AI-103 Text Analysis Practice Test 3 — 15 Questions

Question 1

You are developing a solution that uses custom text classification in Azure AI Language. You choose the multi-label classification architecture. What is the key characteristic of multi-label text classification?

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

Accepted Answer

A single document can be assigned zero, one, or multiple categories simultaneously.. Multi-label text classification in Azure AI Language allows a single document to be assigned zero, one, or multiple categories simultaneously. This is in contrast to multi-class classification, where each document is assigned exactly one mutually exclusive category. The multi-label architecture is ideal when categories are not mutually exclusive and a document can belong to several at once.

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Each document must be assigned to exactly one mutually exclusive category.

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The output is restricted to binary positive or negative values.

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Categories are automatically generated by the service without user training data.

Question 2

You are building an automated support ticketing system. You need to extract names, email addresses, and phone numbers from user messages while leaving non-sensitive text untouched. Which feature of Azure AI Language should you use?

Accepted Answer

Personally Identifiable Information (PII) detection. PII detection in Azure AI Language is purpose-built to identify and categorize sensitive personal data such as names, email addresses, phone numbers, SSNs, and credit card numbers, and it returns entity categories plus redaction offsets so you can mask or remove them. Because the requirement is specifically to isolate sensitive identifiers while leaving other text untouched, PII detection is the exact fit. It is essentially NER with a curated, privacy-focused entity taxonomy and built-in masking support.

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Key phrase extraction

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Named Entity Recognition (NER)

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Custom text classification

Question 3

You are designing a solution that processes user reviews. You want to extract key terms such as 'battery life', 'screen quality', and 'customer service' from the text. Which Azure AI Language feature is best suited for this?

Accepted Answer

Key phrase extraction. Key phrase extraction is designed to identify the main talking points or salient terms in text, which is exactly what is needed to surface terms like 'battery life' and 'screen quality' from reviews. It returns a ranked list of key phrases without requiring training. This makes it the best fit for extracting review topics.

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Sentiment analysis

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PII detection

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Language detection

Question 4

You are training a custom text classification model in Language Studio. After reviewing your trained model's performance metrics, you decide to improve recall for a specific underperforming category. What action should you take?

Accepted Answer

Add more varied training examples for that category to the dataset and retrain the model.. To improve recall for an underperforming category in custom text classification, you should add more varied training examples for that category and retrain the model. Recall measures the model's ability to find all relevant instances, so more diverse examples help the model generalize better. This is a standard approach in machine learning.

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Increase the confidence threshold parameter in your client application code.

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Delete all training documents for that category.

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Switch the classification type from multi-label to single-label.

Question 5

You are analyzing product reviews using Azure AI Language. You receive sentiment analysis output where a sentence has a positive confidence score of 0.85, a neutral score of 0.10, and a negative score of 0.05. What is the sentiment label assigned to this sentence?

Accepted Answer

positive. Azure AI Language sentiment analysis assigns the label corresponding to the highest confidence score among positive, neutral, and negative. Here positive is 0.85, which is the maximum, so the sentence is labeled positive. The scores are a probability distribution that sums to approximately 1.0.

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neutral

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mixed

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negative

Question 6

You are configuring an Azure AI Language resource and want to ensure that it can be accessed securely from an Azure Virtual Network (VNet) without traversing the public internet. Which Azure networking feature should you configure?

Accepted Answer

Private endpoints. Private endpoints create a private IP address for an Azure AI Language resource within your VNet, enabling secure access over the Microsoft backbone network without exposing traffic to the public internet. This is the standard way to privately connect to Azure PaaS services. It uses Azure Private Link to map the service to a private endpoint in your subnet.

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Azure Front Door

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Azure Bastion

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VPN Gateway

Question 7

You are troubleshooting an Azure AI Language asynchronous summarization job. You submitted a batch of documents using the `/analyze` endpoint with long-running operations (LRO). How should your application check the status and retrieve the results of the job?

Accepted Answer

Poll the URL provided in the 'operation-location' response header using a GET request until the status is 'succeeded'.. Azure AI Language asynchronous (LRO) jobs return an 'operation-location' header in the initial 202 Accepted response. The application must poll that URL with GET requests until the status field reads 'succeeded', then retrieve results from the same endpoint. This is the standard Azure long-running operation pattern.

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Listen for incoming webhook notifications on an Azure Event Grid subscription attached to the text analytics resource.

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Query the Azure Monitor metrics API for job completion status.

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Repeatedly send synchronous POST requests to the same document endpoint until a 200 OK is returned.

Question 8

You are reviewing the pricing details for Azure AI Language. How is billing calculated for pre-built text analysis features such as sentiment analysis and key phrase extraction?

Accepted Answer

Number of text records (documents) processed. Azure AI Language pre-built features such as sentiment analysis and key phrase extraction are consumption-based services billed per text record (document) processed. Each API call that submits a document for analysis counts as one billable transaction, regardless of document length within the per-document size limit. This pay-per-use model aligns cost directly with the volume of text analyzed rather than with infrastructure uptime or training activity.

