AI-102 Implement natural language processing solutions Practice Test 8 — 15 Questions

Question 1

You are deploying a conversational language understanding (CLU) project in Azure AI Language for an internal help desk bot. The project predicts intents and extracts entities from employee messages. You must publish the project and then call it from a .NET application. Which two actions must you perform? (Choose two.)

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

Accepted Answer

Specify the deployment name in the analyze-conversations request so the runtime selects the correct trained model version.. A conversational language understanding project becomes callable only after training and publishing a named deployment. The runtime request to analyze-conversations must then identify both the project and the deployment so the service routes to the correct model version. Training the project and supplying the deployment name are therefore the two required actions, while the remaining options belong to unrelated features.

Answer

Create a custom question answering knowledge base and link it to the CLU project before publishing.

Answer

Import the CLU project into an Azure OpenAI deployment so the bot can use GPT-4 for intent prediction.

Answer

Set the project's primary language to the language of the employee messages before training.

Question 2

A development team is using Azure Cognitive Service for Language to extract key phrases from customer reviews. They notice that some reviews are not being processed, and the API returns a 400 error code. What is the most likely cause?

Accepted Answer

One of the reviews exceeds the maximum character limit for a single document.. The Azure Cognitive Service for Language key phrase extraction API enforces a maximum document size of 5,120 characters per document. When a single review exceeds this limit, the API returns a 400 Bad Request error because the request payload violates the service's input constraints. This is the most common cause of 400 errors in batch text analysis operations.

Answer

The reviews contain characters that are not valid UTF-8.

Answer

The request contains more than 5 documents.

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The reviews are written in a language not supported by the service.

Question 3

A company is using Azure Cognitive Service for Language to analyze customer support transcripts. They want to identify custom categories (e.g., 'billing', 'technical support') using a custom text classification model. After training and deploying the model, they receive many false positives for the 'billing' category. What is the best first step to improve model accuracy?

Accepted Answer

Review the training data for the 'billing' category and correct any mislabeled examples.. False positives for a specific category like 'billing' most often stem from mislabeled or ambiguous training examples in that category. By reviewing and correcting the training data for 'billing', you directly address the root cause of the model's confusion, which is the most effective first step in custom text classification model improvement.

Answer

Add more training data to all categories to improve overall model performance.

Answer

Use a different Azure AI service, such as key phrase extraction, to identify billing-related content.

Answer

Increase the confidence threshold for the 'billing' category to reduce false positives.

Question 4

A company wants to use Azure AI Translator to translate customer emails from English to French. They need to ensure that the translation preserves the tone and formality of the original text. What should they configure in the request?

Accepted Answer

Set the 'formality' parameter to the desired level (e.g., 'formal' or 'informal').. Azure AI Translator provides a 'formality' parameter that allows you to specify the desired level of formality (e.g., 'formal' or 'informal') in the translated text. This parameter directly controls the tone and register of the output, ensuring that the translation preserves the original email's tone and formality, which is critical for customer communications.

Answer

Set the 'category' parameter to 'general' to use a standard translation model.

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Set the 'scope' parameter to 'document' to ensure context-aware translation.

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Set the 'language' parameter to 'fr' and the 'from' parameter to 'en'.

Question 5

Which TWO actions should you take to optimize a custom text classification model in Azure Cognitive Service for Language?

Accepted Answer

Ensure that training examples for different labels do not have overlapping content.. Overlapping content between labels (e.g., the same text appearing in both 'positive' and 'negative' training examples) confuses the custom text classification model, leading to poor decision boundaries. Azure Cognitive Service for Language uses a multi-class or multi-label classifier that learns distinct patterns for each label; overlapping content introduces ambiguity, reducing precision and recall. Ensuring distinct, non-overlapping training examples per label helps the model learn clear, separable features.

Answer

Oversample the minority classes to balance the dataset.

Answer

Remove all stop words from the training data.

Answer

Remove examples with neutral sentiment to focus on positive and negative classes.

