AI-103 Text Analysis Practice Test 5 — 15 Questions

Question 1

You need to extract key concepts from customer feedback. Which TWO characteristics describe key phrase extraction in Azure AI Language? (Choose two.)

Accepted Answer

It works well on unstructured text documents.. Option C is correct because Azure AI Language key phrase extraction is designed to process unstructured text documents, such as customer feedback, reviews, or free-form comments, without requiring any special formatting or pre-labeling. Option D is correct because the Key Phrase Extraction API returns a list of strings representing the main talking points or key phrases found in the input text, which is exactly what is needed to extract key concepts from customer feedback. Option A is incorrect because evaluating emotional tone is the job of sentiment analysis, not key phrase extraction. Option B is incorrect because key phrase extraction does not rely on a pre-defined taxonomy of thousands of categories; that describes custom text classification or entity linking with a taxonomy. Option E is incorrect because automatic translation is a separate Azure AI service (Translator), not a feature of key phrase extraction.

Answer

It evaluates the emotional tone of sentences.

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It requires a pre-defined taxonomy of thousands of categories.

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It automatically translates documents into English.

Question 2

You are designing an architecture that processes incoming multilingual support emails using Azure AI Language. Which THREE processing steps should you include in your pipeline? (Choose three.)

Accepted Answer

Execute named entity recognition to extract relevant organizations, locations, and names.. Option C is correct because Azure AI Language's language detection endpoint must be called first to identify each email's primary language, which is essential for routing multilingual content to the appropriate language-specific models. Option A is correct because named entity recognition (NER) extracts entities such as organizations, locations, and person names from the email text, providing structured, actionable information for support workflows. Option E is correct because sentiment analysis and PII detection are supported Azure AI Language features that help gauge customer tone and identify/redact sensitive data in the email documents. Option B is not appropriate because speech-to-text acoustic models are unrelated to processing text emails and training a model per email is impractical. Option D is not appropriate because converting text to raw binary executables serves no analytical purpose and would destroy the text needed for language analysis.

Answer

Train a new speech-to-text acoustic model for every email received.

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Convert all email text into raw binary executable files before analysis.

Question 3

You are configuring an Azure AI Language resource. Which TWO pricing tiers are available for provisioning the resource in the Azure portal? (Choose two.)

Accepted Answer

Standard (S). The Standard (S) tier is correct because Azure AI Language resources are provisioned under the Standard (S) pricing tier, which provides the full production-level throughput and SLA-backed service for text analytics, question answering, and conversational language understanding features. The Free (F0) tier is also correct because Azure AI Language offers a Free (F0) tier for evaluation and low-volume testing, subject to limited transactions, document size, and rate limits per subscription. The Unlimited (U1) option is not a valid Azure AI Language pricing tier; no such tier exists in the Azure portal provisioning flow. Developer (DEV) is not an Azure AI Language tier; developer-oriented access is handled through keys and endpoints on S or F0, not a separate DEV SKU. Enterprise (E1) is likewise not an Azure AI Language pricing tier; enterprise capabilities are delivered through the Standard tier rather than a distinct E1 SKU.

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Unlimited (U1)

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Developer (DEV)

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Enterprise (E1)

Question 4

You are implementing opinion mining in an application that analyzes hotel reviews. Which THREE pieces of information does opinion mining return for an evaluated sentence? (Choose three.)

Accepted Answer

Sentiment polarity (positive, negative, neutral) for the aspect. Opinion mining (aspect-based sentiment analysis) decomposes an evaluated sentence into its key components: option B is correct because it returns the sentiment polarity (positive, negative, or neutral) associated with a specific aspect, which is the core output of the analysis. Option C is correct because it identifies the target aspect, such as 'room' or 'staff', i.e., the entity or feature being evaluated in the sentence. Option D is correct because it extracts the assessment evaluation, such as 'clean' or 'rude', which is the descriptive term expressing the opinion about the aspect. Options A and E are incorrect because compiler error logs and network bandwidth utilization metrics are unrelated to natural language opinion mining outputs; they belong to software development and network monitoring domains, respectively.

Answer

Compiler error logs for client application code

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Network bandwidth utilization metrics

Question 5

You are developing a global news aggregation application using the Azure AI Language service. You need to automatically identify the primary language of incoming user-submitted articles across 120 different languages before routing them to translation pipelines. Which API endpoint should you call?