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Hourly uptime of the resource endpoint

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Number of unique client application connections

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Total gigabytes of training data uploaded

Question 9

You are building a custom named entity recognition model in Language Studio. You want to extract product serial numbers that follow a specific pattern (two uppercase letters followed by four digits, a hyphen, and three digits). Which entity type definition method should you use?

Accepted Answer

Regex entity type. A regex entity type is designed to extract entities that match a specific regular expression pattern, such as two uppercase letters followed by four digits, a hyphen, and three digits (e.g., '[A-Z]{2}\d{4}-\d{3}'). In Language Studio, you define the regex pattern and optionally provide examples, making it ideal for structured identifiers like serial numbers.

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List entity type

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Learned entity type

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Prebuilt entity type

Question 10

You are processing customer feedback emails that contain both English and Spanish paragraphs within the same message. You need to perform sentiment analysis on each paragraph separately. What should you do?

Accepted Answer

Split the email into individual paragraphs and submit them as separate documents in the request payload.. Azure AI Language sentiment analysis processes each document independently and assumes a single language per document. When an email contains mixed languages, submitting the entire email as one document with auto-detect will produce inaccurate results because the service detects one dominant language and applies that language's model to all text. Splitting the email into individual paragraphs and submitting them as separate documents in the request payload allows the service to detect and analyze the language of each paragraph independently, yielding accurate per-paragraph sentiment.

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Use custom text classification instead of sentiment analysis.

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Submit the entire email as a single document with the language set to auto-detect.

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Translate the entire email to French before calling sentiment analysis.

Question 11

You are configuring a webhook to receive notifications when an asynchronous batch text summarization job completes in Azure AI Language. Which Azure service should you use to capture and route the webhook events to your backend function?

Accepted Answer

Azure Event Grid. Azure Event Grid is the event-routing service designed to deliver event notifications from Azure services to subscribers such as webhooks or Azure Functions. Azure AI Language batch jobs publish completion events that Event Grid captures and routes to the configured endpoint. This makes Event Grid the correct service for receiving and forwarding the webhook when the summarization job finishes.

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Azure API Management

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Azure Stream Analytics

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Azure Logic Apps

Question 12

You are writing a Python script using the Azure AI Language SDK (`azure-ai-textanalytics`). Which client class should you instantiate to connect to your language resource?

Accepted Answer

TextAnalyticsClient. The `azure-ai-textanalytics` SDK (now part of the `azure-ai-language` family) exposes `TextAnalyticsClient` as the primary client class for interacting with Azure AI Language services such as sentiment analysis, key phrase extraction, and language detection. You instantiate it with your endpoint and an `AzureKeyCredential` or `DefaultAzureCredential`. The class name directly matches the package name, making it the correct entry point for this SDK version.

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LanguageServiceClient

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TextAnalysisClient

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CognitiveServicesClient

Question 13

You are implementing a text classification pipeline using Azure AI Language. You want to deploy your trained custom model to a production endpoint. What is required before you can assign traffic to the model deployment in Language Studio?

Accepted Answer

You must successfully complete model training and create a deployment in Language Studio.. In Azure AI Language's custom text classification, the workflow requires two distinct steps before traffic can be routed: first, you must train the model (which produces a trained model artifact tied to your project), and second, you must create a deployment that hosts that trained model at an endpoint. Only after a deployment exists can you assign traffic to it in Language Studio. The [CORRECT] option captures both prerequisites — successful training and deployment creation — which are the actual gating requirements.

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You must export the model container and deploy it to an Azure Kubernetes Service (AKS) cluster.

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You must submit a support ticket to Microsoft to enable custom model endpoints.

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You must convert the model files to ONNX format.

Question 14

You are analyzing survey responses. You want to categorize each response into one of three predefined business categories ('Billing', 'Technical Support', 'General Inquiry'). Which Azure AI Language feature should you use?

Accepted Answer

Custom text classification. Custom text classification is designed to categorize text into predefined custom categories that you define and train with your own data. Since you have three specific business categories, you should use custom text classification to train a model that assigns each response to one of these categories.

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Key phrase extraction

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Extractive summarization

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Prebuilt Named Entity Recognition

Question 15

You are setting up Azure AI Language. You want to authenticate your client application using Microsoft Entra ID (formerly Azure Active Directory) instead of using API subscription keys. Which role must be assigned to your application's service principal to allow it to call text analysis APIs?

Accepted Answer

Cognitive Services User. To authenticate a client application using Microsoft Entra ID for Azure AI Language, the service principal must be assigned the Cognitive Services User role. This role grants the necessary permissions to call the text analysis APIs, including sentiment analysis, entity recognition, and language detection, without requiring subscription keys. The role is scoped to the Azure AI Language resource and follows the principle of least privilege for API access.

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Reader

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Storage Blob Data Contributor

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Contributor