Free AI-103 Text Analysis Solutions Practice Questions

The Implement text analysis solutions domain makes up 10–15% of the Microsoft AI-103 exam. Practice free questions covering sentiment analysis, entity recognition, language detection, translation, and speech-to-text with Azure AI Language and Speech — each with the correct answer and a detailed explanation.

AI-103 Text Analysis Solutions Sample Questions with Answers

Sample Question 1 — Implement text analysis solutions

A product team stores plain-text survey comments in Azure. They need to label each comment as positive, neutral, or negative and identify which product features customers praise or criticize. The results will be shown in a dashboard, not used for open-ended generation. Which approach should the developer choose?

  1. A. Use an Azure AI text analysis capability for sentiment analysis with opinion mining. (Correct answer)
  2. B. Use a Microsoft Foundry generative model to rewrite each comment as marketing copy.
  3. C. Use translation to convert every comment into one language before any analysis.
  4. D. Use speech-to-text to infer the tone of each survey response.

Correct answer: A

Explanation: Correct answer (A): Sentiment analysis with opinion mining is the best fit because the requirement is standard text analysis on already-available text: classify sentiment and identify which aspects or features are being praised or criticized. This is exactly the kind of task Azure AI text analysis capabilities are designed for, and it is more direct than using open-ended generation. Why the other options are wrong: - Option B: A generative model can produce text, but rewriting comments as marketing copy does not solve the requirement to classify sentiment and identify praised or criticized features in a consistent analytics pipeline. - Option C: Translation changes language, not meaning extraction. The stem does not say translation is required, so translating first adds unnecessary work and does not directly perform sentiment or opinion mining. - Option D: Speech-to-text is for spoken audio. The source material is already text, so introducing a speech step is unnecessary and does not directly provide sentiment analysis.

Sample Question 2 — Implement text analysis solutions

An insurance intake app receives already-extracted text from claim notes. Adjusters want the app to highlight customer names, company names, and city names so they can route claims to the right team. Which capability is the best fit?

  1. A. Use an Azure AI text analysis capability for named entity recognition. (Correct answer)
  2. B. Use an Azure AI text analysis capability for key phrase extraction.
  3. C. Use an Azure AI text analysis capability for sentiment analysis.
  4. D. Use text-to-speech to read each note aloud to the adjusters.

Correct answer: A

Explanation: Correct answer (A): Named entity recognition is the best choice because the requirement is to identify typed entities such as people, organizations, and locations from text. That differs from key phrase extraction, which surfaces important phrases or topics but does not specifically classify them as names, companies, or cities. Why the other options are wrong: - Option B: Key phrase extraction can identify important topics or phrases, but it is not the best choice when the app must specifically detect and classify names, company names, and city names. - Option C: Sentiment analysis determines tone or opinion, not structured entities such as people, organizations, or locations. - Option D: Text-to-speech synthesizes audio from text. It does not extract entities from the claim notes.

Sample Question 3 — Implement text analysis solutions

A service desk receives chat messages in several supported languages. Before choosing the correct downstream pipeline, the app must determine the language of each text message. The team wants the lowest-latency approach that avoids unnecessary translation. What should the app do first?

  1. A. Translate every message to English and then inspect the translation.
  2. B. Run language detection on the source text. (Correct answer)
  3. C. Convert each message to speech and analyze the audio.
  4. D. Use text-to-speech to generate audio previews for reviewers.

Correct answer: B

Explanation: Correct answer (B): Language detection is the direct and efficient first step because the messages already exist as text and the requirement is to identify the language before routing. Translating everything first adds cost and latency and is unnecessary when the goal is simply to detect the input language. Why the other options are wrong: - Option A: Translating first solves a different problem. It adds cost and latency when the actual requirement is just to identify the language of the source text. - Option C: The source material is already text, so converting it to speech and then analyzing audio would add an unnecessary and inefficient step. - Option D: Text-to-speech creates spoken audio from text. It does not identify the language for pipeline routing.

Sample Question 4 — Implement text analysis solutions

A voice-enabled support assistant receives spoken Italian questions. Internal supervisors want an English text version of each customer utterance for review, and the assistant must reply to the customer with spoken Italian. Which Azure-native design best fits?

  1. A. Use speech-to-text only, because translation is never needed when the output is text.
  2. B. Use speech translation for the spoken input, and use text-to-speech for the spoken Italian response. (Correct answer)
  3. C. Use text-to-speech on the customer's audio, and then summarize the result.
  4. D. Send the raw audio directly to text sentiment analysis without transcription or translation.

Correct answer: B

Explanation: Correct answer (B): Speech translation is the right choice when spoken input must be translated into another language, which is exactly the need for an English text version of spoken Italian. Text-to-speech is then used to synthesize the assistant's reply as spoken Italian. This matches the required speech and language conversion flow. Why the other options are wrong: - Option A: Speech-to-text would transcribe the audio, but it would not provide the required English text version of the spoken Italian input. - Option C: Text-to-speech synthesizes audio from text. It does not convert incoming speech into text or translation. - Option D: Text analysis capabilities operate on text, not raw audio. The audio must first be transcribed or translated from speech.

