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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Implement Decision Support Solutions | 10-15% | - Monitor and optimize decision support solutions - Design and implement decision automation - Integrate Azure AI services into applications |
| Topic 2: Implement Computer Vision Solutions | 15-20% | - Analyze images using Azure AI Vision - Implement object detection and image classification - Process video and generate insights - Implement Azure AI Face service - Read and process documents using Azure AI Document Intelligence |
| Topic 3: Implement Generative AI Solutions | 10-15% | - Apply responsible AI practices - Implement prompt engineering techniques - Integrate Azure OpenAI models into applications - Use Azure OpenAI Service for generative AI |
| Topic 4: Implement Knowledge Mining and Document Intelligence Solutions | 15-20% | - Create custom skills for Azure AI Search - Implement semantic and vector search - Implement Azure AI Search solutions - Design document processing pipelines |
| Topic 5: Implement Natural Language Processing Solutions | 20-25% | - Analyze text using Azure AI Language - Create conversational AI solutions with Azure AI Bot Service - Translate text and speech - Implement speech capabilities using Azure AI Speech - Build question answering solutions |
| Topic 6: Plan and Manage an Azure AI Solution | 15-20% | - Manage AI costs and resource allocation - Implement governance and compliance for AI solutions - Plan and configure secure AI solutions - Select appropriate Azure AI services and technologies |
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NEW QUESTION # 254
You have an Azure subscription that contains an Azure Al Content Safety resource named CS1.
You create a test image that contains a circle.
You submit the test image to CS1 by using the curl command and the following command-line parameters.
What should you expect as the output?
Answer: B
Explanation:
You are analyzing an image with Azure AI Content Safety using the following request payload:
{
" image " : {
" content " : " < base_64_string > "
},
" categories " : [
" Violence "
],
" outputType " : " EightSeverityLevels "
}
* Category specified # " Violence "
* The Content Safety API will check for violent content in the image.
* Test image description # A simple circle.
* This is not violent in any way.
* Output type # " EightSeverityLevels "
* This setting returns a floating-point severity score from 0.0 (no severity) up to 7.0 (highest severity).
* Since the image is only a circle (non-violent), the violence severity level will be at the lowest possible value: 0.0 Key Points: Expected result:
The Answer: B. 0.0
* Azure AI Content Safety - Image moderation
* Content Safety severity levels
Microsoft References
NEW QUESTION # 255
Select the answer that correctly completes the sentence.
Answer:
Explanation:
Explanation:
Azure Synapse Analytics (formerly Azure SQL Data Warehouse) uses a massively parallel processing (MPP) architecture for big data and analytics workloads.
* Control Node:
* Acts as the brain of the system.
* Receives queries, optimizes them, and then coordinates execution.
* Does not perform large-scale data crunching itself.
* Compute Nodes:
* Perform the actual parallel execution of queries.
* Data is distributed across these nodes.
* The MPP engine breaks down queries into smaller tasks and distributes them to compute nodes for fast, parallel execution.
* Distributes processing across compute nodes # Correct. This is the essence of MPP.
* Distributes processing across control nodes # Incorrect. The control node does not process queries in parallel; it coordinates them.
* Redirects client connections across compute nodes # Incorrect. Client connections go to the control node, not directly to compute nodes.
* Redirects client connections across control nodes # Incorrect. There is typically one control node per Synapse instance.
Review of Answer Options:
The Answer: Distributes processing across compute nodes
* Azure Synapse Analytics architecture
* Distributed query processing in Azure Synapse
Microsoft References
NEW QUESTION # 256
You have a Language Understanding solution that runs in a Docker container.
You download the Language Understanding container image from the Microsoft Container Registry (MCR).
You need to deploy the container image to a host computer.
Which three 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
NEW QUESTION # 257
You run the following command.
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
Answer:
Explanation:
Explanation:
Box 1: Yes
http://localhost:5000/status
Also requested with GET, this verifies if the api-key used to start the container is valid without causing an endpoint query.
Box 2: Yes
The command saves container and LUIS logs to output mount at C:\output, located on container host Box 3: Yes
The container provides a full set of documentation for the endpoints and a Try it out feature. With this feature, you can enter your settings into a web-based HTML form and make the query without having to write any code. After the query returns, an example CURL command is provided to demonstrate the HTTP headers and body format that
' s required.
Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/luis/luis-container-howto
NEW QUESTION # 258
You build a custom Form Recognizer model.
You receive sample files to use for training the model as shown in the following table.
Which three files can you use to train the model? Each correct answer presents a complete solution. (Choose three.) NOTE: Each correct selection is worth one point.
Answer: B,C,F
Explanation:
This question is about selecting valid training files for a custom Form Recognizer model in Azure AI Document Intelligence (formerly Form Recognizer).
Supported File Formats for Training
According to Microsoft documentation, Form Recognizer supports the following for training and analysis:
PDF (# 500 pages, # 50 MB for training)
JPEG/JPG (# 50 MB)
PNG (# 50 MB)
TIFF (# 50 MB)
Unsupported:
MP4 # Video not supported.
GIF # Not supported.
Maximum File Size Rules (for training input files):
Images (JPG, PNG, TIFF): # 50 MB
PDFs: # 50 MB
Note: 500 pages limit applies to PDFs.
File-by-File Analysis
File1 - PDF - 20 MB
Type supported.
Size within limit ( < 50 MB).
Valid #
File2 - MP4 - 100 MB
MP4 is not supported.
Invalid #
File3 - JPG - 20 MB
Type supported.
Size within limit ( < 50 MB).
Valid #
File4 - PDF - 100 MB
Type supported.
Size exceeds the 50 MB maximum limit.
Invalid #
File5 - GIF - 1 MB
GIF format not supported.
Invalid #
File6 - JPG - 40 MB
Type supported.
Size within limit ( < 50 MB).
Valid #
The answer:
A). File1, C. File3, F. File6
Microsoft References
Azure AI Document Intelligence (Form Recognizer) - Supported file formats Train a custom model with Azure Form Recognizer
NEW QUESTION # 259
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