Accepted Answer

POST /language/:analyze-text?api-version=2023-04-01 with the TextLanguageDetection task. The TextLanguageDetection task in the /language/:analyze-text endpoint is specifically designed to identify the language of a document and return the detected language code and confidence score. It supports the full range of Azure AI Language languages (currently 120+), making it the correct choice for routing multilingual articles.

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POST /language/translate?api-version=3.0

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POST /language/:analyze-text?api-version=2023-04-01 with the CustomEntityRecognition task

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POST /language/analyze-text/jobs?api-version=2023-04-01 with the TextSentimentAnalysis task

Question 6

Your enterprise customer service department receives thousands of support emails daily. Management wants a system that ingests long support tickets and automatically outputs a concise summary highlighting the most critical issues. Which Azure AI Language service feature should you implement?

Accepted Answer

Text summarization (Abstractive or Extractive). Text summarization in Azure AI Language is designed to condense long documents into concise summaries, exactly matching the requirement to ingest long support tickets and output a concise summary highlighting critical issues. Both abstractive (generates new sentences) and extractive (selects key sentences) modes are available, and the service can handle large volumes of text. The other features focus on extracting specific elements (entities, key phrases, opinions) rather than producing a coherent summary.

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Named Entity Recognition (NER)

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Key phrase extraction

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Opinion mining

Question 7

You are processing customer feedback using sentiment analysis in the Azure AI Language service. You need to extract finer-grained information about specific product attributes mentioned in the reviews (e.g., finding out that the screen is bright but the battery life is poor). Which feature should you enable in your API request?

Accepted Answer

Opinion mining. Opinion mining in Azure AI Language sentiment analysis extracts fine-grained opinions about specific aspects or attributes mentioned in text, such as 'screen' and 'battery life'. It provides sentiment for each aspect, allowing you to determine that the screen is bright (positive) but battery life is poor (negative). This is exactly what the scenario requires.

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PII detection

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Language detection

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Custom text classification

Question 8

Your application needs to scan user input for Personally Identifiable Information (PII) such as Social Security numbers, credit card numbers, and physical addresses before storing logs in a cloud database. Which Azure AI Language service feature should you invoke?

Accepted Answer

Named Entity Recognition (NER) with PII detection. NER with PII detection is the Azure AI Language capability that identifies and classifies sensitive entity types — SSNs, credit card numbers, addresses, names, emails — and returns their offsets so you can redact or block them before persisting logs. It is the only option that both detects and categorizes privacy-sensitive data at the entity level. The other features analyze tone, summarize, or extract topics, none of which identify PII.

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Sentiment analysis

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Text summarization

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Key phrase extraction

Question 9

You are building a custom text classification solution using Azure AI Language in Language Studio. You have a dataset where each document can belong to multiple categories simultaneously (e.g., a news article can be tagged as both 'Technology' and 'Finance'). What classification type should you configure?

Accepted Answer

Multi-label classification. Multi-label classification is designed for scenarios where a single document can be assigned more than one category at the same time. In Azure AI Language, multi-label projects let you apply multiple labels to each document during training and return all applicable labels at inference. This matches the requirement that a news article can be tagged as both 'Technology' and 'Finance'.

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Custom named entity recognition

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Single-label classification

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Extractive summarization

Question 10

You are processing health-related clinical notes using the Azure AI Language service. You need to extract medical concepts and link them to standardized medical vocabularies such as ICD-10-CM and SNOMED-CT. Which specific feature and endpoint task should you use?

Accepted Answer

Healthcare text analytics (Healthcare-specific NER and relation extraction). Azure AI Language's healthcare text analytics feature is specifically designed to extract medical entities and relations from clinical text, and it can link entities to standardized vocabularies like ICD-10-CM, SNOMED-CT, and UMLS. It uses a dedicated endpoint task (e.g., /entities/health/jobs) and returns entities with categories, subcategories, and ontology links. This is the only option that provides medical concept extraction and vocabulary linking out of the box.

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Named Entity Recognition (NER) with standard prebuilt-entity models

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Key phrase extraction with medical domain parameters

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Custom Named Entity Recognition with a trained ICD-10 dictionary

Question 11

Your team needs to extract the most important talking points from research papers using the Azure AI Language service. You want the service to return a list of salient terms and phrases from the input document. Which API task should you execute?

Accepted Answer

Key Phrase Extraction. Key Phrase Extraction is the Azure AI Language service feature specifically designed to identify and return the main concepts or talking points from unstructured text. It analyzes the input document and outputs a list of salient terms and phrases that represent the key ideas, which directly matches the requirement to extract important talking points from research papers. The other options serve different purposes: PII Detection identifies personal information, Sentiment Analysis determines emotional tone, and Language Detection identifies the language of the text.

