AI-102 PASS4SURE VCE - AI-102 UPDATED TRAINING & AI-102 PREP PRACTICE

AI-102 Pass4sure vce - AI-102 Updated Training & AI-102 prep practice

AI-102 Pass4sure vce - AI-102 Updated Training & AI-102 prep practice

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Microsoft Designing and Implementing a Microsoft Azure AI Solution Sample Questions (Q113-Q118):

NEW QUESTION # 113
You have an Azure subscription and 10,000 ASCII files.
You need to identify files that contain specific phrases. The solution must use cosine similarity.
Which Azure OpenAI model should you use?

  • A. GPT-4
  • B. GPT-35 Turbo
  • C. GPT-4-32k
  • D. text-embedding-ada-002

Answer: D


NEW QUESTION # 114
You have an existing Azure Cognitive Search service.
You have an Azure Blob storage account that contains millions of scanned documents stored as images and PDFs.
You need to make the scanned documents available to search as quickly as possible. What should you do?

  • A. Create a Cognitive Search service for each type of document.
  • B. Split the data into multiple blob containers. Create a Cognitive Search service for each container. Within each indexer definition, schedule the same runtime execution pattern.
  • C. Split the data into multiple blob containers. Create an indexer for each container. Increase the search units. Within each indexer definition, schedule a sequential execution pattern.
  • D. Split the data into multiple virtual folders. Create an indexer for each folder. Increase the search units. Within each indexer definition, schedule the same runtime execution pattern.

Answer: D

Explanation:
Incorrect Answers:
A: Need more search units to process the data in parallel. B: Run them in parallel, not sequentially.
C: Need a blob indexer.
Note: A blob indexer is used for ingesting content from Azure Blob storage into a Cognitive Search index. Index large datasets Indexing blobs can be a time-consuming process. In cases where you have millions of blobs to index, you can speed up indexing by partitioning your data and using multiple indexers to process the data in parallel. Here's how you can set this up:
Partition your data into multiple blob containers or virtual folders Set up several data sources, one per container or folder.
Create a corresponding indexer for each data source. All of the indexers should point to the same target search index.
One search unit in your service can run one indexer at any given time. Creating multiple indexers as described above is only useful if they actually run in parallel.
Reference:
https://docs.microsoft.com/en-us/azure/search/search-howto-indexing-azure-blob-storage


NEW QUESTION # 115
You are building a retail chatbot that will use a QnA Maker service.
You upload an internal support document to train the model. The document contains the following question: "What is your warranty period?" Users report that the chatbot returns the default QnA Maker answer when they ask the following question: "How long is the warranty coverage?" The chatbot returns the correct answer when the users ask the following question: 'What is your warranty period?" Both questions should return the same answer.
You need to increase the accuracy of the chatbot responses.
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. (Choose three.)

Answer:

Explanation:

Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/qnamaker/how-to/edit-knowledge-base


NEW QUESTION # 116
You ace building an internet-based training solution. The solution requires that a user's camera and microphone remain enabled.
You need to monitor a video stream ol the user and detect when the user asks an instructor a question. The solution must minimize development effort.
What should you include in the solution?

  • A. language detection in Azure Al Language Service
  • B. object detection in Azure Al Custom Vision
  • C. the Face service in Azure Al Vision
  • D. speech-to-text in the Azure Al Speech service

Answer: D


NEW QUESTION # 117
You are building a chatbot by using the Microsoft Bot Framework Composer as shown in the exhibit. (Click the Exhibit tab.)

The chatbot contains a dialog named GetUserDetails. GetUserDetails contains a TextInput control that prompts users for their name.
The user input will be stored in a property named name.
You need to ensure that you can dispose of the property when the last active dialog ends.
Which scope should you assign to name?

  • A. curn
  • B. user
  • C. dialog
  • D. conversation

Answer: C

Explanation:
The dialog scope associates properties with the active dialog. Properties in the dialog scope are retained until the dialog ends.
Incorrect Answers:
A: The conversation scope associates properties with the current conversation. Properties in the conversation scope have a lifetime of the conversation itself. These properties are in scope while the bot is processing an activity associated with the conversation (for example, multiple users together in a Microsoft Teams channel).
B: The user scope associates properties with the current user. Properties in the user scope do not expire. These properties are in scope while the bot is processing an activity associated with the user.
C: The turn scope associates properties with the current turn. Properties in the turn scope expire at the end of the turn.
Reference:
https://docs.microsoft.com/en-us/composer/concept-memory?tabs=v2x


NEW QUESTION # 118
......

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