AI-102 Implement natural language processing solutions Practice Test 13 — 15 Questions

Question 1

Your organization runs a popular news website. You want to use Azure AI Language to automatically generate summaries of news articles for the homepage. The summaries must be concise (under 100 words), extractive (selecting key sentences from the article), and available in both English and Spanish. You have a large corpus of articles in both languages. You need to implement a solution that requires minimal custom development and leverages Azure AI Language's prebuilt capabilities. Which approach should you take?

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

Accepted Answer

Use the prebuilt extractive summarization API in Azure AI Language for both languages, specifying the maximum summary length.. The prebuilt extractive summarization API in Azure AI Language natively supports both English and Spanish, can generate summaries under 100 words by specifying the maximum summary length, and requires no custom development. This directly meets the requirements for concise, extractive summaries with minimal effort.

Answer

Train a custom extractive summarization model using Azure AI Language's custom text summarization feature with labeled data in both languages.

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Use the conversation summarization API to summarize each article.

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Use the prebuilt abstractive summarization API to generate summaries.

Question 2

You are building a solution to analyze customer feedback from multiple sources: emails, chat logs, and survey responses. You need to detect the overall sentiment trend over time and identify the most frequently mentioned topics. The solution must also allow the business analyst to ask natural language questions about the data (e.g., 'Show me complaints about shipping in the last month'). You have all data in Azure Blob Storage. You need to implement a solution with minimal custom code. Which combination of Azure services should you use?

Accepted Answer

Use Azure AI Language to extract sentiment and key phrases, then index the data in Azure Cognitive Search with semantic search enabled; use the search's built-in features for trend analysis and natural language queries.. Azure AI Language provides built-in sentiment analysis and key phrase extraction, and Azure Cognitive Search with semantic search enables indexing the extracted data for trend analysis and natural language queries without custom code. This combination directly meets the requirements of detecting sentiment trends, identifying topics, and allowing natural language questions, all with minimal custom development.

Answer

Use Azure OpenAI Service to analyze sentiment and generate summaries, and store results in a Cosmos DB for querying.

Answer

Use Azure AI Language to perform sentiment analysis and key phrase extraction, then load the results into Power BI for trend analysis and natural language Q&A.

Answer

Use Azure AI Language's custom question answering to create a knowledge base from the feedback and allow natural language queries.

Question 3

Your company uses Azure AI Language to process legal documents. You have built a custom NER model to extract parties, dates, and obligations. The model performs well on English documents but now you need to support French and German documents. You have no labeled data in those languages. You want to use the existing English model as a starting point. The solution must be cost-effective and avoid manual labeling as much as possible. You also need to ensure that the model can be retrained quickly when new document types are added. Which approach should you take?

Accepted Answer

Enable the multilingual option in the custom NER project and retrain the model using the existing English labeled data.. Enabling the multilingual option in custom NER allows training on the existing English labeled data, and the model can generalize to French and German without requiring labeled data in those languages. This is cost-effective and avoids manual labeling. Option A is wrong because using the prebuilt NER would not extract custom entities like parties, dates, and obligations in the specific format needed. Option C is wrong because translating documents introduces potential errors and latency, and may not capture legal nuances. Option D is wrong because building separate models requires translating the labeled data, which is additional effort and may lose context.

Answer

Use the prebuilt NER model for French and German to extract entities like dates and organizations, and then map them to your custom entity types.

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Use the English custom NER model as-is and translate the French and German documents to English before processing.

Answer

Build separate custom NER models for French and German by translating the English labeled data using Azure AI Translator and training new models.

Question 4

A healthcare organization is deploying a solution using Azure AI Language to extract medical entities from clinical notes. The solution must comply with HIPAA and support the following requirements: extract medication names, dosages, and frequencies; identify patient conditions; and recognize negated terms (e.g., 'no sign of infection'). Which THREE Azure AI Language features should the organization use?

Accepted Answer

Prebuilt NER for Healthcare. Prebuilt NER for Healthcare is specifically designed to extract medical entities such as medication names, dosages, frequencies, and patient conditions from unstructured clinical text. It is a HIPAA-eligible Azure service that provides domain-specific entity categories, making it the correct choice for the healthcare use case described.

