AI-102 Implement natural language processing solutions Practice Test 15 — 15 Questions

Question 1

You are developing an Azure AI Language solution to analyze customer support tickets. Each ticket has a subject and a description. You need to automatically classify tickets into categories (e.g., 'billing', 'technical', 'account') and extract the product name mentioned. You have a labeled dataset of 10,000 tickets with category labels and product name annotations. The solution must be cost-effective and easy to retrain as new categories emerge. You want to use a single Azure AI Language resource. Which approach should you use?

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

Use custom text classification for category and custom named entity recognition for product name extraction.. The most cost-effective and retrainable approach is to use custom text classification for categorizing tickets and custom named entity recognition (NER) for extracting product names. Both are part of Azure AI Language and can be trained on your labeled dataset. Custom NER allows you to define your own entity types (e.g., product name) and is more accurate than prebuilt NER for domain-specific products. Using a single Azure AI Language resource supports both features.

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Use custom text classification for category and key phrase extraction for product name.

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Use conversational language understanding (CLU) to handle both classification and entity extraction in a single model.

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Use custom text classification for category and prebuilt named entity recognition for product name extraction.

Question 2

You are a developer at a global e-commerce company. You are building a multilingual chatbot using Azure AI Language that supports English, French, German, and Spanish. The chatbot must answer frequently asked questions about order status, returns, and shipping. You plan to use Custom Question Answering with a single project containing questions and answers in all four languages. However, during testing, you notice that queries in French and German often return incorrect answers or no answer, while English and Spanish work well. You need to ensure accurate answers across all four languages. What should you do?

Accepted Answer

Create a separate Custom Question Answering project for each language and route user queries to the appropriate project based on language detection.. Custom Question Answering (CQA) projects are language-specific; a single project cannot reliably handle multiple languages due to differences in tokenization, stemming, and stop-word handling. By creating a separate project per language and routing queries based on language detection (e.g., using Azure AI Language's language detection API), you ensure that each project's model is optimized for its respective language, improving answer accuracy for French and German.

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Use Azure Cognitive Search with semantic ranking to index the QnA pairs.

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Add synonyms in the project for French and German terms to improve matching.

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Deploy the same project to multiple regions and use traffic manager.

Question 3

A financial services company uses Azure AI Language to analyze customer support transcripts. They want to identify the main topics discussed in each conversation and generate a summary of the key points. The solution must minimize development effort and use prebuilt functionality. You need to recommend the appropriate Azure AI Language features. What should you use?

Accepted Answer

Key phrase extraction and conversation summarization.. Key phrase extraction identifies the main topics in text, and conversation summarization produces a summary of key points from a conversation. Both are prebuilt Azure AI Language features requiring no custom training, minimizing development effort. Together they meet the requirement to identify topics and summarize transcripts.

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Custom Named Entity Recognition (NER) and conversation summarization.

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Entity linking and conversation summarization.

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Sentiment analysis and key phrase extraction.

Question 4

A research organization uses Azure AI Language to process large volumes of scientific papers. They need to extract specific entities such as gene names, protein names, and chemical compounds. The entity types are highly specialized and not covered by prebuilt models. The organization has a labeled dataset of 10,000 documents. You need to recommend the most efficient approach to build the entity extraction solution. What should you do?

Accepted Answer

Train a Custom Named Entity Recognition (NER) model using the labeled dataset in Azure AI Language.. Custom Named Entity Recognition (NER) in Azure AI Language allows you to train a model on your own labeled dataset (10,000 documents) to extract highly specialized entity types like gene names, protein names, and chemical compounds that are not covered by prebuilt models. This approach is the most efficient as it leverages the labeled data directly, avoiding the need for complex post-processing or hybrid solutions.

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Use the prebuilt NER model and map the recognized entities to the required types.

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Use Azure Logic Apps to call the Text Analytics API and post-process the results.

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Train a custom NER model for genes and use prebuilt NER for chemicals.

