The requirement is:
" Measure the public perception of your brand on social media using natural language processing (NLP). " A). Content Moderator Designed to filter offensive or inappropriate content in text, images, and videos.
Not suitable for sentiment analysis or measuring perception.
B). Form Recognizer
Extracts structured data from documents (invoices, receipts, forms).
Not used for analyzing social media posts.
C). Computer Vision
Analyzes images and videos for object detection, image classification, OCR, etc.
Not used for text-based sentiment analysis.
D). Language service #
Provides natural language processing (NLP) capabilities such as sentiment analysis, opinion mining, key phrase extraction, entity recognition , and language detection .
Perfect fit for analyzing public perception from social media posts .

Explanation:
Stream processing has access to the most recent data received or data within a rolling time window. # Yes
Batch processing must occur immediately and have latency in the order of seconds or milliseconds. # No Stream processing is used for simple response functions, aggregates, or calculations such as rolling averages. # Yes
Comprehensive Detailed Explanation
Stream processing has access to the most recent data received or data within a rolling time window.
Correct.
Stream processing handles real-time or near real-time data.
It operates on a continuous data stream and often uses a rolling time window for analytics.
The answer: Yes
Batch processing must occur immediately and have latency in the order of seconds or milliseconds.
Incorrect.
Batch processing is designed for large volumes of data collected over time.
It typically has high latency (minutes, hours, or even days), not immediate execution.
The answer: No
Stream processing is used for simple response functions, aggregates, or calculations such as rolling averages.
Correct.
Common stream analytics tasks include event-driven responses, real-time aggregations, anomaly detection, and rolling averages.
The answer: Yes
Correct Answers:
Yes
No
Yes
Microsoft References
Batch vs. Stream processing in Azure
Azure Stream Analytics overview

Scenario Recap
You have a web app app1 running on VM1 in vnet1.
You plan to create an Azure Cognitive Search service (service1).
Goal: Ensure app1 can connect to service1 without routing over the public internet.
Proposed Solution
"You deploy service1 and a public endpoint to a new virtual network, and you configure Azure Private Link." Analysis Azure Cognitive Search and Private Link:
Azure Cognitive Search supports Private Endpoints via Azure Private Link.
When you configure a private endpoint, the service is accessible privately over the virtual network and traffic does not go over the public internet.
You can still keep a public endpoint enabled optionally, but access from your VM can be restricted to the private endpoint.
Key Point: The solution specifies using a public endpoint AND a private link.
This still meets the requirement because Private Link ensures that VM1 in vnet1 communicates with service1 privately.
The existence of the public endpoint does not force traffic to go public - traffic from the private endpoint is routed over the VNet.
Conclusion
Yes, this solution meets the goal because Private Link provides secure, private connectivity to Cognitive Search, avoiding the public internet.
The answer: A. Yes
Microsoft References
Secure access to Azure Cognitive Search with Private Link
Azure Private Link overview

You have a chatbot built on Azure OpenAI (AI1). The requirement is that all input and output from the chatbot must be checked for objectionable content (such as hate speech, sexual, violent, or self-harm content).
To meet this requirement, Microsoft recommends integrating Azure AI Content Safety with Azure OpenAI.
Azure AI Content Safety provides APIs to detect and filter harmful or objectionable text and images.
For chatbot scenarios, you can configure Content Safety as a moderation layer on both user input (prompt) and model output (response).
This ensures safe interactions before messages are returned to users.
Other options:
A). Azure Machine Learning - used for building, training, and deploying custom ML models, not for content moderation.
B). Log Analytics - used for monitoring and logging, not for filtering objectionable content.
D). Microsoft Defender Threat Intelligence (Defender TI) - provides cybersecurity intelligence, not responsible for moderating chatbot content.
Therefore, the correct resource to create is Azure AI Content Safety.
The answer: C. Azure AI Content Safety
Microsoft References
Azure OpenAI and Azure AI Content Safety integration
Azure AI Content Safety overview
Moderate text with Azure AI Content Safety

Defender for Cloud Apps (A): Focused on monitoring SaaS app usage and cloud security, not image content moderation # # Custom Vision (B): Can classify images, but requires building/training your own model # more development effort # # Azure AI Vision (C): Can analyze images, including adult/racy content detection, but Content Safety is now the preferred service for moderation. Still valid # # Azure AI Content Safety (D): Specifically designed for detecting and blocking harmful/inappropriate content in text and images # # Document Intelligence (E): Extracts text/structure from documents, not relevant here # #
The answer:
C). Azure AI Vision and D. Azure AI Content Safety
Reference:
Azure AI Content Safety overview
Azure AI Vision - image moderation