AI-102 Implement natural language processing solutions Practice Test 11 — 15 Questions

Question 1

You are using Azure AI Language to analyze social media comments. You need to identify the language of each comment and then extract key phrases. Which TWO features should you use? (Select TWO.)

Accepted Answer

Language detection. Language detection is the correct feature because it identifies the language of each comment, which is a prerequisite for further analysis. Key phrase extraction is the second correct feature because it extracts important terms from the text, directly addressing the requirement to 'extract key phrases' after language identification.

Answer

Sentiment analysis

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Summarization

Answer

Entity recognition

Question 2

Refer to the exhibit. You are configuring an Azure AI Language resource using an ARM template. The settings include PII recognition with domain set to 'phi'. What is the effect of this setting?

Accepted Answer

It will detect protected health information (PHI) such as medical record numbers.. When you set the domain to 'phi' in PII recognition for Azure AI Language, the service is configured to detect protected health information (PHI) as defined by HIPAA, such as medical record numbers, health plan numbers, and patient names. This is a specialized subset of PII that focuses on health-related sensitive data, not general health entities or standard PII categories. Option D correctly identifies this behavior.

Answer

It will detect all standard PII categories plus additional health-specific entities.

Answer

It will enable the entity recognition model to detect health entities.

Answer

It will ignore PII detection and only detect health-related entities.

Question 3

Refer to the exhibit. You have an Azure AI Language resource named MyLangService. You need to call the conversational language understanding (CLU) API. Which URL should you use?

Accepted Answer

https://mylangservice.cognitiveservices.azure.com/language/:analyze-conversations?api-version=2022-10-01-preview. The conversational language understanding (CLU) API endpoint requires the `/language/:analyze-conversations` path with a colon prefix before the operation name, which is the correct syntax for the Azure AI Language REST API. The `api-version=2022-10-01-preview` parameter is required to specify the preview version that supports CLU. This URL structure targets the analyze-conversations operation specifically for CLU, not the general text analytics or root endpoint.

Answer

https://mylangservice.cognitiveservices.azure.com/text/analytics/v3.0/sentiment

Answer

https://mylangservice.cognitiveservices.azure.com/language/analyze-conversations?api-version=2022-10-01-preview

Answer

https://mylangservice.cognitiveservices.azure.com/

Question 4

You are a developer at a large retail company. The company receives thousands of product reviews daily. You need to build a solution that automatically categorizes reviews into positive, negative, and neutral sentiments, and also extracts key product features mentioned (e.g., battery life, screen quality) along with their associated sentiments. The solution must be scalable and cost-effective. You have access to Azure AI Language. You decide to use the built-in sentiment analysis and opinion mining features. However, after initial testing, you find that the opinion mining feature does not always correctly associate sentiments with the correct product features. For example, in the review 'The battery life is great but the screen is terrible', opinion mining might incorrectly associate 'terrible' with 'battery life'. You need to improve the accuracy of feature-sentiment association. What should you do?

Accepted Answer

Create a custom NER project in Azure AI Language to extract product features, then use the opinion mining results and post-process to associate sentiments with the extracted features.. It combines Azure AI Language's built-in opinion mining with a custom NER model to extract product features, then uses post-processing logic to correctly associate sentiments with those features. This approach addresses the core limitation of opinion mining, which can misalign sentiments when multiple features with contrasting sentiments appear in the same sentence. By first extracting features via custom NER, you can then map each sentiment phrase to the nearest or most relevant extracted entity, improving accuracy without sacrificing scalability or cost-effectiveness.

Answer

Use the PII recognition feature to identify product features as entities.

Answer

Use Conversational Language Understanding (CLU) to define intents for each product feature and train a model with labeled utterances.

Answer

Use the standard sentiment analysis API without opinion mining, and then use key phrase extraction to identify features and assign overall sentiment.

Question 5

A company is building a chatbot using Azure AI Language. The chatbot must understand user intents and extract entities like dates and locations. The solution should minimize manual labeling effort. Which feature should the team use?

Accepted Answer

CLU prebuilt entity components. CLU prebuilt entity components provide ready-made entity extraction for common types like dates and locations without requiring manual labeling. This minimizes manual effort while still allowing the chatbot to understand user intents and extract entities, as the prebuilt components are domain-agnostic and cover a wide range of entity types out of the box.

Answer

Conversational Language Understanding (CLU) Orchestration Workflow

Answer

CLU prebuilt intents

Answer

Azure AI QnA Maker

Question 6

A developer is creating a custom text classification model using Azure AI Language. The dataset has 10,000 documents across 50 categories. Which method is most suitable for labeling the data efficiently?

Accepted Answer

Use active learning in the custom text classification project. Active learning in custom text classification projects automatically selects the most informative unlabeled documents for manual review, reducing labeling effort while maximizing model accuracy. With 10,000 documents across 50 categories, active learning prioritizes ambiguous or high-uncertainty samples, making it the most efficient approach for iterative labeling.

Answer

Use a prebuilt model from the Azure AI Language service

Answer

Manually label all documents in the Language Studio

Answer

Use Azure Machine Learning designer to auto-label

Question 7

A multinational corporation uses Azure AI Language to analyze customer feedback in multiple languages. The solution must detect the language of incoming text and then perform sentiment analysis. Which approach minimizes latency and cost?

