AI-103 Practice Test 28 — 15 Questions

Question 1

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?

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

Accepted Answer

Text summarization (Abstractive or Extractive). Extractive or abstractive text summarization is designed to process long documents and generate concise summaries.

Answer

Named Entity Recognition (NER)

Answer

Key phrase extraction

Answer

Opinion mining

Question 2

When configuring a Custom Vision project, which TWO project types are available? (Choose two.)

Accepted Answer

Classification. Custom Vision supports two main project types: Classification (Multiclass/Multilabel) and Object Detection.

Answer

Speech Synthesis

Answer

Form Extraction

Answer

Text Recognition (OCR)

Question 3

You are building an Azure AI Document Intelligence solution to process complex financial statements. Due to varying layouts across different financial institutions, you decide to use a custom neural model instead of a custom template model. What is a key architectural advantage of the custom neural model in this scenario?

Accepted Answer

It supports cross-page tables and varying visual layouts without requiring strict coordinate alignment.. Custom neural models use deep learning to understand document structures without requiring exact visual template matching, making them robust to layout variations.

Answer

It requires significantly fewer training documents than a template model to achieve high accuracy.

Answer

It can be trained entirely unsupervised without requiring any human-labeled training documents.

Answer

It can extract data from scanned documents without running Optical Character Recognition (OCR) first.

Question 4

You are building an application that analyzes user feedback. You need to detect instances where users share their phone numbers, email addresses, and credit card numbers so that you can automatically redact them. Which feature should you use?

Accepted Answer

PII detection. Personally Identifiable Information (PII) detection identifies and redacts sensitive information such as phone numbers, emails, and financial data.

Answer

Sentiment analysis

Answer

Language detection

Answer

Named Entity Recognition (NER)

Question 5

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 is an extension of sentiment analysis that provides more granular information about the opinions related to product or service attributes (aspect-based sentiment analysis).

Answer

PII detection

Answer

Language detection

Answer

Custom text classification

Question 6

You are deploying an open-source model (such as Llama 3) via Model Catalog in Azure AI Foundry. You want to optimize inference latency and throughput for high-concurrency requests. Which deployment type should you select?

Accepted Answer

Managed compute with serverless API deployment. Serverless APIs with pay-as-you-go billing or managed compute provisioned through Azure AI Foundry Model Catalog provide optimized inference hosting.

Answer

Azure SQL Database scalar User Defined Functions

Answer

Local developer workstation Docker container

Answer

Azure Functions consumption plan

Question 7

You are developing an agent using Azure AI Agent Service and notice that the model consistently refuses to answer queries about company-internal technical specifications despite the documentation being uploaded to the vector index. Which action should you prioritize?

Accepted Answer

Verify the 'Grounding' settings in the Azure AI Foundry portal to ensure the search index is correctly linked.. Grounding ensures the model queries the provided data before generating a response. If it refuses to answer, the grounding configuration is likely failing or missing the retrieval step.

Answer

Enable 'Content Safety' to bypass filtering.

Answer

Increase the system prompt length in the model configuration.

Answer

Switch the model to a higher parameter count version.

Question 8

Which THREE prebuilt document models are natively available out-of-the-box in Azure AI Document Intelligence? Each correct selection represents a valid prebuilt model.

Accepted Answer

Prebuilt W-2 tax form model. Azure AI Document Intelligence provides specific prebuilt models for invoices, receipts, and W-2 tax forms, among others.

Answer

Prebuilt Video Sentiment analyzer

Answer

Prebuilt 3D CAD Model Analyzer

Question 9

Your company processes business cards from international clients. Which prebuilt Azure AI Document Intelligence model should you use to extract names, company names, emails, and phone numbers from these cards?

Accepted Answer

prebuilt-businessCard. The prebuilt business card model (prebuilt-businessCard) extracts contact information from business cards.

Answer

prebuilt-invoice

Answer

prebuilt-layout

Answer

prebuilt-receipt

Question 10

You are using Azure AI Language custom text classification. You have uploaded your training dataset and trained a model. You notice that your evaluation metrics show low precision. What does low precision indicate about your model?

Accepted Answer

Many of the items predicted as a specific class actually belonged to a different class.. Precision measures the ratio of true positive predictions to total predicted positives. Low precision means the model produces many false positives.

Answer

The model failed to find many actual positive instances in the dataset.

Answer

The latency of the REST API calls is too high.

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The training dataset contained fewer than 5 documents per class.

Question 11

You are analyzing customer product reviews using the Azure AI Language REST API. You submit a batch of documents for sentiment analysis. One of the returned JSON objects includes a 'confidenceScores' property containing 'positive', 'neutral', and 'negative' values, but the overall document sentiment is listed as 'mixed'. What condition causes the service to return 'mixed'?

Accepted Answer

The confidence scores for both positive and negative sentiments exceed established thresholds within the same document.. The service returns a 'mixed' sentiment when the confidence scores for both positive and negative emotions are significant within the same document, preventing a clear single classification.

Answer

The document contains an equal number of uppercase and lowercase words.

Answer

The document length exceeds the 5,120 character limit.

Answer

The language detection confidence score is below 0.5.

Question 12

You are configuring an Azure AI Document Intelligence orchestration model to automatically classify and extract data from incoming customer correspondence, which includes invoices, work orders, and purchase orders. One of the component models is failing to classify documents correctly. How should you resolve this issue?

Accepted Answer

Retrain the failing component custom model with more distinct sample documents that represent the specific document class.. Orchestration models rely on component custom models. If classification fails, you must retrain or refine the specific component model with distinct training samples.

Answer

Modify the orchestration model routing threshold in the Azure portal settings.

Answer

Switch the orchestration model from a template-based classifier to a neural classifier using the REST API.

Answer

Update the API version to bypass classification and force direct extraction via the general document model.

Question 13

You are troubleshooting a low confidence score on specific fields extracted by your Azure AI Document Intelligence custom template model. Which TWO actions can help improve the extraction confidence for those fields? (Choose TWO)

Accepted Answer

Ensure all training documents are of high visual quality and free from blur or heavy skew.. Improving training quality through diverse, high-quality samples and accurate labeling improves confidence scores.

Answer

Increase the confidence threshold parameter in the analyzeDocument request body to 0.99.

Answer

Convert all training PDFs to grayscale and reduce their resolution to 72 DPI to speed up processing.

Answer

Switch the model type from custom template to a prebuilt business card model.

Question 14

When composing multiple individual Custom Neural models into a single composed model in Azure AI Document Intelligence, which THREE behaviors or characteristics apply? Each correct selection represents a valid aspect.

Accepted Answer

All sub-models included in a composed model must share the same major API version.. Composed models route documents automatically to the correct underlying model, share a single endpoint, and require all component models to be built using the same major API version.

Answer

Composed models require a separate Azure resource endpoint for each individual sub-model.

Answer

Composed models support an unlimited number of sub-models without any performance impact.

Question 15

Which prebuilt Azure AI Document Intelligence model should you use to extract line items, merchant name, transaction date, and total tax from point-of-sale purchase receipts?

Accepted Answer

prebuilt-receipt. The prebuilt receipt model (prebuilt-receipt) is specifically trained to extract data from sales receipts.

Answer

prebuilt-invoice

Answer

prebuilt-idDocument

Answer

prebuilt-businessCard