Question 6

A company is building a chatbot using Azure Bot Service and Language Understanding (LUIS). The chatbot needs to handle user intents for booking flights and checking flight status. After testing, the chatbot frequently fails to distinguish between the two intents when users mention flight numbers. Which action should the engineer take to improve intent recognition?

Accepted Answer

Add more utterances that include flight numbers to the training data for both intents.. Adding more utterances that include flight numbers to both intents provides LUIS with more varied examples of how flight numbers appear in natural language, enabling the model to learn distinguishing patterns. Without sufficient training data containing flight numbers, LUIS cannot reliably differentiate between 'BookFlight' and 'CheckFlightStatus' when users mention flight numbers, as the entity alone does not determine intent.

Answer

Increase the number of intents to split the flight-related queries further.

Answer

Reduce the confidence score threshold for intent detection.

Answer

Use the prebuilt domain for flight booking to improve accuracy.

Question 7

A developer is using Azure Cognitive Service for Language to perform sentiment analysis on customer reviews. The service returns sentiment labels (positive, negative, neutral) and confidence scores. For a particular review, the service returns 'positive' with a confidence score of 0.55. The developer wants to ensure that only high-confidence results are used. What should the developer do?

Accepted Answer

Configure a minimum confidence threshold of 0.75 in the application logic.. The developer must implement a confidence threshold in the application logic to filter out low-confidence results. The Azure Cognitive Service for Language returns confidence scores between 0 and 1 for each sentiment label, and the developer can set a minimum threshold (e.g., 0.75) to ensure only high-confidence predictions are used. This approach does not require retraining the model or modifying the input text, as the threshold is applied post-inference in the client code.

Answer

Use the Text Analytics for Health API instead.

Answer

Retrain the sentiment analysis model with additional labeled data.

Answer

Adjust the input text by removing ambiguous phrases.

Question 8

Which THREE actions should an engineer take when deploying a custom question answering project in Azure Cognitive Service for Language?

Accepted Answer

Set up a multi-turn extraction policy for follow-up questions.. Multi-turn extraction is a core feature of custom question answering that allows the system to handle follow-up questions by maintaining context across turns. This is essential for conversational flows where a user's subsequent query depends on the previous answer, and it is configured via the project settings in Language Studio.

Answer

Integrate LUIS for intent detection.

Answer

Configure a single-turn extraction policy.

Question 9

A healthcare organization is building a clinical decision support system that must extract medical entities (e.g., symptoms, diagnoses, medications) from unstructured clinical notes. The solution must be able to detect relationships between entities, such as 'medication X treats symptom Y'. Which Azure AI service should be used?

Accepted Answer

Azure AI Language - Custom NER with entity linking. Azure AI Language's Custom Named Entity Recognition (NER) with entity linking allows training a model to extract domain-specific medical entities such as symptoms, diagnoses, and medications from unstructured clinical notes. Entity linking connects these entities to a knowledge base (e.g., UMLS), which contains structured relationships between medical concepts, enabling the inference of relationships like 'medication X treats symptom Y'. Thus, Custom NER with entity linking is the appropriate service for both entity extraction and relationship detection.

Answer

Azure AI Translator

Answer

Azure AI Speech-to-Text

Answer

Azure AI Document Intelligence (formerly Form Recognizer)

Question 10

You are developing a chatbot for a retail company using Azure AI Language's custom question answering. The chatbot must provide answers from a knowledge base of 500 FAQ documents. Users often ask the same question in different wording, and the chatbot fails to return an answer for paraphrased queries. What is the most effective solution?

Accepted Answer

Enable active learning in the project settings and periodically publish the updated knowledge base.. Enabling active learning in Azure AI Language's custom question answering allows the system to learn from user interactions. It suggests alternative phrasings for existing QnA pairs, which helps answer paraphrased queries without manual effort. Option A is wrong because Direct Line Speech channel is for voice interactions, not improving answer coverage. Option C is wrong because simply adding more documents does not address the paraphrasing issue; it may increase redundancy but not handle different wording of the same question. Option D is wrong because manually adding alternate phrases is time-consuming and not scalable; active learning automates this process by identifying and suggesting variations based on user queries.

