AI-102 Implement natural language processing solutions Practice Test 10 — 15 Questions

Question 1

You are building a solution that must translate customer chat messages from Spanish to English in real-time. The solution must also detect the language of incoming messages to handle cases where users write in other languages. Which TWO Azure AI service features should you use?

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

Accepted Answer

Azure AI Translator - Language Detection. Azure AI Translator's Language Detection feature can identify the language of incoming chat messages, which is necessary to handle cases where users write in languages other than Spanish. Option B is correct because Azure AI Translator's Translation feature can then translate those detected Spanish messages into English in real-time, meeting the core requirement of the solution.

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Azure AI Speech - Speech Translation

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Azure AI Language Understanding (LUIS)

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Azure AI Custom Question Answering

Question 2

You are developing a custom text classification model using Azure AI Language. You have labeled 2000 documents across 10 categories. You need to evaluate the model's performance before deploying to production. Which THREE metrics should you examine?

Accepted Answer

Recall. Recall is correct because it measures the proportion of actual positive instances correctly identified by the model, which is critical in custom text classification to ensure that relevant documents are not missed. In Azure AI Language, recall helps assess how well the model captures all instances of each category, especially when class distribution is imbalanced across the 10 categories.

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Word Error Rate

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BLEU Score

Question 3

You are using the Azure AI Language service to process customer reviews. You need to extract the following insights: overall sentiment, key phrases, and entity types (such as product names). Which THREE operations should you call?

Accepted Answer

Key Phrase Extraction. Key Phrase Extraction is correct because it identifies the most important points in the text, such as product names or features, which directly supports extracting 'key phrases' from customer reviews. This operation is part of the Azure AI Language service's text analytics capabilities and is essential for summarizing review content.

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Language Detection

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PII Detection

Question 4

You need the project to support English, Spanish, and French. What change should you make to the command?

Accepted Answer

Change --multilingual false to --multilingual true.. The command requires multilingual support for English, Spanish, and French. By default, the `--multilingual` flag is set to `false`, which restricts the project to a single language. Changing it to `true` enables the project to accept utterances in multiple languages, allowing the Conversational Language Understanding (CLU) model to process and train on intents and entities across all specified languages.

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Change --language to "multi".

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Add --description "Multi-language support".

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Change --project-name to "SupportBotML".

Question 5

Based on the exhibit, which entity should you focus on improving by adding more labeled examples?

Accepted Answer

ProductName. The exhibit shows that ProductName has a recall of 0.65, which is lower than the recall for Date (0.98) and OrderNumber (0.99). Low recall indicates that the model is missing many true instances of ProductName. Adding more labeled examples specifically for ProductName will help the model learn its patterns better, improving recall and overall performance. This aligns with the practice of iterative model improvement in custom entity extraction within Azure AI Language.

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Date

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OrderNumber

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All entities need improvement.

Question 6

You notice a spike in errors (HTTP 429) on a specific day. What is the most likely cause?

Accepted Answer

The number of calls exceeded the rate limit for the service tier.. HTTP 429 (Too Many Requests) is a rate-limiting response that occurs when the number of API calls exceeds the allowed threshold for the service tier. In Azure AI services, each pricing tier has a specific requests-per-second (RPS) or requests-per-minute (RPM) limit, and exceeding this limit triggers a 429 error to protect backend resources.

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Network connectivity issues.

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Authentication tokens expired.

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Invalid API keys were used.

Question 7

You are building a chat bot that uses Azure AI Language to process customer support tickets. The bot must extract entities like order numbers (e.g., ORD-12345) and issue categories. You need to choose the best approach for entity extraction to minimize development effort and ensure high accuracy.

Accepted Answer

Create a custom named entity recognition (NER) project in Azure AI Language that includes prebuilt components for order numbers and trains custom models for categories.. Azure AI Language's custom named entity recognition (NER) allows you to combine prebuilt components (like regex-based order number patterns) with custom-trained models for issue categories, minimizing development effort while achieving high accuracy. This approach leverages the built-in entity extraction capabilities of Azure AI Language without requiring intent classification or complex pipeline orchestration.

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Leverage the Text Analytics for Health API to extract entities from the support tickets.

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Use the Prebuilt Entity Extraction skill in Azure AI Search to extract order numbers and categories from the text.

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Use the Conversational Language Understanding (CLU) project type to train a model for both intent and entity extraction.

Question 8

You are developing a solution that uses Azure AI Language to analyze customer feedback. You need to determine whether the sentiment of a given sentence is positive, negative, or neutral. Which Azure AI Language feature should you use?

Accepted Answer

Sentiment Analysis. Sentiment Analysis is the correct Azure AI Language feature because it is specifically designed to evaluate text and determine whether the sentiment expressed is positive, negative, or neutral. This feature uses machine learning classifiers trained on large datasets to assign a sentiment label and confidence scores at the sentence and document level, directly matching the requirement to analyze customer feedback for sentiment polarity.

