November/2021 Latest Braindump2go AI-102 Exam Dumps with PDF and VCE Free Updated Today! Following are some new AI-102 Real Exam Questions!
QUESTION 92
Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.
After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.
You have an Azure Cognitive Search service.
During the past 12 months, query volume steadily increased.
You discover that some search query requests to the Cognitive Search service are being throttled.
You need to reduce the likelihood that search query requests are throttled.
Solution: You migrate to a Cognitive Search service that uses a higher tier.
Does this meet the goal?
A. Yes
B. No
Answer: A
Explanation:
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
QUESTION 93
You have receipts that are accessible from a URL.
You need to extract data from the receipts by using Form Recognizer and the SDK. The solution must use a prebuilt model.
Which client and method should you use?
A. the FormRecognizerClient client and the StartRecognizeContentFromUri method
B. the FormTrainingClient client and the StartRecognizeContentFromUri method
C. the FormRecognizerClient client and the StartRecognizeReceiptsFromUri method
D. the FormTrainingClient client and the StartRecognizeReceiptsFromUri method
Answer: D
Explanation:
To analyze receipts from a URL, use the StartRecognizeReceiptsFromUri method
Example code:
private static async Task AnalyzeReceipt(
FormRecognizerClient recognizerClient, string receiptUri)
{
RecognizedFormCollection receipts = await recognizerClient.StartRecognizeReceiptsFromUri(new Uri(receiptUrl)).WaitForCompletionAsync();
Reference:
https://docs.microsoft.com/en-us/azure/applied-ai-services/form-recognizer/quickstarts/client-library
QUESTION 94
You have a collection of 50,000 scanned documents that contain text.
You plan to make the text available through Azure Cognitive Search.
You need to configure an enrichment pipeline to perform optical character recognition (OCR) and text analytics. The solution must minimize costs.
What should you attach to the skillset?
A. a new Computer Vision resource
B. a free (Limited enrichments) Cognitive Services resource
C. an Azure Machine Learning pipeline
D. a new Cognitive Services resource that uses the S0 pricing tier
Answer: A
Explanation:
The Computer Vision API uses text recognition APIs to extract and recognize text information from images.
Read uses the latest recognition models, and is optimized for large, text-heavy documents and noisy images.
Reference:
https://docs.microsoft.com/en-us/azure/architecture/solution-ideas/articles/cognitive-search-with-skillsets
QUESTION 95
Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.
After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.
You have an Azure Cognitive Search service.
During the past 12 months, query volume steadily increased.
You discover that some search query requests to the Cognitive Search service are being throttled.
You need to reduce the likelihood that search query requests are throttled.
Solution: You add indexes.
Does this meet the goal?
A. Yes
B. No
Answer: B
Explanation:
Instead, you could migrate to a Cognitive 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
QUESTION 96
Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.
After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.
You have an Azure Cognitive Search service.
During the past 12 months, query volume steadily increased.
You discover that some search query requests to the Cognitive Search service are being throttled.
You need to reduce the likelihood that search query requests are throttled.
Solution: You enable customer-managed key (CMK) encryption.
Does this meet the goal?
A. Yes
B. No
Answer: B
Explanation:
Customer-managed key (CMK) encryption does not affect throttling.
Instead, you could migrate to a Cognitive 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
QUESTION 97
You are training a Language Understanding model for a user support system.
You create the first intent named GetContactDetails and add 200 examples.
You need to decrease the likelihood of a false positive.
What should you do?
A. Enable active learning.
B. Add a machine learned entity.
C. Add additional examples to the GetContactDetails intent.
D. Add examples to the None intent.
Answer: A
Explanation:
Active learning is a technique of machine learning in which the machine learned model is used to identify informative new examples to label. In LUIS, active learning refers to adding utterances from the endpoint traffic whose current predictions are unclear to improve your model.
Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/luis/luis-glossary
QUESTION 98
You are developing an application that will use Azure Cognitive Search for internal documents.
You need to implement document-level filtering for Azure Cognitive Search.
Which three actions should you include in the solution? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
A. Send Azure AD access tokens with the search request.
B. Retrieve all the groups.
C. Retrieve the group memberships of the user.
D. Add allowed groups to each index entry.
E. Create one index per group.
F. Supply the groups as a filter for the search requests.
Answer: CDF
Explanation:
Your documents must include a field specifying which groups have access. This information becomes the filter criteria against which documents are selected or rejected from the result set returned to the issuer.
D: A query request targets the documents collection of a single index on a search service.
CF: In order to trim documents based on group_ids access, you should issue a search query with a group_ids/any(g:search.in(g, ‘group_id1, group_id2,…’)) filter, where ‘group_id1, group_id2,…’ are the groups to which the search request issuer belongs.
Reference:
https://docs.microsoft.com/en-us/azure/search/search-security-trimming-for-azure-search
QUESTION 99
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. dialog
B. user
C. turn
D. conversation
Answer: A
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
QUESTION 100
You need to enable speech capabilities for a chatbot.
Which three actions should you perform? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
A. Enable WebSockets for the chatbot app.
B. Create a Speech service.
C. Register a Direct Line Speech channel.
D. Register a Cortana channel.
E. Enable CORS for the chatbot app.
F. Create a Language Understanding service.
Answer: ABC
Explanation:
You can use the Speech service to voice-enable a chat bot.
The Direct Line Speech channel uses the text-to-speech service, which has neural and standard voices.
You’ll need to make a small configuration change so that your bot can communicate with the Direct Line Speech channel using web sockets.
Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/speech-service/tutorial-voice-enable-your-bot-speech-sdk
QUESTION 101
Drag and Drop Question
You need to develop an automated call handling system that can respond to callers in their own language.
The system will support only French and English.
Which Azure Cognitive Services service should you use to meet each requirement? To answer, drag the appropriate services to the correct requirements. Each service may be used once, more than once, or not at all. You may need to drag the split bat between panes or scroll to view content.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Box 1: Text Analytics
The Language Detection feature of the Azure Text Analytics REST API evaluates text input for each document and returns language identifiers with a score that indicates the strength of the analysis.
Incorrect Answers:
Speaker Recognition which accurately verifies and identifies speakers by their unique voice characteristics.
Box 2: Translator
Translator is a cloud-based neural machine translation service that is part of the Azure Cognitive Services family of REST APIs. Translator can be used with any operating system and powers many Microsoft products and services used by thousands of businesses worldwide to perform language translation and other language-related operations.
Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/text-analytics/how-tos/text-analytics-how-to-language-detection
https://docs.microsoft.com/en-us/azure/cognitive-services/translator/translator-overview
QUESTION 102
Drag and Drop Question
You are developing an application that will recognize faults in components produced on a factory production line. The components are specific to your business.
You need to use the Custom Vision API to help detect common faults.
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:
Step 1: Create a project
Create a new project.
Step 2: Upload and tag the images
Choose training images. Then upload and tag the images.
Step 3: Train the classifier model.
Train the classifier
Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/custom-vision-service/getting-started-build-a-classifier
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