Sample Question 5 — Implement text analysis solutions

A logistics app reads free-form escalation emails and must populate a workflow with fields named incident_type, business_impact, requested_deadline, and approval_required. The current prompt asks a model to "return JSON," but the app still receives malformed output or missing fields. What is the best change?

  1. A. Add more descriptive wording to the prompt so the model sounds more formal.
  2. B. Replace the plain prompt with schema-constrained JSON output or tool/function calling in Foundry. (Correct answer)
  3. C. Switch to sentiment analysis, because sentiment guarantees structured fields.
  4. D. Add content safety filtering, because it verifies that every field is present.

Correct answer: B

Explanation: Correct answer (B): When downstream automation depends on reliable structure, plain prompting is not enough. A schema-constrained response or tool/function calling is more robust for custom business fields because it guides the model toward valid machine-readable output and reduces malformed JSON compared with prompt-only instructions. Why the other options are wrong: - Option A: Prompt wording can help quality, but it does not guarantee valid JSON or required fields when the workflow depends on strict structure. - Option C: Sentiment analysis is a built-in text analysis task for tone, not a method for extracting a custom business schema from free-form emails. - Option D: Content safety filtering helps detect harmful content categories, but it does not validate that the JSON is complete, well formed, or correct.

Sample Question 6 — Implement text analysis solutions

A customer insights pipeline receives already-written product reviews in Spanish, German, and English. The business wants the main discussion topics from each review, not a translation, and the chosen Azure AI text analysis capability supports those languages. The team currently translates everything to English first. Management wants lower latency and cost. What should the developer do?

  1. A. Keep translating everything first, because key phrase extraction only works after translation.
  2. B. Run key phrase extraction on the source text and translate only if a translated copy is needed later. (Correct answer)
  3. C. Replace the pipeline with text-to-speech, because audio analysis is less expensive than text analysis.
  4. D. Use content safety filtering instead of topic extraction.

Correct answer: B

Explanation: Correct answer (B): The requirement is to identify main topics from multilingual text, not to translate it. If the chosen analysis capability supports the source languages, running key phrase extraction directly on the source text is the better design because it avoids unnecessary translation cost and latency. Translation should be added only when a translated output is actually needed. Why the other options are wrong: - Option A: This adds an unnecessary translation stage. The stem explicitly says the analysis capability supports the source languages and the business does not primarily need translated output. - Option C: The reviews are already text, so moving to a speech workflow would introduce unnecessary processing rather than reduce it. - Option D: Content safety filtering checks for harmful content categories. It does not extract the main discussion topics from reviews.

Sample Question 7 — Implement text analysis solutions

An Azure-hosted chat app must detect personal data in conversation transcripts before storing them. Security policy forbids embedded secrets and requires least-privilege access to Azure AI services. Which implementation is best?

  1. A. Call a dedicated PII detection capability and authenticate the app with managed identity and Azure RBAC. (Correct answer)
  2. B. Call sentiment analysis and store an API key in source control for easier deployment.
  3. C. Call content safety filtering and place the service key in app settings for all environments.
  4. D. Deploy a Foundry model endpoint and rely on the endpoint selection to enforce app security.

Correct answer: A

Explanation: Correct answer (A): The app needs two things: the right capability and the right security pattern. A dedicated PII detection capability fits the requirement to identify sensitive personal data, and managed identity with Azure RBAC is the preferred Azure-hosted access method because it avoids embedded secrets and supports least privilege. Why the other options are wrong: - Option B: Sentiment analysis is the wrong capability for identifying personal data, and storing API keys in source control directly violates the no-embedded-secrets requirement. - Option C: Content safety filtering is not the best tool for dedicated PII identification, and using service keys still leaves secret management in the application path instead of using managed identity. - Option D: Selecting or deploying a model endpoint does not by itself implement PII detection, secure the application, or configure least-privilege RBAC access.

Sample Question 8 — Implement text analysis solutions

A compliance team uses an AI workflow to summarize customer complaints and extract recommended follow-up actions. Management wants to automatically send regulatory notices whenever the output indicates a serious violation. A mistaken summary or extraction would have legal impact. What is the best safeguard to add?

  1. A. Rely only on content safety filtering before sending the notice.
  2. B. Increase the model size and let the workflow act automatically.
  3. C. Require human review or downstream validation before the workflow triggers the notice. (Correct answer)
  4. D. Deploy the same prompt to another endpoint so two models can make the decision without people.

Correct answer: C

Explanation: Correct answer (C): Because the workflow can trigger a high-impact legal action, the output should not be trusted blindly. Human review or downstream validation is the best safeguard in sensitive workflows where a summarization or extraction error could create regulatory or legal consequences. Why the other options are wrong: - Option A: Content safety filtering can help with harmful content categories, but it does not prove that the complaint summary or extracted action is accurate enough to justify a legal notice. - Option B: A larger model might change output quality, but it does not remove the need for governance and validation in a legally sensitive workflow. - Option D: Using two automated model calls still leaves the decision fully automated. It does not provide the governance safeguard needed for legally significant actions.

Keep Practicing

Take the 10-question AI-103 quick-start test across all 5 domains, or return to the AI-103 practice test hub for 200+ questions, exam details, and a study plan.