AI-102 Implement natural language processing solutions Practice Test 16 — 15 Questions

Question 1

You are building a solution that analyzes customer feedback in real-time using Azure AI Language. The feedback is streamed from a web app and must be processed within 1 second. You need to extract sentiment and key phrases. Which service should you use?

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

Accepted Answer

Azure AI Language synchronous API. The synchronous API is built for immediate analysis of short texts, making it ideal for real-time sentiment and key phrase extraction from streaming feedback. Asynchronous and batch options introduce delays, and containers are not optimized for low-latency cloud-based streaming.

Answer

Azure AI Language asynchronous API

Answer

Azure AI Language container

Answer

Azure AI Language batch processing with Azure Blob Storage

Question 2

You are building an Azure AI Language solution that must extract named entities from support tickets and classify each entity as a person, organization, or location. The tickets are stored as UTF-8 text files. You need to call the REST API for Named Entity Recognition (NER) and ensure the response includes entity categories. Which request should you send?

Accepted Answer

POST to https://<resource>.cognitiveservices.azure.com/language/:analyze-text?api-version=2023-04-01 with a JSON body containing "kind": "EntityRecognition" and the documents array.. The correct request uses the unified Language service analyze-text endpoint with the EntityRecognition task, which returns entities with categories such as Person, Organization, and Location. The older Text Analytics endpoint is deprecated, and other task types like KeyPhraseExtraction do not provide entity categorization. Using POST with a JSON body is mandatory for analyze-text.

Answer

POST to https://<resource>.cognitiveservices.azure.com/text/analytics/v3.1/entities/recognition/general with the documents array.

Answer

POST to https://<resource>.cognitiveservices.azure.com/language/:analyze-text?api-version=2023-04-01 with a JSON body containing "kind": "KeyPhraseExtraction" and the documents array.

Answer

GET to https://<resource>.cognitiveservices.azure.com/language/:analyze-text?api-version=2023-04-01 with the text as a query parameter.

Question 3

A support team wants to build a bot that answers employee questions by using a set of internal HR policy documents. The team does not want to author question-and-answer pairs manually and needs the bot to return the most relevant passage from the documents, with the source cited. The documents are in English and are updated frequently in a blob container. Which Azure AI Language feature should the team use?

Accepted Answer

Custom question answering with a project that imports the documents and enables the option to extract answers from the source content. Custom question answering supports importing documents and automatically generating question-and-answer pairs, plus extracting answers directly from source content. It returns the best matching passage with a citation, and it can refresh from a blob container as documents change, meeting the no-manual-authoring and source-citation requirements.

Answer

Named Entity Recognition with a custom model trained on HR policies

Answer

Conversational language understanding with an intent for each HR topic

Answer

Custom text classification with a single-label project

Question 4

You are designing a solution that uses Azure AI Language's conversational language understanding (CLU) to interpret user requests in a banking app. The app must handle utterances like 'Transfer $500 from savings to checking' and extract the amount, source account, and destination account. Which two actions should you perform when creating the CLU project? (Choose two.)

Accepted Answer

Create entities for 'Amount', 'SourceAccount', and 'DestinationAccount'.. To correctly interpret the banking utterances, you must define intents to capture the user's goal and entities to extract the specific data fields. Intents like 'TransferMoney' and entities for 'Amount', 'SourceAccount', and 'DestinationAccount' are fundamental. Prebuilt entities alone lack specificity, and custom training is required.

Answer

Train the model using only the default training set provided by Azure.

Answer

Add a prebuilt entity for 'Number' to automatically extract the amount.

Answer

Configure the project to use multiple languages for utterance interpretation.

Question 5

A logistics company receives shipping manifests as PDF documents. They need to extract the shipper name, consignee, and total weight from each document. The documents have a consistent layout. Which Azure AI service should they use?

Accepted Answer

Azure AI Document Intelligence with a custom extraction model.. Document Intelligence is built to extract fields from documents, especially forms with consistent layouts. A custom extraction model can be trained to identify shipper name, consignee, and total weight accurately. The other services either do not handle document layout or are not designed for field extraction from PDFs.

