AI-102 Implement natural language processing solutions Practice Test 10 — 15 Questions

Question 1

A company uses Azure AI Language Service with Custom Entity Recognition to extract invoice fields. The model correctly extracts invoice numbers but fails to extract dates in the format 'dd/mm/yyyy'. The training data includes dates in 'mm/dd/yyyy' format. What is the most likely issue?

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

Accepted Answer

The training data does not contain examples with the 'dd/mm/yyyy' format. Custom Entity Recognition in Azure AI Language Service learns patterns from labeled training data. Since the training data only contains dates in 'mm/dd/yyyy' format, the model has not seen any examples of 'dd/mm/yyyy' and therefore cannot generalize to that format. The model relies on the exact token sequences and date structures present in the training set, so missing format variations directly cause extraction failures.

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The dates exceed the maximum entity length

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The language detection is incorrectly identifying the locale

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The model is overfitting to invoice numbers

Question 2

Which TWO actions should you take to ensure that an Azure AI Language Service custom entity recognition model complies with data privacy regulations?

Accepted Answer

Configure data retention policies to delete data after processing. Option D is correct because configuring data retention policies to delete data after processing ensures that sensitive text used by the Azure AI Language custom entity recognition model is not stored longer than necessary, directly supporting data minimization and storage-limitation requirements of privacy regulations such as GDPR. Option E is correct because anonymizing or removing PII from training data prevents the model from learning and potentially exposing personal data, reducing privacy risk at the source and aligning with principles of data protection by design. The other options do not address privacy compliance: A (using prebuilt models) changes model type but does not itself enforce privacy controls, B (increasing training epochs) only affects model accuracy/training time, and C (enabling diagnostic logging) may aid auditing but can also create additional personal data retention unless carefully governed, so it is not one of the two required actions here.

Answer

Use prebuilt entity recognition models instead of custom

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Increase the number of training epochs

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Enable diagnostic logging for audit trails

Question 3

Which Azure AI service can be used to analyze sentiment in text data?

Accepted Answer

Azure AI Language Service. Azure AI Language Service (formerly Text Analytics) includes a built-in sentiment analysis feature that evaluates text and returns sentiment labels (positive, negative, neutral, mixed) along with confidence scores at the sentence and document level. This makes it the primary service for analyzing sentiment in text data. Azure AI Content Safety is not designed for sentiment analysis; it is intended for harmful content moderation.

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Azure AI Translator

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Azure AI Vision

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Azure AI Content Safety

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Azure AI Speech

Question 4

Refer to the exhibit. You send this request to the Azure AI Language Service for custom entity recognition. The response returns no entities. What is the most likely reason?

Accepted Answer

The projectName and deploymentName are missing from the body parameters. The Azure AI Language Service for custom entity recognition requires both `projectName` and `deploymentName` in the request body to identify which trained custom model to invoke. Without these parameters, the service cannot route the request to the correct custom model, so it returns no entities. The standard pre-built entity recognition does not require these fields, but custom entity recognition mandates them.

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The text input is too short for entity recognition

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The language parameter is set incorrectly

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The model version is not specified

Question 5

You are designing a multilingual chatbot using Azure AI Language. The chatbot must support English, Spanish, and French. You need to minimize development effort and ensure consistent intent recognition across languages. What should you do?

Accepted Answer

Use a single CLU multilingual project that supports all three languages.. A single CLU multilingual project in Azure AI Language natively supports multiple languages within one project, allowing you to define intents and entities once and provide utterances in English, Spanish, and French. The model learns shared semantic representations across languages, so intent recognition remains consistent without building separate models or translation pipelines. This directly minimizes development effort because you maintain one project, one deployment, and one set of intent definitions.

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Use the Azure AI Translator to translate all utterances to English before sending to a single English-only CLU project.

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Create a separate CLU project for each language and combine them with a routing mechanism.

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Use the Language Understanding (LUIS) service with a single app that includes language-specific utterances.

Question 6

A company wants to use Azure AI Language to automatically summarize large documents. The summarization must extract the most important sentences from each document. Which feature should they use?

Accepted Answer

Extractive summarization. Extractive summarization selects the most important sentences directly from the source document to create a concise summary, preserving the original wording. This aligns with the requirement to extract key sentences without generating new text, making it the correct choice for this scenario.

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Abstractive summarization

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Key phrase extraction

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Entity recognition

Question 7

You need to use Azure AI Language to analyze customer feedback. Which THREE analysis types are available in the Text Analytics API?

Accepted Answer

Sentiment analysis. Sentiment analysis (C) is a core Text Analytics API feature that returns sentiment labels and confidence scores (positive, neutral, negative, mixed) for documents or sentences, making it valid for analyzing customer feedback. Entity recognition (D), specifically Named Entity Recognition (NER), is also available and identifies entities such as people, places, organizations, and dates in text. Key phrase extraction (E) is another supported Text Analytics capability that returns the main concepts or topics in a document, which is useful for summarizing customer feedback. Image captioning (A) belongs to Azure AI Vision, not Azure AI Language, and speech-to-text (B) is provided by Azure AI Speech, so neither is part of the Text Analytics API.