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Entity Recognition

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Language Detection

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Key Phrase Extraction

Question 9

Your company uses a custom question answering knowledge base in Azure AI Language to answer employee questions about HR policies. You need to update the knowledge base with a new set of FAQ documents that contain tables and images. What is the best way to ingest the new content?

Accepted Answer

Use the Azure AI Language REST API to add the new QnA pairs programmatically.. The best way because the Azure AI Language REST API allows you to add a source (e.g., a document) to the custom question answering knowledge base, which can include tables and images. This is more efficient than manually adding QnA pairs. Option D is incorrect because Microsoft Foundry Copilot Studio is a separate tool for building copilots and does not directly update Azure AI Language custom question answering knowledge bases. Option A is incorrect because Azure AI Search is used for indexing and searching, not for direct ingestion into the knowledge base. Option B is incorrect because manually adding QnA pairs is time-consuming and does not automatically extract structured content like tables and images from documents.

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Use Azure AI Search to index the documents and then import the index into the knowledge base.

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Manually add the new QnA pairs using the Language Studio portal.

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Use Microsoft Foundry Copilot Studio to automatically ingest the documents into the knowledge base.

Question 10

You are building a multilingual chatbot using Azure AI Language. The chatbot must handle English, Spanish, and French. You need to configure the LUIS (Language Understanding) model to support multiple languages efficiently. What is the best practice?

Accepted Answer

Create a separate LUIS app for each language and use the same intents and entities structure.. LUIS does not support a single multilingual model; each LUIS app is designed for a single primary language. To handle multiple languages efficiently, you must create a separate LUIS app per language, each with the same intent and entity schema, and route user queries to the appropriate app based on the detected language. This ensures optimal language-specific model training and accuracy.

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Use the prebuilt multilingual LUIS model and fine-tune it with your intents.

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Create a single LUIS app and add utterances in all three languages.

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Create one LUIS app with language set to 'Multilingual' and add utterances in all languages.

Question 11

You are developing a solution that uses Azure AI Translator to translate documents from English to French. You need to ensure that the translated text maintains the original formatting, such as HTML tags. Which feature should you use?

Accepted Answer

Set the content type to 'text/html' in the translation request.. The translator can preserve the original formatting when using the appropriate content type or using the 'textType' parameter set to 'html'. Option A is wrong because the glossary is for custom translations. Option B is wrong because the parallel corpus is for training custom models. Option D is wrong because transliteration converts between scripts, not formats.

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Use the Translator glossary to preserve formatting.

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Use the Translator parallel corpus to train a custom model.

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Use the Transliterate method to preserve formatting.

Question 12

You are building a custom text classification solution in Azure AI Language. You have a dataset with 10 categories and 1000 labeled documents. You need to choose the best project type. What should you use?

Accepted Answer

Custom text classification (single or multi-label). Custom text classification (single or multi-label) is the correct project type because you have a labeled dataset with 10 categories and need to train a model to classify text into those specific categories. Azure AI Language provides a custom text classification feature that allows you to train a model using your own labeled data, supporting both single-label and multi-label classification scenarios. This is the only option that enables you to build a bespoke classifier tailored to your 10-category dataset.

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Conversational Language Understanding (CLU)

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Key Phrase Extraction

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Prebuilt Text Classification API

Question 13

You need to summarize a large document using Azure AI Language. Which feature should you use?

Accepted Answer

Document summarization. Document summarization is the correct feature because it is specifically designed to generate concise summaries of large documents, extracting the most important information. Azure AI Language's document summarization uses extractive or abstractive techniques to produce a summary, directly addressing the requirement to summarize a large document.

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Key phrase extraction

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Entity recognition

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Sentiment analysis

Question 14

You are building a custom entity extraction solution using Azure AI Language. You have a small dataset (50 documents) with annotated entities. You need to train a model that can extract similar entities from new documents. What is the best approach?

Accepted Answer

Create a custom NER project in Azure AI Language and train it with your annotated data.. Azure AI Language's custom NER (Named Entity Recognition) feature allows you to train a model using your own annotated dataset. With 50 documents, you have enough labeled data to fine-tune a custom entity extraction model that learns the specific entity types and patterns in your domain, enabling accurate extraction from new documents.

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Use the prebuilt entity recognition API to extract entities.

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Use the Conversational PII entity extraction feature.

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Use the built-in entity extraction skill in Azure AI Search.

Question 15

You are building a solution that uses Azure AI Language to analyze customer support transcripts. You need to detect personally identifiable information (PII) and also redact the detected PII from the text. Which TWO features should you use? (Select TWO.)

Accepted Answer

PII detection. PII detection (Option A) is correct because it is the Azure AI Language feature specifically designed to identify categories of personally identifiable information such as names, addresses, phone numbers, and social security numbers within text. PII redaction (Option D) is correct because it is the companion feature that replaces the detected PII entities with placeholder tokens (e.g., '********') or masks them, enabling safe sharing of transcripts. Together, they fulfill the requirement to both detect and redact PII from customer support transcripts.

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Translation

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Sentiment analysis

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Key phrase extraction