Answer

Azure AI Translator to convert the PDF to text and then parse with regex.

Answer

Azure AI Language with custom named entity recognition (NER).

Answer

Azure AI Vision with OCR and then custom text classification.

Question 6

You are designing a solution that uses Azure AI Language custom question answering. The knowledge base contains 200 question-answer pairs. Users report that the bot sometimes returns answers to questions that are semantically similar but not actually asked. You need to reduce these false positives while maintaining the ability to answer paraphrased questions. What should you configure?

Accepted Answer

Increase the confidence threshold score for the project.. The confidence threshold determines the minimum score required for an answer to be returned. By increasing it, the bot becomes more selective, reducing false positives from semantically similar questions. Active learning and alternative questions improve coverage but do not directly filter low-confidence matches. The default answer is only a fallback and does not affect matching behavior.

Answer

Set the 'default answer' to a custom message indicating no answer was found.

Answer

Enable the 'Enable active learning' option.

Answer

Add more alternative questions to each question-answer pair.

Question 7

You have a Conversational Language Understanding (CLU) project in Azure AI Language. Users frequently type utterances such as 'Book a flight from Seattle to Tokyo next Friday for two people.' The solution must extract the origin city, destination city, date, and passenger count as separate structured values so the booking system can act on them. You need to configure the project to capture these values. What should you do?

Accepted Answer

Add entities to the project and label them in the training utterances, using prebuilt entities where available.. In Conversational Language Understanding, entity extraction depends on defining entities and labeling their occurrences in training utterances. Adding entities for the four values and labeling them, using prebuilt components such as geography and number where applicable, enables the model to return those values as structured fields for the booking system.

Answer

Add more intents and assign each utterance to a single intent.

Answer

Enable sentiment analysis and opinion mining on the CLU project.

Answer

Increase the training data by duplicating existing utterances with slight wording changes.

Question 8

You are using Azure AI Language to analyze customer feedback. You need to identify the sentiment of each sentence within a review, not just the overall document sentiment. Which feature should you use?

Accepted Answer

Sentiment analysis with opinion mining enabled.. Sentiment analysis in Azure AI Language can return sentiment at the document and sentence level when opinion mining is enabled. This provides the granularity needed to see sentiment per sentence. Key phrase extraction, custom classification, and entity linking do not offer built-in sentence-level sentiment.

Answer

Key phrase extraction.

Answer

Custom text classification with sentiment labels.

Answer

Entity linking.

Question 9

You are building a solution that must detect the sentiment polarity (positive, negative, neutral, or mixed) of incoming customer support tickets written in English and German. You want to use a single Azure AI Language resource and avoid maintaining separate models per language. Which approach should you use?

Accepted Answer

Call the Sentiment Analysis feature of Azure AI Language with the language parameter set to 'auto' so the service detects the language and returns sentiment for both English and German text.. The prebuilt Sentiment Analysis capability in Azure AI Language natively supports multiple languages and can automatically detect the input language. Pointing a single resource at both English and German tickets avoids the overhead of separate resources, translation pipelines, or custom model training while still returning polarity and confidence scores.

Answer

Create two separate Azure AI Language resources, one configured for English and one for German, and route tickets based on a language detection step.

Answer

Train a custom sentiment model in Azure AI Language using labeled German and English tickets, then deploy it as a custom single-label classification project.

Answer

Use the Translator service to convert German tickets to English, then call Sentiment Analysis only on the translated English text.

Question 10

You are building an Azure AI Language solution that analyzes customer support emails. You need to detect the language of each email before routing it to the correct translation pipeline. You call the Language Detection API with the following request body: {"kind": "LanguageDetection", "analysisInput": {"documents": [{"id": "1", "text": "Bonjour, je besoin d'aide avec mon compte."}]}}. What will the response contain?

Accepted Answer

A single document result with the detected language code 'fr', a confidence score, and the original text with offsets.. The Language Detection API returns the detected language code, confidence score, and text metadata for each document. It does not return all languages, translated text, or errors for common punctuation. The correct response includes the ISO code 'fr' and a confidence score, which you can use to route the email appropriately.

