factory that contains a production line.
You need to develop a solution that will analyze a video feed to detect and track the movement of employees as they work on the production line.
Which service should you include in the solution?
Answer: D
Explanation:
Azure Vision in Foundry Tools (Spatial Analysis) can be used to analyze a video feed to detect and track the movement of employees as they work on the production line. This technology processes live or recorded video to understand spatial relationships and human activity.
Key capabilities for this application include:
Person Tracking & Movement: Spatial Analysis models identify human presence by detecting a body bounding box and tracking it in real time, enabling monitoring of movement patterns throughout a facility.
Zone & Line Monitoring: You can configure the system to detect when a person enters or exits a designated zone (e.g., a specific workstation) or crosses a line.
Dwell Time Calculation: The system can measure how long an employee spends in a specific zone, which can be useful for identifying bottlenecks.
Safety and Compliance: It can monitor for safety infractions, such as workers entering prohibited areas or failing to wear protective equipment (PPE).
Efficiency Analysis: By integrating these insights into Palantir Foundry's ontology, manufacturers can track assembly processes, optimize workflows, and enhance productivity.
Reference:
https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/whats-new
NEW QUESTION # 98
You plan to configure an evaluation in Microsoft Foundry for a Retrieval Augmented Generation (RAG) chat app. You need to provide scores for groundedness, relevance, and harmful-content categories. Which two evaluation categories can you use? Each correct answer presents part of the solution.
Answer: A,C
Explanation:
Select AI quality (AI assisted) metrics for groundedness and relevance. Groundedness evaluates whether the generated answer is supported by the retrieved RAG context, while relevance evaluates whether the answer appropriately addresses the user's query. These evaluations use an AI judge model to assess meaning and contextual alignment rather than relying only on exact token matches. Microsoft Foundry categorizes groundedness and relevance as quality evaluators and requires a compatible judge-model deployment for AI- assisted evaluation.
Select risk and safety metrics to evaluate harmful-content categories. These evaluators detect and score content involving violence, sexual material, self-harm, and hate or unfairness. They provide the safety dimension required alongside the RAG quality measurements.
AI quality NLP metrics include mathematically calculated measures such as F1, BLEU, GLEU, ROUGE, and METEOR. These primarily compare token or n-gram overlap with reference answers and do not provide the requested groundedness and content-harm assessments. A fluency evaluator measures language readability, while a similarity evaluator compares a response with ground truth; neither is a complete evaluation category covering the requirements.
Study Guide alignment: evaluate generative AI solutions by using quality, groundedness, relevance, risk, and safety metrics .
NEW QUESTION # 99
A copilot must answer multi-part questions that depend on earlier turns in the conversation. You want the system to break each complex question into focused subqueries, run them in parallel, and semantically rerank the results before composing an answer. Which Azure AI Search capability provides this?
Answer: B
Explanation:
Agentic retrieval in Azure AI Search uses a large language model to decompose a complex query into focused subqueries, runs them in parallel, semantically reranks each set of results, and merges them into a unified response, while taking conversation history into account. That is precisely the behaviour the scenario describes.
NEW QUESTION # 100
You have an Azure subscription that contains an Azure OpenAI resource.
You need to build an app that will suggest product names from a given product description.
Which model should you use?
Answer: A
Explanation:
To suggest product names from a given product description, the ideal model is GPT-4. GPT-4 is a powerful language model that can understand and generate human-like text based on given inputs, making it well-suited for tasks such as generating creative suggestions like product names from descriptions.
Whisper is used for speech-to-text conversion, not for generating text suggestions.
DALL-E is designed for generating images from text prompts, not for text generation tasks like naming products.
Embeddings are used for tasks involving similarity search and semantic understanding but not for generating new text like product names.
NEW QUESTION # 101
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 # 102
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