Accepted Answer

Call the language detection API followed by the sentiment analysis API. The requirement is to detect the language of incoming text and then perform sentiment analysis. The sentiment analysis API in Azure AI Language does not detect language; it requires the language to be specified. Therefore, you must first call the language detection API to identify the language, then pass that language code to the sentiment analysis API. While this involves two API calls, it is still more efficient than translating text (Option B) which adds significant latency and cost, or using cognitive skills with Azure AI Search (Option C) which adds indexing overhead. Option A is wrong because the sentiment analysis API cannot detect language on its own; it requires the language as an input parameter. Thus, Option D minimizes latency and cost by only performing the necessary steps without unnecessary transformations.

Answer

Use the sentiment analysis API with multilingual support

Answer

Use the Translator service to translate text to English, then call sentiment analysis

Answer

Store text in Azure AI Search and use cognitive skills for sentiment

Question 8

A company uses Azure AI Speech to transcribe customer service calls. They need to identify callers by name and account number. Which feature should be enabled?

Accepted Answer

Custom speech model. A custom speech model is required because the company needs to recognize specific domain terms like caller names and account numbers, which are not part of the default base speech model. By training a custom speech model with relevant audio and text data, Azure AI Speech can accurately transcribe these specialized terms, improving recognition accuracy for the call center scenario.

Answer

Speaker recognition

Answer

Pronounciation assessment

Answer

Batch transcription

Question 9

A healthcare organization uses Azure AI Language to extract medical entities from clinical notes. The solution must comply with HIPAA and redact protected health information (PHI). Which capability should the team configure?

Accepted Answer

Text Analytics for health. Text Analytics for health is a specialized Azure AI Language feature designed to extract and label medical entities from unstructured clinical text, such as diagnoses, medications, and procedures. It also includes a built-in PHI detection and redaction capability that automatically identifies and masks protected health information, enabling HIPAA compliance without requiring custom model training.

Answer

Azure Purview Information Protection

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Custom named entity recognition (NER)

Answer

Azure AI Document Intelligence

Question 10

A developer is building a multilingual translation solution using Azure AI Translator. The solution must translate text to French, German, and Spanish. Which parameter should the developer set to specify the target language?

Accepted Answer

to. In Azure AI Translator, the target language for translation is specified using the 'to' query parameter in the API request. This parameter accepts language codes such as 'fr' for French, 'de' for German, and 'es' for Spanish. The 'to' parameter is required and can be specified multiple times to translate into multiple target languages simultaneously.

Answer

language

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targetLanguage

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from

Question 11

A company uses Azure AI Speech for real-time captioning during live events. They notice a delay of 5 seconds between speech and caption display. Which action should they take to reduce latency?

Accepted Answer

Use the Speech SDK with intermediate results enabled. Enabling intermediate results in the Speech SDK allows the client to receive partial, real-time recognition hypotheses as the audio is being processed, rather than waiting for the final, fully processed result. This reduces the perceived latency from the full utterance duration (which can be several seconds) to near-instantaneous display of partial captions, directly addressing the 5-second delay.

Answer

Deploy a custom speech model

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Switch to batch transcription API

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Increase the maxAlternatives parameter

Question 12

A financial services firm uses Azure AI Language to analyze earnings call transcripts. They need to extract key phrases and identify sentiment for each speaker's turn. Which approach should they use?

Accepted Answer

Split the transcript by speaker turns and call key phrase extraction and sentiment analysis on each part. The requirement is to extract key phrases and identify sentiment per speaker turn. The Azure AI Language key phrase extraction and sentiment analysis APIs operate on individual text inputs. By splitting the transcript by speaker turns, each segment can be analyzed independently, providing per-speaker insights. Processing the entire transcript as a single document would aggregate sentiment and key phrases, losing per-speaker granularity.

Answer

Call the prebuilt sentiment analysis API on the entire transcript

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Use QnA Maker to extract Q&A pairs per speaker

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Use Text Analytics for health to extract entities and sentiment

Question 13

A developer is creating a custom question answering project in Azure AI Language. The knowledge base contains product manuals in PDF format. Which step is essential before importing the PDFs?

Accepted Answer

Ensure PDFs are in a supported format and accessible. Before importing PDFs into a custom question answering project in Azure AI Language, the essential step is to ensure the PDFs are in a supported format (e.g., searchable PDF, not scanned images without OCR) and accessible via a valid URL or local path. This is because the import process relies on the service being able to read and extract text from the documents; unsupported or inaccessible files will cause the import to fail.

Answer

Create an Azure AI Search index

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Deploy a QnA Maker service

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Translate PDFs to English

Question 14

A company is building a bot using Microsoft Copilot Studio (formerly Power Virtual Agents). They want to use Azure AI Language to understand user intents. Which TWO components are required?

Accepted Answer

A Conversational Language Understanding (CLU) app. Conversational Language Understanding (CLU) is the Azure AI Language service that enables intent recognition and entity extraction from user utterances. When building a bot with Microsoft Copilot Studio, you can integrate a CLU app to handle complex natural language understanding, allowing the bot to route user intents to appropriate topics or actions.

Answer

A QnA Maker knowledge base

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An Azure AI Translator resource

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An Azure AI Bot Service bot

Question 15

A company uses Azure AI Language to analyze customer reviews. They need to detect sentiment, extract key phrases, and identify named entities. Which THREE capabilities should they combine?

Accepted Answer

Named entity recognition (NER). Named entity recognition (NER) is correct because it identifies and categorizes named entities (e.g., people, organizations, locations) in text, which is essential for extracting structured information from customer reviews. Combined with sentiment analysis and key phrase extraction, NER enables a comprehensive understanding of customer feedback by detecting sentiment, surfacing important terms, and recognizing specific entities mentioned.

Answer

Abstractive summarization

Answer

Language detection