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NEW QUESTION 1
You manage an Azure Machine Learning workspace. The Python script named script.py reads an argument named training_data. The training_data argument specifies the path to the training data in a file named dataset1.csv. You plan to run the script.py Python script as a command job that trains a machine learning model. You need to provide the command to pass the path for the dataset as a parameter value when you submit the script as a training job.
Solution: python train.py –training_data training_data
Does the solution meet the goal?

A.    Yes
B.    No

Answer: B
Explanation:
https://learn.microsoft.com/en-us/azure/machine-learning/how-to-read-write-data-v2

NEW QUESTION 2
You work in Microsoft Foundry with a prompt flow. You must manually evaluate prompts and compare results across prompt variants. You need to capture the inputs, outputs, token usage, and latencies for each flow run for the evaluation.
Solution: Use the prompt flow SDK to enable tracing for the flow before executing runs. Then run the flow to generate traceable results.
Does the solution meet the goal?

A.    Yes
B.    No

Answer: B
Explanation:
https://www.linkedin.com/pulse/streamlining-generative-ai-development-azure-foundry-tracing-taneja-mbwze

NEW QUESTION 3
An organization is deploying several generative AI workloads by using Microsoft Foundry. Each workload must meet different requirements related to data governance, task specialization, and operational cost control. The organization requires models that meet the following requirements:
– Model behavior aligns with the task being performed.
– Data handling aligns with internal governance policies.
– Operational complexity and cost are justified by workload needs.
You need to select the foundation model options that meet the requirements. Which three models can you select? (Each correct answer presents a complete solution. Choose three.)

A.    A model that is optimized for conversational reasoning when deploying an interactive assistant.
B.    The largest available model to simplify operational management.
C.    The smallest available model to minimize the usage cost.
D.    A model that supports multiple input types when workloads require combined text and image analysis.
E.    A model that offers enterprise governance controls when workloads process regulated business data.

Answer: BCE
Explanation:
https://azure.microsoft.com/en-us/products/ai-foundry/models
https://learn.microsoft.com/en-us/azure/foundry/responsible-ai/openai/data-privacy

NEW QUESTION 4
A Retrieval-Augmented Generation (RAG) solution returns incomplete answers because relevant content is inconsistently retrieved from the knowledge source. You need to improve RAG accuracy without changing the embedding model currently in use. You need to achieve this goal while minimizing operational costs. Which two actions should you perform? (Each correct answer presents part of the solution. Choose two.)

A.    Tune chunk size and overlap to match content structure.
B.    Implement an optimized re-ranker.
C.    Increase token limits for all requests.
D.    Optimize the length of embedding vectors.

Answer: AB
Explanation:
To improve Retrieval-Augmented Generation (RAG) accuracy, address inconsistent retrieval, and eliminate incomplete answers without changing the embedding model or increasing costs significantly, you must move beyond naive fixed-length chunking and implement a two-stage retrieval process. Here is the targeted, low-cost strategy:
1. Tune Chunk Size and Overlap to Match Content Structure. Inconsistent retrieval often occurs because important information is split across chunk boundaries (breaking context) or chunks are too large, diluting the semantic meaning.
2. Implement an Optimized Re-ranker. The initial vector search often returns “noise” – chunks that are semantically close but not actually relevant. A re-ranker acts as a second, smarter, but more “expensive” step that works on a smaller subset of data, making it low-cost overall.
https://medium.com/@sthanikamsanthosh1994/how-to-improve-rag-retrieval-augmented-generation-performance-2a42303117f8

NEW QUESTION 5
You manage an Azure Machine learning workspace. You develop a machine learning model. You must deploy the model to use a low-priority VM with a pricing discount. You need to deploy the model. Which compute target should you use?

A.    Azure Container Instances (ACI).
B.    Azure Machine Learning compute clusters.
C.    Local deployment.
D.    Azure Kubernetes Service (AKS).

Answer: B
Explanation:
The best compute target for deploying a model using low-priority VMs (or their modern successor, Spot VMs) is an Azure Machine Learning compute cluster.
https://learn.microsoft.com/en-us/azure/machine-learning/how-to-use-low-priority-batch

NEW QUESTION 6
A team manages an Azure Machine Learning workspace where they deploy models to online endpoints. The team needs to introduce a new version of a model to production without disrupting existing users. The team must validate the new version before full rollout. You need to reduce risk during deployment. What should you do?

A.    Deploy the model to a batch endpoint.
B.    Split traffic between deployments.
C.    Replace the existing endpoint.
D.    Route all traffic to the new deployment.

Answer: B
Explanation:
To introduce a new model version in Azure Machine Learning without service interruption, you should use Blue/Green Deployment with Traffic Splitting. This strategy allows you to run two versions of a model simultaneously under a single Online Endpoint, gradually shifting users to the new version once it is validated.
https://learn.microsoft.com/en-us/azure/well-architected/ai/operations

NEW QUESTION 7
You have a deployment of an Azure OpenAI Service base model. You plan to fine-tune the model. You need to prepare a file that contains training data. Which file format should you use?

A.    CSV
B.    TSV
C.    JSONL
D.    JSON

Answer: C
Explanation:
To fine-tune a model in the Azure OpenAI Service, your training data must be in JSONL (JSON Lines) format.
https://dev.to/icebeam7/fine-tuning-a-model-with-azure-open-ai-studio-39p7

NEW QUESTION 8 27
You have a deployment of an Azure OpenAI Service base model. You plan to fine-tune the model. You need to prepare a file that contains training data for multi-turn chat. Which file encoding method should you use?

A.    ISO-8859-1
B.    UTF-16
C.    UTF-8
D.    ASCII

Answer: C
Explanation:
For preparing a multi-turn training data file for the Azure OpenAI Service, you should use UTF-8 with a Byte Order Mark (BOM) encoding.
https://dev.to/icebeam7/fine-tuning-a-model-with-azure-open-ai-studio-39p7

NEW QUESTION 9
You are fine-tuning a base language model to analyze customer feedback. You label examples of support tickets. You must improve classification accuracy by configuring and fine-tuning the base model in Microsoft Foundry. You need to configure and run fine-tuning. What should you do first?

A.    Use prompt flow to generate multiple prompt templates for evaluation.
B.    Deploy the base model to an online endpoint before starting fine-tuning.
C.    Enable tracing for all inference calls in the evaluation pipeline.
D.    Format the dataset as a JSONL file with prompt-completion pairs and upload the file.

Answer: C
Explanation:
In Microsoft Foundry, when configuring and running a fine-tuning job for analyzing customer feedback (e.g., classifying support tickets), you should first enable tracing for all inference calls in the evaluation pipeline. Tracing is a critical step in the “Evaluate” phase of the fine-tuning workflow, allowing you to capture input/output examples, identify the root cause of classification errors, monitor latency, and analyze model behavior before and after training.
https://devblogs.microsoft.com/foundry/a-developers-guide-to-fine-tuning-gpt-4o-for-image-classification-on-azure-ai-foundry