Question 5

A legal compliance team needs to automatically redact personally identifiable information (PII) from legal documents before sharing them with external auditors. The documents are stored in Azure Blob Storage. The solution must use Azure AI Language to detect PII and then redact the identified entities. The redaction must be performed on the original documents, and the redacted versions must be saved to a separate container. You need to design a serverless solution with minimal latency. What should you do?

Accepted Answer

Create an Azure Function triggered by Blob Storage events to detect PII, redact, and save to a separate container.. An Azure Function triggered by Blob Storage events is the canonical serverless pattern for event-driven document processing: the blob-created event fires the function, which calls the Azure AI Language PII detection/redaction API and writes the redacted output to a separate container. This minimizes latency because processing starts immediately on upload, with no orchestration overhead and automatic scaling. It also satisfies the requirement to keep the original intact and save redacted copies elsewhere.

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Use Azure Data Factory to copy documents to a processing location and call an Azure Function.

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Use Azure Batch Service to process documents in parallel and redact PII.

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Use Azure Logic Apps with a Blob trigger to call the PII detection API and write redacted documents.

Question 6

Refer to the exhibit. You have created a Text Analytics resource and retrieved its keys. You want to use the key1 to call the Sentiment Analysis API from a Python application. Which endpoint URL should you use?

Accepted Answer

https://mytextanalytics.cognitiveservices.azure.com/text/analytics/v3.1/sentiment. The Sentiment Analysis API for Azure Cognitive Services Text Analytics uses the endpoint pattern `https://<resource-name>.cognitiveservices.azure.com/text/analytics/v3.1/sentiment`. This is the standard REST API endpoint for sentiment analysis in version 3.1, which requires the `/text/analytics/v3.1/sentiment` path appended to the custom resource domain.

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https://mytextanalytics.cognitiveservices.azure.com/sentiment/v3.1

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https://mytextanalytics.api.cognitive.microsoft.com/text/analytics/v3.1/sentiment

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https://mytextanalytics.cognitiveservices.azure.com/analyze

Question 7

Refer to the exhibit. You deployed a custom model for Language service. Which command should you run to check if the deployment is ready to accept inference requests?

Accepted Answer

az cognitiveservices account deployment show --resource-group myRG --name myLangService --deployment-name myDeployment. The `az cognitiveservices account deployment show` command retrieves the current state of a specific deployment, including its provisioning status (e.g., 'Succeeded'). Only when the status is 'Succeeded' can the deployment accept inference requests. This is the correct command to verify readiness before sending any prediction calls.

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az cognitiveservices account deployment list --resource-group myRG --name myLangService

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az cognitiveservices account deployment delete --resource-group myRG --name myLangService --deployment-name myDeployment

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az cognitiveservices account deployment create --resource-group myRG --name myLangService --deployment-name myDeployment

Question 8

Refer to the exhibit. You called the Named Entity Recognition API on a document. Which entity type is "Seattle"?

Accepted Answer

Location. The Named Entity Recognition (NER) API in Azure AI Language identifies 'Seattle' as a Location entity because it is a recognized geographical place. The API uses a pre-trained model that categorizes entities into types such as Location, Person, Organization, etc., and 'Seattle' falls under the Location type based on its semantic context in the document.

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Organization

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Person

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City

Question 9

Refer to the exhibit. A developer is configuring a QnA Maker skill for a bot. The skill fails to respond to queries. What is the most likely issue?

Accepted Answer

The modelUrl points to the authoring API instead of the runtime endpoint.. The modelUrl uses the v4.0 API, but the endpoint key and kbId are hardcoded and may be invalid or expired. Additionally, the URL should be for the runtime endpoint, not the authoring API.

Answer

The kbId is not published.

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The skill is not deployed.

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The endpointKey is incorrect.

Question 10

Refer to the exhibit. A developer is testing the Text Analytics sentiment analysis API and receives a 401 error. What is the most likely cause?

Accepted Answer

The subscription key is invalid or expired.. HTTP 401 Unauthorized specifically means the request lacked valid authentication credentials — for Azure Cognitive Services Text Analytics, that means the Ocp-Apim-Subscription-Key header is missing, malformed, or the key has been regenerated/expired. The endpoint and API version are validated separately (404/400), and resource group naming is irrelevant to runtime API calls. Therefore the invalid or expired subscription key is the most likely cause.

