AI-103 Text Analysis — 10 Questions

Question 1

You are troubleshooting a custom text classification project in Language Studio where your model's macro F1-score is low. Which THREE remedial actions can help improve the macro F1-score across all categories? (Choose two.)

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

Accepted Answer

Review and correct inconsistent or ambiguous labels in the training dataset.. Improving macro F1-score requires addressing imbalances, adding more training data for poorly performing classes, and reviewing labeling consistency. (Note: Since this is a 'Which THREE' question, select three valid remediation options).

Answer

Manually hardcode all possible classification outputs in client application code.

Answer

Delete all training data across every category.

Question 2

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 3

Your development team is building a global customer support portal. Incoming tickets arrive in dozens of different languages. You need to automatically route tickets to the correct regional team based on the language of the ticket text. Which Azure AI Language feature should you call?

Accepted Answer

Language detection. Language detection evaluates text input and returns the detected language and a confidence score for each document provided, making it ideal for routing workflows.

Answer

Sentiment analysis

Answer

Named Entity Recognition (NER)

Answer

Key phrase extraction

Question 4

You are calling the Azure AI Language REST API for Named Entity Recognition (NER) and receive an HTTP 429 status code. How should your application handle this error?

Accepted Answer

Implement an exponential backoff retry policy and resend the request after a delay.. HTTP 429 indicates 'Too Many Requests' (rate limiting). The client application should implement exponential backoff and retry the request.

Answer

Switch the API endpoint to use the Free pricing tier.

Answer

Regenerate the subscription key and restart the client service.

Answer

Immediately re-submit the request with doubled payload size.

Question 5

You are processing medical intake forms using Azure AI Language. You need to identify and categorize medical terms, medications, and dosages. Which feature should you use?

Accepted Answer

Health text analytics. The health text analytics feature (part of Azure AI Language) extracts medical entities and relations specifically tailored for healthcare domains.

Answer

Key phrase extraction

Answer

Standard Named Entity Recognition (NER)

Answer

Custom text classification

Question 6

You need to extract key concepts and main ideas from long-form technical support articles using the Azure AI Language service. Which feature should you invoke?

Accepted Answer

Key phrase extraction. Key phrase extraction returns a list of strings denoting the main talking points in the input text.

Answer

Personal Personally Identifiable Information (PII) detection

Answer

Language detection

Answer

Summarization

Question 7

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 8

You are configuring text summarization using the Azure AI Language REST API. You need to generate a summary that consists of a few distinct sentences pulled directly from the source document without altering their wording. Which summarization kind should you specify?

Accepted Answer

Extractive summarization. Extractive summarization extracts sentences that collectively capture the most important context from the source document without rewriting them.

Answer

Key phrase extraction

Answer

Abstractive summarization

Answer

Custom single-label classification

Question 9

You are configuring an Azure AI Language resource in the Azure portal. You need to ensure that the text analysis solution supports custom text classification and named entity recognition. Which pricing tier should you select?

Accepted Answer

Standard (S). Custom features such as custom text classification and custom named entity recognition require the Standard (S) pricing tier. The Free (F0) tier only supports base pre-built features with monthly limits.

Answer

Premium (P1)

Answer

Basic (B)

Answer

Free (F0)

Question 10

You are provisioning an Azure AI Language resource through the Azure CLI. You need to specify the resource kind parameter. Which value should you provide for a general text analysis resource?

Accepted Answer

TextAnalytics. When provisioning via CLI or ARM templates, the kind for Azure AI Language is 'TextAnalytics'.

Answer

LanguageService

Answer

TextUnderstanding

Answer

CognitiveServices