Answer

Image captioning

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Speech-to-text

Question 8

You run the PowerShell script above to call the Text Analytics API. The response shows a sentiment label of 'positive' with a score of 0.99. However, you expected 'negative' because the word 'excellent' was meant to be sarcastic. What is the most likely reason for this result?

Accepted Answer

The model does not detect sarcasm and interprets the text literally.. The Text Analytics API performs sentiment analysis using a machine learning model that evaluates the literal wording of the text. It does not have built-in capability to detect sarcasm, irony, or implied meaning. Therefore, the word 'excellent' is interpreted as positive regardless of the intended sarcastic tone, resulting in a high positive score.

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The score threshold for negative sentiment is too high.

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The language parameter is set incorrectly.

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The API version does not support sentiment analysis.

Question 9

You are developing a chat application that uses Azure OpenAI GPT-4 to answer customer questions. You need to ensure the model does not generate harmful content. Which configuration should you set?

Accepted Answer

Enable the content filter in Azure OpenAI Service.. Azure OpenAI Service includes a built-in content filter that actively scans both input prompts and generated completions to detect and block harmful content such as hate speech, violence, or self-harm. This filter operates at the service level, providing a robust safety layer that cannot be bypassed by model configuration alone. While system prompts can guide behavior, they are not a reliable safeguard against adversarial inputs or model misuse.

Answer

Use a system prompt that instructs the model to be safe.

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Set the temperature parameter to 0.

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Set max_tokens to a low value.

Question 10

You are building a solution to extract key phrases from customer reviews using Azure AI Language. Which feature should you use?

Accepted Answer

Key Phrase Extraction. Key Phrase Extraction is the correct feature because it is specifically designed to identify and return the main talking points or important terms from unstructured text, such as customer reviews. Azure AI Language's Key Phrase Extraction API analyzes the text structure and linguistic patterns to surface the most relevant phrases, which directly addresses the requirement to extract key phrases from reviews.

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Sentiment Analysis

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Language Detection

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Named Entity Recognition

Question 11

You are designing an NLP solution to analyze legal documents. The solution must identify specific clauses and parties involved. Which Azure AI service is most appropriate?

Accepted Answer

Custom Named Entity Recognition in Azure AI Language. Custom Named Entity Extraction (Custom NER) in Azure AI Language is the correct choice because it allows you to train a model to recognize domain-specific entities like legal clauses and party names from your own labeled data. Pre-built NER only recognizes generic entity types (e.g., person, organization, location) and cannot be customized for legal terminology. This makes Custom NER the only option that meets the requirement to identify specific clauses and parties unique to legal documents.

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Pre-built Named Entity Recognition in Azure AI Language

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Text Analytics for Health

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Immersive Reader

Question 12

You need to translate a large volume of documents from English to French while preserving the original formatting. Which Azure service should you use?

Accepted Answer

Azure AI Translator (Document Translation). Azure AI Translator's Document Translation feature is specifically designed to translate entire documents while preserving the original structure, layout, and formatting (e.g., tables, headers, bullet points). It supports batch processing of large volumes of documents and maintains the source file's format in the translated output, making it the correct choice for this requirement.

Answer

Azure AI Language

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Custom Translator in Azure AI Translator

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Azure OpenAI Service with GPT-4

Question 13

You are deploying a question answering solution using Azure AI Language. The solution must be able to provide answers from a set of frequently asked questions (FAQs) in PDF format. What should you do?

Accepted Answer

Create a custom question answering project and add the PDF as a source.. Azure AI Language's custom question answering feature allows you to directly upload a PDF as a knowledge source. The service automatically extracts Q&A pairs from the document, enabling the solution to answer questions based on the FAQ content without needing additional search or cognitive skill pipelines.

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Use Azure AI Search with cognitive skills.

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Use Azure OpenAI with a system prompt containing the PDFs.

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Use the pre-built question answering in Azure AI Language.

Question 14

You need to analyze customer call transcripts to identify positive and negative sentiment. Which Azure AI Language feature should you use?

Accepted Answer

Sentiment Analysis. Sentiment Analysis is the correct Azure AI Language feature because it is specifically designed to evaluate text and determine whether the sentiment expressed is positive, negative, or neutral. For customer call transcripts, this feature analyzes each sentence or document and returns a sentiment label and confidence scores, directly addressing the requirement to identify positive and negative sentiment.

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Language Detection

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Named Entity Recognition

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Key Phrase Extraction

Question 15

You are designing a solution that must extract personally identifiable information (PII) from medical records stored in Azure Blob Storage. The solution must redact the PII before storing the results. Which combination of Azure services should you use?

Accepted Answer

Use Azure AI Language's PII detection feature and a custom Azure Function to redact.. Azure AI Language's PII detection feature is specifically designed to identify and categorize PII entities in text, and combining it with a custom Azure Function allows you to programmatically redact those entities before storing the results in Blob Storage. This provides a serverless, scalable pipeline that meets the requirement of extracting and redacting PII from medical records without manual intervention.

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Use Azure AI Search with cognitive skills for PII detection.

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Use Azure OpenAI to detect and redact PII.

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Use Text Analytics for Health and then manually redact.