AI-102 Implement natural language processing solutions Practice Test 14 — 15 Questions

Question 1

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?

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

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 2

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 3

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

Answer

targetLanguage

Answer

from

Question 4

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

Answer

Switch to batch transcription API

Answer

Increase the maxAlternatives parameter

Question 5

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

Answer

Use QnA Maker to extract Q&A pairs per speaker

Answer

Use Text Analytics for health to extract entities and sentiment

Question 6

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

Answer

Deploy a QnA Maker service

Answer

Translate PDFs to English

Question 7

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). Sentiment analysis (E) is correct because it returns sentiment labels and confidence scores (positive, negative, neutral, mixed) for the review text, directly satisfying the requirement to detect sentiment. Key phrase extraction (D) is correct because it identifies the main talking points in unstructured text, which fulfills the requirement to extract key phrases. Named entity recognition (A) is correct because it detects and categorizes entities such as people, places, organizations, and dates, satisfying the requirement to identify named entities. Abstractive summarization (B) is not needed because it generates new condensed text rather than performing the three requested analyses, and language detection (C) is not required because the scenario does not ask to identify the review language.

Answer

Abstractive summarization

Answer

Language detection

Question 8

A developer is deploying a custom text classification model in Azure AI Language. The model must be accessible via a REST API with low latency. Which TWO actions should the developer take?

Accepted Answer

Obtain the endpoint URL and primary key from Language Studio. Option D is correct because deploying the custom text classification model to a real-time endpoint in Azure AI Language exposes a synchronous REST API that returns predictions immediately, satisfying the low-latency requirement. Option C is correct because, to call that REST API, the developer must obtain the endpoint URL and the primary key (or a secondary key) from the project's deployment details in Language Studio, which are used in the Ocp-Apim-Subscription-Key header for authentication. Option A is incorrect because the batch processing API is designed for asynchronous, high-volume jobs and does not provide the low-latency synchronous responses required here. Option B is incorrect because exporting the model as a Docker container is for on-premises or disconnected container deployment, not for exposing the model through the managed Azure AI Language REST endpoint. Option E is incorrect because a test endpoint in the Azure portal is intended for validation and does not provide the production-grade, low-latency real-time REST API needed for the application.

Answer

Use the batch processing API

Answer

Export the model as a Docker container

Answer

Deploy to a test endpoint in the Azure portal

Question 9

A company uses Azure AI Speech to provide real-time transcription for customer support calls. The solution must handle multiple languages and filter out profanity. Which THREE configurations are needed?

Accepted Answer

Set the SpeechConfig.SpeechRecognitionLanguage property. Option C is correct because setting SpeechConfig.SpeechRecognitionLanguage specifies the recognition locale (for example, en-US or fr-FR) so the Speech SDK loads the appropriate acoustic and language model for the target language. Option D is correct because using the Speech SDK with intermediate results (via Recognizing events or continuous recognition) delivers real-time partial transcriptions as the caller speaks, which is required for live customer support transcription. Option E is correct because enabling the ProfanityFilter option (for example, setting Profanity to Masked or Removed in the SDK configuration) filters profane words from the recognized text. Option A is not appropriate because the Batch Transcription REST API is asynchronous and designed for processing stored audio files, not real-time streaming. Option B is unnecessary because custom speech models are only needed to improve accuracy for domain-specific vocabulary or accents, not merely to handle multiple languages, which built-in models already support.

Answer

Use the Batch Transcription REST API

Answer

Deploy a custom speech model for each language

Question 10

You are a solution architect at a media company. The company uses Azure AI Speech to generate subtitles for videos. The current solution uses the batch transcription API and takes several hours to process a 1-hour video. The business requires near-real-time subtitles for live streaming events. You need to design a new solution that provides low-latency transcription. You have the following options:

Option A: Use the batch transcription API with a higher priority queue.

Option B: Use the Speech-to-text REST API for real-time streaming with the Speech SDK.

Option C: Use the Azure AI Language API to transcribe audio from a file.

Option D: Use Azure AI Video Indexer to generate subtitles.

Accepted Answer

Option B. The Speech-to-text REST API with the Speech SDK supports real-time streaming transcription, which provides low-latency results suitable for live streaming events. Unlike the batch transcription API, which processes audio asynchronously and can take hours, the streaming API processes audio chunks in near-real-time, returning partial and final results with sub-second latency.