Answer

The translated version of the text in English, because the default target language is English.

Answer

A list of all supported languages with confidence scores for each, allowing you to select the highest-scoring language.

Answer

An error because the text contains an apostrophe, which is not supported in the JSON payload.

Question 11

You plan to deploy a custom named entity recognition (NER) project in Azure AI Language. The project must extract supplier names and contract identifiers from procurement documents. You need to prepare the project so that the model can be trained and evaluated before deployment. Which two actions should you perform? (Choose two.)

Accepted Answer

Upload and label training documents by selecting the text spans for SupplierName and ContractId entities.. To train a custom NER model, you create a project of the custom named entity recognition type with the correct language, then upload and label documents by marking the spans for each entity such as SupplierName and ContractId. Training and evaluation follow, and only then can the model be deployed for extraction.

Answer

Define a list of intents that correspond to each procurement document type.

Answer

Create a question answering knowledge base that contains the procurement documents.

Answer

Enable opinion mining so the model can detect sentiment around supplier mentions.

Question 12

You are building a solution that must summarize long documents in real time as they are uploaded to Azure Blob Storage. The summaries must be concise and capture the main points. You want to use Azure AI Language. Which feature should you use?

Accepted Answer

Abstractive summarization. Abstractive summarization in Azure AI Language generates new, concise sentences that capture the main ideas of a document. It is designed for producing brief summaries and is available as a real-time API. Extractive summarization selects existing sentences, which may be less concise. Key phrase extraction and NER provide different types of analysis and do not generate summaries.

Answer

Extractive summarization

Answer

Key phrase extraction

Answer

Named entity recognition (NER)

Question 13

You are building an Azure AI Language solution that ingests support tickets and routes each ticket to the correct department. Each ticket must be assigned exactly one department, and departments are defined only by the examples you label in Language Studio. You need to build the model with the fewest labeling and configuration steps. Which project type and configuration should you use?

Accepted Answer

A custom single-label classification project with one class per department. The scenario requires exactly one department per ticket and departments defined purely by labeled examples, which is precisely what custom single-label classification provides. Each department maps to one class, so the deployed model returns a single predicted department that can drive routing without additional logic. The other project types either permit multiple labels, extract text spans, or model utterances, none of which enforces a single whole-document category.

Answer

A custom multi-label classification project with one class per department

Answer

A custom named entity recognition project with one entity type per department

Answer

A conversational language understanding project with one intent per department

Question 14

You are implementing a conversational language understanding (CLU) project in Azure AI Language. Your utterances include entities that are sometimes a single word and sometimes a multi-word phrase, such as 'New York' and 'San Francisco'. You need the model to correctly capture these multi-word entities during training and prediction. Which entity component type should you use?

Accepted Answer

Learned entity component. Learned entity components in CLU are trained from labeled utterances, allowing the model to identify entity spans based on surrounding context. This is the correct choice for multi-word entities such as city names that vary in phrasing and cannot be captured reliably by exact-match lists or fixed regex patterns.

Answer

Prebuilt entity component

Answer

Regex entity component

Answer

List entity component

Question 15

You are developing a solution that uses Azure AI Language's custom named entity recognition (NER) to extract product names from technical support tickets. After training a model, you notice it performs well on the training set but poorly on new tickets. You need to improve the model's ability to generalize. What should you do?

Accepted Answer

Add more labeled examples that include variations in phrasing, context, and entity placement.. Poor generalization on new data indicates overfitting. The best remedy is to expand the training set with varied examples that reflect real-world diversity, helping the model learn robust patterns. Increasing epochs or reducing data would exacerbate overfitting, and prebuilt models cannot extract custom entities like product names.

Answer

Reduce the training dataset size to prevent the model from memorizing too much information.

Answer

Increase the number of training epochs to allow the model to learn more from the existing data.

Answer

Switch to a prebuilt entity extraction model, because custom models cannot generalize.