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The API version is not supported.

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The endpoint URL is incorrect.

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The resource group is misspelled.

Question 11

You are developing a solution that uses Azure AI Language to perform sentiment analysis on multilingual product reviews. The reviews are in English, German, and Japanese. You need to ensure that the sentiment score is accurate for each language. What should you do?

Accepted Answer

Specify the correct language code for each review in the request, such as 'en', 'de', or 'ja'.. Azure AI Language sentiment analysis supports multiple languages, and specifying the correct language code for each review ensures the model uses the appropriate linguistic rules. This yields the most accurate sentiment scores. Auto-detection or forcing a single language can lead to misinterpretation, while translation adds unnecessary complexity and potential loss of sentiment nuance.

Answer

Translate all reviews to English using Azure AI Translator, then perform sentiment analysis with language set to 'en'.

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Set the language parameter to 'en' for all requests to force English sentiment analysis.

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Omit the language parameter and let the service auto-detect the language for each review.

Question 12

You are building a solution that uses Azure AI Language to analyze transcribed call-center conversations. The transcripts are stored as plain text in Azure Blob Storage. You need to identify the specific products, dates, and monetary amounts mentioned in each conversation while distinguishing them from generic nouns. You also need to return the character offset and length for each detected mention so the UI can highlight them. Which Azure AI Language feature should you use?

Accepted Answer

Named Entity Recognition (NER). Named Entity Recognition in Azure AI Language is designed to detect entity categories such as Product, DateTime, and Quantity and to return each mention with its offset and length. Those offsets allow the application to highlight exact spans in the transcript, and the category labels let developers filter products from dates and amounts.

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Key Phrase Extraction

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Extractive Summarization

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Conversation Summarization

Question 13

A healthcare company uses Azure AI Language's custom question answering to build a bot that answers patient FAQs. The knowledge base contains documents with sensitive information. The company needs to ensure that the bot only returns answers from documents that the user is authorized to access. What should they implement?

Accepted Answer

Implement document-level access control by using metadata tags and filtering in the query.. The correct solution is to use metadata tags on documents and apply filters during query execution. This allows the custom question answering service to return only answers from documents that match the user's authorization level. Other options either address resource management, require complex duplication, or only authenticate without enforcing access control.

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Configure the bot to prompt users for credentials before answering and validate against Azure Active Directory.

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Enable role-based access control (RBAC) on the Azure AI Language resource.

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Use separate knowledge bases for each user group and route queries based on user identity.

Question 14

You are building a custom question answering solution using Azure AI Language. The knowledge base contains a large number of question-answer pairs. You need to ensure that the solution returns the most relevant answer for a user's query, even when the query contains synonyms not present in the knowledge base. What should you do?

Accepted Answer

Add alternative questions for each answer that include common synonyms.. Custom question answering matches user queries to questions in the knowledge base. To handle synonyms, you can add alternative questions that include common synonyms for each answer. This improves the likelihood that a query with different wording will match the correct answer. Other options do not directly address synonym matching and may not improve relevance.

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Enable the 'enableHierarchicalExtraction' setting in the project configuration.

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Set the 'defaultAnswer' to a response that asks the user to rephrase using known terms.

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Increase the number of documents in the knowledge base to provide more context.

Question 15

You are building a conversational language understanding (CLU) project in Azure AI Language. You need to ensure the model can correctly interpret user utterances that include both an intent and multiple entities. Which two actions should you perform? (Choose two.)

Accepted Answer

Add utterances that contain multiple entities and label each entity with the correct entity type.. To handle utterances with multiple entities, you need labeled examples that include multiple entities with correct types, and you must train and evaluate the model to verify performance. Creating intents per entity, looking for a non-existent setting, or using prebuilt entities for custom types will not achieve the goal.

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Add a prebuilt entity component for each custom entity to supplement training data.

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Enable the "Extract multiple entities" option in the project settings.

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Define a separate intent for each entity type to improve extraction accuracy.