Answer

Option C

Answer

Option D

Answer

Option A

Question 11

A company is building a chatbot using Azure OpenAI Service to handle customer inquiries. The bot sometimes responds with incorrect or fabricated information. The team wants to ground the model responses using their own product documentation stored in Azure Cognitive Search. Which configuration should they implement?

Accepted Answer

Configure Azure OpenAI on your data with Azure Cognitive Search as the data source.. It directly integrates Azure Cognitive Search as a data source for Azure OpenAI, enabling the model to retrieve and ground its responses in the indexed product documentation. This approach uses a 'retrieve-then-read' pattern where the search results are injected into the prompt, reducing hallucinations by constraining the model's output to verified content.

Answer

Use Azure AI Document Intelligence to extract text and generate embeddings, then store them in a vector database for direct similarity search.

Answer

Fine-tune the GPT model on the product documentation dataset.

Answer

Enable the semantic ranker in Azure Cognitive Search to improve the relevance of search results.

Question 12

A healthcare organization needs to redact personally identifiable information (PII) from patient records before using them for research. They have large volumes of unstructured text in multiple languages. Which Azure AI service should they use?

Accepted Answer

Azure AI Language (PII detection feature). Azure AI Language's PII detection feature is specifically designed to identify and redact personally identifiable information from unstructured text. It supports multiple languages and can handle large volumes of text, making it the correct choice for redacting PII from patient records before research use.

Answer

Azure AI Content Safety

Answer

Azure AI Document Intelligence (formerly Form Recognizer)

Answer

Azure AI Translator

Question 13

You are building a multilingual customer support chatbot using Azure AI Language. The bot must understand user intents in English, Spanish, and French. You have pre-existing labeled data in English only. The solution should minimize manual labeling effort. Which approach should you recommend?

Accepted Answer

Use the multilingual option in conversational language understanding (CLU) and train on the English data only.. Azure AI Language's conversational language understanding (CLU) supports a multilingual project option that allows you to train a single model on labeled data in one language (e.g., English) and have it generalize to understand intents in other languages (e.g., Spanish and French) without needing additional labeled data. This directly minimizes manual labeling effort while still leveraging the pre-existing English data.

Answer

Use Azure AI Translator to detect the language and route to a rules-based intent handler for each language.

Answer

Build a separate CLU project for each language and use the English data to bootstrap labeling with active learning.

Answer

Translate the English labeled data into Spanish and French using Azure AI Translator, then train a separate CLU model per language.

Question 14

You are deploying an Azure AI Language custom text classification model. You need to ensure the model meets performance requirements before promoting it to production. Which two actions should you take? (Choose two.)

Accepted Answer

Evaluate the model on a held-out test set that was not used during training.. Option A is correct because evaluating the custom text classification model on a held-out test set that was never used during training provides an unbiased estimate of generalization performance, which is essential before promoting the model to production. Option B is correct because reviewing the confusion matrix reveals per-class precision/recall patterns and shows exactly which classes are frequently misclassified, enabling targeted data or label improvements. Option C is incorrect because Azure AI Language does not require a fixed 95% accuracy threshold on a cross-validation split; performance targets are scenario-specific and cross-validation is not the standard evaluation workflow for this service. Option D is incorrect because computing accuracy on the training set measures memorization rather than generalization and will be optimistically biased. Option E is incorrect because comparing to a majority-class baseline is a useful sanity check but is not one of the required actions for validating a custom text classification model before production.

Answer

Ensure the model achieves at least 95% accuracy on a cross-validation split.

Answer

Use the training set to compute accuracy and ensure it is above 90%.

Answer

Compare the model's performance to a baseline model that always predicts the most common class.

Question 15

Your organization uses Azure AI Language to perform sentiment analysis and opinion mining on product reviews. You notice that the sentiment scores are often neutral even when the review text contains clearly positive or negative opinions. You suspect the model is not capturing the nuances. Which three actions could improve the sentiment analysis accuracy? (Choose three.)

Accepted Answer

Provide more labeled training examples that cover a wider variety of writing styles and sentiments.. Providing more labeled training examples that cover a wider variety of writing styles and sentiments directly improves the custom model's ability to learn nuanced patterns. Azure AI Language's custom sentiment analysis relies on supervised learning; more diverse, high-quality labeled data helps the model generalize better and reduces the tendency to default to neutral scores for ambiguous or complex reviews.

Answer

Pre-process the text with key phrase extraction to highlight important terms before sentiment analysis.

Answer

Use the basic sentiment analysis API without any customization.