Exactprep AI-102 Questions

To quickly improve a GPT-3.5 chatbot’s response quality with minimal development effort:

Provide grounding content (B): Ground the model with authoritative data (e.g., the travel agent’s policies, inventory, fares) using Azure OpenAI on your data or a lightweight RAG pattern. Grounding reduces hallucinations and makes answers more accurate and context-aware without retraining the model. This typically involves configuring a data connection (Azure Cognitive Search/Blob) and augmenting prompts—far less effort than training.

Add sample request/response pairs (C): Use few-shot examples directly in the system/user messages to demonstrate ideal answers, style, and formatting. Few-shot prompting is a core prompt-engineering technique that meaningfully boosts quality with minimal code changes or infrastructure.

Options A, D, and E involve model training or retraining (fine-tuning or building a custom LLM), which require data preparation, experimentation, monitoring, and higher cost/complexity—contrary to the “minimize development effort” constraint.

Microsoft References

Prompt engineering with examples (few-shot) to improve output quality:

Azure OpenAI “on your data” (grounding to improve relevance and accuracy):

Guidance comparing prompt engineering/grounding with fine-tuning and when to use each:

How-to: Fine-tuning (shows added complexity vs. prompt techniques):

To build narration for video training content using Custom Neural Voice, we must know where each step is done:

Creating a Custom Neural Voice

A Custom Neural Voice is a text-to-speech (TTS) feature within Azure AI Speech.

It allows you to create a unique, natural-sounding synthetic voice.

The correct tool to build and configure a custom voice is the Speech Studio portal.

Not the Language Understanding portal (LUIS), Azure portal directly, or Bot Framework Composer.

Generating narration

Narration requires converting written text into speech.

This is done via the Text-to-Speech service in Azure AI Speech.

Other options:

Language Understanding → For intent/utterance processing, not narration.

Speaker Recognition → For identifying/authenticating speakers, not narration.

Speech-to-text → Converts spoken words to text (the opposite direction).

Thus:

Custom Neural Voice → Speech Studio portal

Narration → Text-to-speech

Correct Answers:

Custom neural voice: The Speech Studio portal

Narration: Text-to-speech

Microsoft References

Custom Neural Voice in Speech Studio

Text-to-Speech overview

Named Entity Recognition (NER) is one of the features offered by Azure Cognitive Service for Language, a collection of machine learning and AI algorithms in the cloud for developing intelligent applications that involve written language. The NER feature can identify and categorize entities in unstructured text. For example: people, places, organizations, and quantities.

Scenario Recap:

IoT data from 100 machines × 50 sensors × 1-minute intervals for 1 year = 5,000 time series datasets .

Requirement: Detect unusual values (outliers) to predict failures.

Correct Service:

Azure Anomaly Detector (A) is designed for time-series anomaly detection.

It learns temporal patterns and detects anomalies in sensor/IoT data streams.

Other options:

Cognitive Search = full-text search

Form Recognizer = extract text/fields from documents

Custom Vision = image classification

Answer: A. Anomaly Detector