Answer

PII detection

Answer

Prebuilt NER for Finance

Question 5

A legal firm is using Azure AI Language to analyze contracts. They need to extract key clauses, parties involved, and dates. The solution must be customizable to their specific contract types. Which TWO Azure AI Language features should they use?

Accepted Answer

Prebuilt NER for Legal. Prebuilt NER for Legal is correct because it provides out-of-the-box entity extraction tailored to legal documents, including parties, dates, and key clauses, without requiring custom training. This feature is specifically designed for legal use cases, making it ideal for a firm that needs to quickly extract standard legal entities from contracts.

Answer

Conversation summarization

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Key phrase extraction

Answer

Entity linking

Question 6

Refer to the exhibit. You have a Custom Question Answering project configured with the JSON shown. When you test the project in Azure AI Language Studio, the query 'How many vacation days do I get?' returns no answer. What is the most likely cause?

Accepted Answer

The query is not phrased as an exact match to the trained question.. Custom Question Answering can be configured to require exact matching between the user query and the trained questions. In the exhibit, the JSON likely defines a QnA pair with a specific question, but the test query 'How many vacation days do I get?' does not exactly match the trained question (e.g., it might be slightly different wording). Since the project is set to exact match, the service returns no answer because the query is not an exact match. Option A correctly identifies this cause.

Answer

The language is set to English but the query uses informal language.

Answer

The answer field is empty in the JSON.

Answer

The confidence score threshold is set too high.

Question 7

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.

Answer

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.

Answer

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

Question 8

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.. Conversation summarization is a prebuilt feature that generates summaries of conversations, and key phrase extraction identifies main topics. Both are available in Azure AI Language without custom training. Option B correctly combines these two features to identify main topics and generate a summary. Option A is wrong because custom NER requires custom labeling and is not necessary for identifying main topics. Option C is wrong because entity linking is not used for summarization or topic identification. Option D is wrong because sentiment analysis does not provide summarization or topic extraction.

Answer

Custom Named Entity Recognition (NER) and conversation summarization.

Answer

Entity linking and conversation summarization.

Answer

Sentiment analysis and key phrase extraction.

Question 9

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.

Answer

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.

Answer

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

Question 10

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.

Answer

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

Answer

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

Answer

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

Question 11

Refer to the exhibit. You called the Key Phrase Extraction API on two documents. What is the total number of key phrases extracted?

Accepted Answer

5. The Key Phrase Extraction API returns a list of key phrases per document. In this case, Document 1 returns 'weather', 'Seattle', and 'rainy', while Document 2 returns 'weather' and 'Seattle'. The total number of unique key phrases across both documents is 3 ('weather', 'Seattle', 'rainy'), but the question asks for the total number of key phrases extracted, which counts each occurrence per document. That gives 3 + 2 = 5 key phrases. Option B is correct because the API outputs phrases per document, not deduplicated globally.

Answer

7

Answer

3

Answer

2

Question 12

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.

Answer

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

Answer

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

Answer

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

Question 13

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.

Answer

Organization

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Person

Answer

City

Question 14

Refer to the exhibit. You receive this error when calling an Azure Cognitive Services API. What is the most likely cause?

Accepted Answer

The subscription key is not valid for this Cognitive Services resource.. The error indicates that the subscription key provided is not associated with the Cognitive Services resource being accessed. In Azure Cognitive Services, each resource has a unique set of keys, and using a key from a different resource (even in the same region) will result in a 401 Unauthorized error with this specific message. Option D is correct because the key must match the resource's endpoint exactly.

Answer

The API endpoint is incorrect.

Answer

The subscription key has expired.

Answer

The subscription key is from a different region.

Question 15

Refer to the exhibit. A developer is debugging an Azure web app that uses Azure AD authentication. The app frequently fails to authenticate users. What is the most likely cause of the error?

Accepted Answer

The web app's outbound traffic is blocked by a firewall or VNet restriction.. The error indicates that the web app cannot reach the Azure AD metadata endpoint, likely due to a network restriction or firewall blocking outbound HTTPS traffic.

Answer

The JWT token has expired.

Answer

The web app is not registered in Azure AD.

Answer

The web app is using an incorrect client secret.