The application stores data in a shared folder on a Windows server. The equivalent in Azure Storage is Azure Files , which provides:

Fully managed file shares in the cloud.

Support for the SMB (Server Message Block) protocol, so they can be mounted like traditional shared folders.

Easy migration of on-premises file shares to Azure.

Other storage options:

Table → NoSQL key-value store, not for file shares.

Queue → For message storage and processing, not files.

Blob → For unstructured object storage, but not mountable as a shared drive.

Therefore, Azure File Storage is correct.

Microsoft Reference: Azure Files overview

On-premises Microsoft SQL Server database cannot be used as an index data source.

Note: Indexer in Azure Cognitive Search: : Automate aspects of an indexing operation by configuring a data source and an indexer that you can schedule or run on demand. This feature is supported for a limited number of data source types on Azure.

Indexers crawl data stores on Azure.

Azure Blob Storage

Azure Data Lake Storage Gen2 (in preview)

Azure Table Storage

Azure Cosmos DB

Azure SQL Database

SQL Managed Instance

SQL Server on Azure Virtual Machines

Explanation

curl -i -X: The POST method is appropriate because you are creating a new custom analyzer, which involves sending a request to create a resource. POST is used for creating new resources in REST APIs.

https:// < cu1 > .services.ai.azure.com/: The base URL should be updated to include the contentunderstanding endpoint, as this is the specific service within the Azure AI Content Understanding resource (cu1) that handles analyzer-related operations. Replace < cu1 > with the actual resource name or endpoint provided by your Azure subscription.

/analyzers/myInvoice7api-version-2024-12-01-preview: This part of the URL is correct as it specifies the endpoint for creating a custom analyzer (/analyzers) with the name myInvoice7 and the API version 2024-12-01-preview. No changes are needed here, assuming the analyzer name and version are intended as shown.

Analysis of Options:

A. Lower the confidence score threshold of App1

Would just accept more low-confidence predictions, but won’t add support for a new format. ❌

B. Lower the accuracy threshold of App1

Similar reasoning: affects prediction acceptance, not model capabilities. ❌

C. Add the additional contract format to the existing training set and retrain the model

Correct approach. You extend the training data to include the new format, retrain, and reuse the same model. ✅

D. Create a new training set and add the additional contract format

This means starting from scratch, which requires more effort. Not minimal. ❌

E. Create and train a new custom model

Same as D, higher effort than necessary. ❌

Correct Answer:

C. Add the additional contract format to the existing training set. Retrain the model.

Microsoft Reference:

Azure AI Document Intelligence – Custom models

To detect aggressive language (e.g., profanity/abuse) and sexually explicit content in text using the Content Moderator Text – Screen API:

classify = true

Enables machine-assisted text classification that returns category scores (e.g., sexually explicit/suggestive categories). This is required to identify sexual content.

language = < code > (for example, eng )

Specifies the language so profanity/abusive terms are detected correctly. The profanity detection in Text-Screen is language dependent.

autocorrect = true

Helps normalize intentionally misspelled or obfuscated words so that profanity or aggressive terms are still detected (e.g., “f@#$” → “fuck”).

Other parameters in the blade are not necessary for this goal:

PII targets personal data extraction, not aggression/sexuality.

listId is for matching against custom blocklists.

Headers like Ocp-Apim-Subscription-Key and Content-Type are required to call the API, but they are not the content-analysis settings that control detection behavior asked for in this question.

Microsoft Azure AI References

Azure AI Content Moderator – Screen text API parameters (autocorrect, language, classify) and category outputs.

Azure AI Content Moderator – Text moderation concepts: profanity detection and classification categories.

To maximize answer accuracy (reduce randomness and hallucinations) with Azure OpenAI GPT-3.5, you should lower the sampling randomness. The primary control for this is temperature :

Temperature controls how “creative” or random the model is.

Lower values → more deterministic, focused, and conservative outputs.

temperature = 0 makes the model pick the highest-probability tokens, which typically yields the most accurate and repeatable responses for transactional bots like booking/reservations.

Top P (nucleus sampling) is an alternative to temperature. Microsoft recommends using one or the other. While reducing Top P also narrows randomness, the clearest and most common approach for maximizing accuracy is setting temperature to 0. Setting Top P to 0 is generally not recommended and may be invalid depending on the SDK.

System message tuning (Option D) helps with tone, role, and policy adherence, but does not itself guarantee factual accuracy if decoding randomness remains high.

Including the travel agent’s database (Option A) would require Azure OpenAI on your data or a RAG pattern. That can improve grounding of facts, but it’s not a model parameter change and is outside the scope of simply configuring GPT-3.5; moreover, accuracy still benefits from low temperature even when using your own data.

Therefore, the best single action here is: set Temperature to 0.

Microsoft Azure AI Solution References

Azure OpenAI Service – Chat Completions parameters (Temperature and Top P): “Lower values like 0.2 will make the output more focused and deterministic… use either temperature or top_p but generally not both.”

https://learn.microsoft.com/azure/ai-services/openai/reference#chat-completions

Prompt engineering with Azure OpenAI – controlling randomness and determinism:

https://learn.microsoft.com/azure/ai-services/openai/concepts/prompt-engineering

Azure OpenAI on your data (grounding with enterprise data/RAG):

https://learn.microsoft.com/azure/ai-services/openai/concepts/use-your-data

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Language Understanding: An AI service that allows users to interact with your applications, bots, and IoT devices by using natural language.

QnA Maker is a cloud-based Natural Language Processing (NLP) service that allows you to create a natural conversational layer over your data. It is used to find the most appropriate answer for any input from your custom knowledge base (KB) of information.

Text Analytics: Mine insights in unstructured text using natural language processing (NLP)—no machine learning expertise required. Gain a deeper understanding of customer opinions with sentiment analysis. The Language Detection feature of the Azure Text Analytics REST API evaluates text input

The system must analyze multiple sensor data points (rotation speed, angle, temperature, pressure) and generate alerts on anomalies.

Application Insights → used for monitoring applications, not IoT sensor data.

Metric alerts in Azure Monitor → works with single metrics but does not handle complex correlations.

Univariate Anomaly Detection → only works on a single time series (e.g., just temperature).

Multivariate Anomaly Detection → specifically built to detect anomalies across multiple correlated variables (sensor data).

Thus, the correct choice is Multivariate Anomaly Detection.