AI-103 Text Analysis — Timed 40 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.)

40 minute time limit — choose an answer to begin.

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

Question 11

You are building an app to analyze customer feedback. You want to extract named entities such as people, locations, and organizations. Which Azure AI Language feature should you use?

Accepted Answer

Named Entity Recognition (NER). Named Entity Recognition (NER) extracts known entities from text and categorizes them into predefined classes such as people, places, and organizations.

Answer

Language detection

Answer

Sentiment analysis

Answer

Key phrase extraction

Question 12

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.

Answer

The training dataset contained fewer than 5 documents per class.

Question 13

You are implementing opinion mining as part of sentiment analysis in Azure AI Language. An incoming review states, 'The room was spacious, but the staff was rude.' What does opinion mining return for this sentence?

Accepted Answer

Aspect targets ('room', 'staff') along with their corresponding evaluations ('spacious', 'rude') and individual sentiments.. Opinion mining targets specific aspects (e.g., 'room', 'staff') and associates a separate sentiment and assessment (e.g., 'spacious' is positive, 'rude' is negative) for each aspect.

Answer

A binary classification indicating whether the review is spam or genuine.

Answer

A single neutral sentiment score for the entire document.

Answer

A translated version of the review in English.

Question 14

You are configuring abstractive summarization in Azure AI Language. You want to control the length of the generated summary output. Which parameter should you configure in your request body?

Accepted Answer

maxSummaryLength. Abstractive summarization requests accept a 'max和社会' or max-length parameter depending on the API version to control summary length, specifically 'maxSummaryLength'.

Answer

documentLengthLimit

Answer

sentenceCount

Answer

confidenceThreshold

Question 15

You are developing an application that processes multilingual documents using Azure AI Language. You want to ensure that your API payload complies with the maximum document size limit per single request document in synchronous calls. What is the maximum character length allowed for a single document input?

Accepted Answer

5,120 characters. For the Azure AI Language REST API, the maximum character length for a single document input is 5,120 characters (as measured by StringInfo.LengthInTextElements).

Answer

1,000,000 characters

Answer

50,000 characters

Answer

1,024 characters

Question 16

You are migrating an existing solution from the legacy Text Analytics API v3.1 to the current Azure AI Language REST API. You notice changes in the JSON request body structure. Where should the document text array now be nested in the payload for a sentiment analysis request?

Accepted Answer

Nested inside an 'analysisInput' object under the root.. In the Azure AI Language API (such as version 2022-05-01 and later), documents are wrapped inside an analysis input object, specifically under 'analysisInput': { 'documents': [...] }.

Answer

Inside the HTTP header as a base64 encoded string.

Answer

Directly under the root object as a 'documents' array.

Answer

Nested inside a 'parameters' object.

Question 17

You need to evaluate customer reviews to determine whether the overall tone is positive, negative, or neutral. Which Azure AI Language feature should you use?

Accepted Answer

Sentiment analysis. Sentiment analysis provides sentiment labels (positive, negative, neutral) and confidence scores at both the document and sentence levels.

Answer

Language detection

Answer

Named Entity Recognition

Answer

Key phrase extraction

Question 18

You are using Language Studio to build a custom named entity recognition (custom NER) project. You have tagged your training data and successfully trained your first model. You want to test the model interactively without writing code. Which tool in Language Studio should you use?

Accepted Answer

Test model pane. Language Studio provides a built-in 'Test' interface where you can input custom text and inspect the model's entity extractions interactively.

Answer

Deployment settings

Answer

Data labeling page

Answer

Model performance tab

Question 19

You are creating a Language resource in the Azure portal. Which blade should you navigate to if you need to retrieve your endpoint URL and subscription keys for your application configuration?

Accepted Answer

Keys and Endpoint. The 'Keys and Endpoint' blade in the Azure portal displays the API keys and endpoint URL required to connect your application to the Azure AI Language service.

Answer

Diagnostic settings

Answer

Pricing tier

Answer

Access control (IAM)

Question 20

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?

Accepted Answer

A single document can be assigned zero, one, or multiple categories simultaneously.. In multi-label classification, a single document can be assigned zero, one, or multiple distinct category labels simultaneously.

Answer

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

Answer

The output is restricted to binary positive or negative values.

Answer

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