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PII Detection

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Sentiment Analysis

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Language Detection

Question 12

You are designing an asynchronous text analysis workflow using the Azure AI Language service to process large volumes of documents (e.g., hundreds of megabytes of text files stored in Azure Blob Storage). Which TWO features support asynchronous batch processing jobs via the /analyze-text/jobs endpoint?

Accepted Answer

Custom text classification. The /analyze-text/jobs endpoint is the Azure AI Language asynchronous batch API, and it is designed for long-running document processing tasks that submit a job and poll for results. Option C (Custom text classification) is correct because custom text classification is explicitly supported as an asynchronous job through /analyze-text/jobs, allowing you to classify large volumes of documents against your trained model. Option E (Text summarization (Abstractive)) is correct because abstractive summarization is also a long-running operation submitted via /analyze-text/jobs, which is appropriate for generating summaries over hundreds of megabytes of text. Options A (Standard sentiment analysis), B (Language detection), and D (Key phrase extraction) are not correct here because these are synchronous, single-document or small-batch features exposed through the /analyze-text endpoint (or the synchronous analyze-text call), not the asynchronous jobs endpoint.

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Standard sentiment analysis

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Language detection

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Key phrase extraction

Question 13

When configuring a custom Named Entity Recognition (NER) project in Language Studio using the Azure AI Language service, which THREE steps or configuration options are required during the model lifecycle?

Accepted Answer

Define your entity schema and types. Option A is correct because a custom NER project requires you to define the entity schema — the entity types (labels) and their descriptions — in Language Studio before you can label data, as this schema drives the labeling experience and the model's output classes. Option D is correct because after labeling your documents you must start a training job in Language Studio (or via the REST API/az CLI) that builds the model from your labeled dataset; the service handles the underlying training without you writing training code. Option E is correct because a trained custom NER model is not queryable until it is assigned to a deployment (a named deployment in the Language resource), which the client application then calls via the analyze-text endpoint. Option B is not required because Azure AI Language abstracts the base model and performs fine-tuning internally; you do not write GPU gradient backpropagation code. Option C is not required because Language Studio accepts plain-text/JSON documents in its supported formats and performs tokenization internally, so manual pre-tokenized JSON vectors are unnecessary.

Answer

Fine-tune the underlying base transformer weights via direct GPU gradient backpropagation code

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Manually convert all input text files into pre-tokenized JSON vectors

Question 14

You are reviewing the JSON response of a sentiment analysis API call from the Azure AI Language service. Which THREE properties or confidence scores are returned in the standard response payload for a given document?

Accepted Answer

Sentence-level sentiment and confidence scores. Option A is correct because the Azure AI Language sentiment analysis response includes a 'sentences' array where each sentence carries its own 'sentiment' label and 'confidenceScores' (positive, neutral, negative), enabling sentence-level analysis. Option B is correct because every document and sentence in the payload contains a 'confidenceScores' object with numeric values for the positive, neutral, and negative classes. Option D is correct because the top-level document object returns an overall 'sentiment' value that can be positive, negative, neutral, or mixed (mixed applies when sentence sentiments conflict). Option C is incorrect because sentiment analysis does not perform translation; that is a separate Azure AI Translator capability. Option E is incorrect because grammar and spelling correction is not part of the sentiment analysis response payload.

Answer

Exact translation of the text into English

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Grammar and spelling correction suggestions

Question 15

You are building an Azure Functions app that calls the Azure AI Language REST API to run sentiment analysis on batches of customer survey comments. The surveys are submitted in bursts of roughly 40 documents at a time, and each document is well under 5,000 characters. The function currently sends one HTTP request per document and is hitting throttling limits during peak hours. You want to reduce the number of requests while keeping the same model and results. What should you change?

Accepted Answer

Send all 40 documents in a single request to the /language/:analyze-text endpoint using one JSON body that contains a documents array.. The analyze-text endpoint accepts a JSON body with a documents array, letting you submit multiple short documents in one synchronous call. Consolidating 40 per-document calls into one batch request lowers the request rate that triggers throttling and keeps the same prebuilt sentiment model and per-document results.

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Enable local caching in the function so identical comment text is not resubmitted to the service within the same execution window.

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Switch the function to call the /language/:analyze-text/jobs endpoint and poll the operation-location header until the long-running job completes.

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Increase the number of function instances so the per-document requests are spread across more concurrent workers.