Microsoft AI-102 Exam Dumps

Data visualization's primary purpose is to make data understandable and meaningful through visuals such as charts, dashboards, and graphs.

It helps to communicate significance (option A) and show patterns, correlations, and trends over time (option B).

C . Implement machine learning to predict future values Not visualization, but rather ML/AI.

D . Enforce business logic across reports Business logic is handled in data modeling or ETL, not visualization.

Correct Answers: A and B

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 Reference

Prompt engineering with examples (few-shot) to improve output quality: https://learn.microsoft.com/azure/ai-services/openai/concepts/prompt-engineering

Azure OpenAI ''on your data'' (grounding to improve relevance and accuracy): https://learn.microsoft.com/azure/ai-services/openai/concepts/use-your-data

Guidance comparing prompt engineering/grounding with fine-tuning and when to use each: https://learn.microsoft.com/azure/ai-services/openai/concepts/fine-tuning

How-to: Fine-tuning (shows added complexity vs. prompt techniques): https://learn.microsoft.com/azure/ai-services/openai/how-to/fine-tuning

Analysis of Options:

A . Lower the confidence score threshold of App1

Would just accept more low-confidence predictions, but won't add support for a new format.

B . Lower the accuracy threshold of App1

Similar reasoning: affects prediction acceptance, not model capabilities.

C . Add the additional contract format to the existing training set and retrain the model

Correct approach. You extend the training data to include the new format, retrain, and reuse the same model.

D . Create a new training set and add the additional contract format

This means starting from scratch, which requires more effort. Not minimal.

Comprehensive Detailed Explanation

You have an Azure Cognitive Search service. Query volume has increased, and some search requests are now being throttled. This means your current tier is no longer sufficient to handle the query traffic.

Why does migrating to a higher tier work?

Each Azure Cognitive Search pricing tier (Basic, Standard S1/S2/S3, Storage Optimized, etc.) provides different resource limits for queries per second (QPS), indexing throughput, and storage.

Throttling occurs when query traffic exceeds the capacity limits of the current tier.

Moving to a higher tier increases:

The allowed query units (QUs),

Maximum queries per second (QPS),

and available compute resources.

Therefore, migrating to a higher tier reduces the likelihood of throttling and supports the increased query volume.

Why not other solutions?

Simply adding replicas can help scale out query workloads, but the question specifically asks whether moving to a higher tier meets the goal---and it does.

Using indexer scaling or adjusting query patterns might help, but they are not direct answers to the throttling caused by insufficient service tier capacity.

Correct Answe r: A. Yes

Microsoft Reference

Azure Cognitive Search service limits by tier

Scale resources in Azure Cognitive Search

Azure Cognitive Search pricing tiers