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NEW QUESTION # 339
You are building an app that will analyze text by using the Azure Al Language service.
You need to configure the app to mask the telephone number and email details in a given document.
How should you complete the code? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
To mask (redact) phone numbers and email addresses in text using Azure AI Language, you should call the PII entity recognition operation and then print the redacted text that the service returns. In the SDKs, this corresponds to calling RecognizePiiEntities and reading the RedactedText from the result object. The PII feature automatically detects categories such as PhoneNumber and Email and returns a redacted version of the input (masked) in the redactedText/RedactedText field. This is exactly what the code snippet's "Masked Text" output is asking for.
Therefore:
Use RecognizePiiEntities to detect and redact PII in the document.
Output RedactedText to print the masked string.
Microsoft References
How-to: Detect and redact PII in text (overview and redaction behavior). Microsoft Learn Quickstart: Detect Personally Identifiable Information (shows PII detection and redacted output). Microsoft Learn NET reference: PiiEntityCollection.RedactedText property ("Gets the text of the input document with all PII redacted"). Microsoft Learn Python client overview: recognize_pii_entities operation for PII detection. Microsoft Learn
NEW QUESTION # 340
You are building a Language Understanding model for purchasing tickets.
You have the following utterance for an intent named PurchaseAndSendTickets.
Purchase [2 audit business] tickets to [Paris] [next Monday] and send tickets to [[email protected]] You need to select the entity types. The solution must use built-in entity types to minimize training data whenever possible.
Which entity type should you use for each label? To answer, drag the appropriate entity types to the correct labels. Each entity type may be used once, more than once, or not at all.
You may need to drag the split bar between panes or scroll to view content.
Answer:
Explanation:
Explanation
Graphical user interface, application Description automatically generated
Box 1: GeographyV2
The prebuilt geographyV2 entity detects places. Because this entity is already trained, you do not need to add example utterances containing GeographyV2 to the application intents.
Box 2: Email
Email prebuilt entity for a LUIS app: Email extraction includes the entire email address from an utterance.
Because this entity is already trained, you do not need to add example utterances containing email to the application intents.
Box 3: Machine learned
The machine-learning entity is the preferred entity for building LUIS applications.
Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/luis/luis-reference-prebuilt-geographyv2
https://docs.microsoft.com/en-us/azure/cognitive-services/luis/luis-reference-prebuilt-email
https://docs.microsoft.com/en-us/azure/cognitive-services/luis/reference-entity-machine-learned-entity
NEW QUESTION # 341
You are developing a solution to generate a word cloud based on the reviews of a company's products.
Which Text Analytics REST API endpoint should you use?
Answer: C
Explanation:
Reference:
Comprehensive Detailed ExplanationA word cloud is typically generated by extracting the most relevant keywords or key phrases from text data.
The Key Phrases API of Text Analytics returns the important words and phrases in a set of documents.
You can then use those key phrases and their frequency counts to build a word cloud visualization.
Why not the others?
B). sentiment: Identifies sentiment (positive/negative/neutral), not useful for word cloud keywords.
C). languages: Detects the language of the text, not relevant here.
D). entities/recognition/general: Extracts named entities (like people, locations, organizations), but this is narrower than general key phrase extraction.
Reference:
Text Analytics - Key Phrase Extraction
NEW QUESTION # 342
You plan to use a Language Understanding application named app1 that is deployed to a container.
App1 was developed by using a Language Understanding authoring resource named lu1.
App1 has the versions shown in the following table.
You need to create a container that uses the latest deployable version of app1.
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/luis/luis-container-howto
NEW QUESTION # 343
You plan to use containerized versions of the Anomaly Detector API on local devices for testing and in on- premises datacenters.
You need to ensure that the containerized deployments meet the following requirements:
Prevent billing and API information from being stored in the command-line histories of the devices that run the container.
Control access to the container images by using Azure role-based access control (Azure RBAC).
Which four 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 four.) NOTE: More than one order of answer choices is correct. You will receive credit for any of the correct orders you select.
Answer:
Explanation:
Explanation:
Comprehensive Detailed ExplanationYou are deploying Anomaly Detector API as a container. The key requirements are:
* Prevent billing and API keys from being exposed in command-line history.
* Control access to container images using Azure RBAC.
Step-by-step reasoning:
* Pull the Anomaly Detector container image
* Microsoft Cognitive Services (including Anomaly Detector) provides container images via Microsoft Container Registry.
* You must first pull the container image locally.
* Push the image to an Azure container registry (ACR)
* To enforce Azure RBAC, you need to use Azure Container Registry instead of Docker Hub.
* Storing in ACR ensures authentication and role-based access control to the image.
* Distribute a docker run script
* Instead of providing credentials directly on the command line (which risks storing API keys in shell history), best practice is to distribute a docker run script (or use environment variables / mounted files) to run the container.
* This prevents exposure of sensitive information.
* Run the container on local devices or on-premises datacenters
* The container now runs securely, with API key and billing info injected at runtime via environment variables or mounted secrets.
* Create a custom Dockerfile # Not needed, because Microsoft provides the official Anomaly Detector container image.
* Build the image # Not required; again, the container is prebuilt.
* Push the image to Docker Hub # Does not allow RBAC, so this would not meet the security requirement.
Incorrect Options
Correct Sequence:
* Pull the Anomaly Detector container image.
* Push the image to an Azure container registry.
* Distribute a docker run script.
* Run the container in the local or on-prem environment.
* Use Cognitive Services containers
* Azure Container Registry - Authentication and RBAC
* Best practices for running Cognitive Services containers
Microsoft References
NEW QUESTION # 344
......
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