Explanation:
Step 1: Enable active learning
To enable active learning, you must log user queries. This is accomplished by calling the endpoint query with the log=true querystring parameter and value.
Step 2: Validate the utterances logged for review and modify the model
Step 3: Train and republish the Language Understanding model
The process of reviewing endpoint utterances for correct predictions is called Active learning.
Active learning captures endpoint queries and selects user's endpoint utterances that it is unsure of. You review these utterances to select the intent and mark entities for these real-world utterances. Accept these changes into your example utterances then train and publish. LUIS then identifies utterances more accurately.
Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/luis/luis-how-to-review-endpoint- utterances#log-user-queries-to-enable-active-learning
https://docs.microsoft.com/en-us/azure/cognitive-services/luis/luis-concept-prebuilt-model

Customer-managed key (CMK) encryption does not affect throttling.
Instead, you could migrate to a Azure AI Search service that uses a higher tier.
Note: A simple fix to most throttling issues is to throw more resources at the search service (typically replicas for query-based throttling, or partitions for indexing-based throttling). However, increasing replicas or partitions adds cost, which is why it is important to know the reason why throttling is occurring at all.
Reference:
https://docs.microsoft.com/en-us/azure/search/search-performance-analysis

Input requirements
Azure AI Document Intelligence works on input documents that meet these requirements:
Format must be JPG, PNG, PDF (text or scanned), or TIFF. Text-embedded PDFs are best because there's no possibility of error in character extraction and location.
File size must be less than 50 MB.
Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/form-recognizer/overview