Answer

Use Azure AI Bot Service's Direct Line Speech channel to improve accuracy.

Answer

Increase the number of FAQ documents in the knowledge base.

Answer

Manually add alternate question phrases to the knowledge base for each QnA pair.

Question 11

A company wants to analyze customer reviews to determine whether sentiment is positive, negative, or neutral. The solution must also extract key phrases such as 'great battery life' and 'poor camera quality'. Which Azure AI feature should be used?

Accepted Answer

Azure AI Language - Sentiment Analysis and Key Phrase Extraction. Azure AI Language's Sentiment Analysis and Key Phrase Extraction are specifically designed to evaluate text for positive, negative, or neutral sentiment and to extract meaningful phrases like 'great battery life' or 'poor camera quality'. This combined capability directly matches the dual requirement of sentiment classification and key phrase extraction in a single API call, using pre-built models that require no custom training.

Answer

Azure AI Language - Named Entity Recognition (NER)

Answer

Azure AI Content Safety

Answer

Azure AI Language Understanding (LUIS)

Question 12

A financial services company uses Azure AI Language's custom text classification to categorize loan applications as 'Approved', 'Denied', or 'Review Required'. The model is trained on historical data but is producing poor accuracy on new applications. The data scientist suspects data leakage between training and test sets. What should the data scientist do to validate this?

Accepted Answer

Split the data chronologically and ensure no overlapping data between train and test sets.. Data leakage occurs when information from outside the training set inadvertently influences the model, often due to overlapping or non-independent data splits. By splitting the data chronologically (e.g., training on older applications and testing on newer ones), the data scientist ensures that no future information leaks into the training process, which directly validates whether temporal leakage is causing poor accuracy. This approach is standard for time-series or sequential data like loan applications, where patterns may shift over time.

Answer

Increase the training dataset size and retrain the model.

Answer

Use k-fold cross-validation during training.

Answer

Adjust the classification confidence threshold.

Question 13

A news organization wants to automatically summarize long articles into short, coherent summaries. The solution must preserve the original meaning and key points. Which Azure AI service should be used?

Accepted Answer

Azure AI Language - Extractive Summarization. Azure AI Language's Extractive Summarization is specifically designed to generate concise summaries by extracting the most important sentences from a document while preserving the original meaning and key points. This service uses natural language processing to rank sentences based on relevance and coherence, making it ideal for summarizing long articles without altering the original content.

Answer

Azure AI Document Intelligence

Answer

Azure AI Language - Key Phrase Extraction

Answer

Azure AI Translator

Question 14

A legal firm uses Azure AI Language's custom NER to extract party names, dates, and clauses from contracts. The model performs well on English contracts but poorly on French contracts. The firm wants to improve performance without retraining from scratch. What is the most efficient approach?

Accepted Answer

Use the multilingual option in Azure AI Language custom NER to extend the existing project to include French.. Azure AI Language's custom NER supports a multilingual option that allows you to extend an existing project to include additional languages without retraining from scratch. By enabling this option and adding French labeled data, the model learns to recognize entities in French while retaining its English performance, making it the most efficient approach.

Answer

Create a separate custom NER project for French and train from scratch using French contracts.

Answer

Retrain the English model with a mix of English and French contracts.

Answer

Use Azure AI Translator to translate French contracts to English, then use the English model.

Question 15

A company wants to build a solution that can identify and redact personally identifiable information (PII) from customer support transcripts. The solution must handle multiple languages. Which Azure AI service should be used?

Accepted Answer

Azure AI Language - PII Detection. Azure AI Language's PII Detection feature is specifically designed to identify and redact personally identifiable information in text across multiple languages. It supports over 30 languages and can detect entities such as names, addresses, phone numbers, and credit card numbers, making it the correct choice for this multilingual PII redaction requirement.

Answer

Azure AI Content Safety

Answer

Azure AI Document Intelligence

Answer

Azure AI Translator