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Microsoft AI-300 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Design and implement a GenAIOps infrastructure | 20–25% | - Implement infrastructure for generative AI workloads
|
| Topic 2: Design and implement an MLOps infrastructure | 15–20% | - Implement infrastructure as code for Machine Learning
|
| Topic 3: Implement machine learning model lifecycle and operations | 25–30% | - Orchestrate model training and experimentation
|
| Topic 4: Implement generative AI quality assurance and observability | 10–15% | - Monitor generative AI systems
|
| Topic 5: Optimize generative AI systems and model performance | 15–20% | - Improve efficiency and cost-effectiveness
|
Microsoft Operationalizing Machine Learning and Generative AI Solutions Sample Questions:
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 Machine Learning compute clusters
- B. Azure Container Instances (ACI)
- C. Local deployment
- D. Azure Kubernetes Service (AKS)
Correct Answer: A 🗳️
Explanation: Only visible for Exam4Docs members. You can sign-up / login (it's free).
You run Azure Machine Learning training experiments. The training scripts directory contains 100 files that includes a file named. amlignore. The directory also contains subdirectories named. /outputs and./logs.
There are 20 files in the training scripts directory that must be excluded from the snapshot to the compute targets. You create a file named. gift ignore in the root of the directory. You add the names of the 20 files to the. gift ignore file. These 20 files continue to be copied to the compute targets.
You need to exclude the 20 files. What should you do?
- A. Move the file named. gift ignore to the. /outputs directory.
- B. Copy the contents of the file named. gift ignore to the file named. amlignore.
- C. Move the file named. gift ignore to the. /logs directory.
- D. Add the contents of the file named. amlignore to the file named. gift ignore.
Correct Answer: B 🗳️
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. Route all traffic to the new deployment.
- C. Split traffic between deployments.
- D. Replace the existing endpoint.
Correct Answer: C 🗳️
Explanation: Only visible for Exam4Docs members. You can sign-up / login (it's free).
You are reviewing a dataset that will be used for an advanced fine-tuning job in Microsoft Foundry.
The fine-tuning job uses preference comparison data.
You review the following dataset excerpt.
For each of the following statements, select Yes if the statement is true. Otherwise, select No. NOTE: Each correct selection is worth one point.
Correct Answer:
Explanation:
Preference comparison data with chosen versus rejected response pairs is the input format for Direct Preference Optimization (DPO) or RLHF-style fine-tuning - an advanced fine-tuning technique available in Microsoft Foundry. A valid DPO dataset record must have three fields: a prompt as the input, a chosen field containing the preferred response, and a rejected field containing the less preferred response. The file must be in JSONL format with UTF-8 encoding, where each line represents one complete preference pair. When evaluating statements about this dataset, mark True if the dataset contains all three required fields and chosen responses represent higher-quality outputs than rejected ones. Mark False if the format is incompatible with DPO requirements, if the required rejected field is missing, or if the chosen and rejected responses appear to be of equivalent quality with no clear preference signal.
Microsoft Learn Reference Topic: Advanced fine-tuning with preference data in Microsoft Foundry - DPO dataset format
-
You have a Microsoft Foundry project with a connected Azure OpenAI Service model.
You have a set of text files stored locally on your computer.
You must set up a flow that will generate responses based on the content of your local files.
You need to implement a solution.
Which four actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.
Correct Answer:
Explanation:
Correct sequence:
* Create a data asset.
* Create a Foundry Search resource.
* Create a vector index.
* Create a flow.
First, create a data asset so that the locally stored text files are available within the project as a managed data source. The content must then be made searchable for Retrieval-Augmented Generation.
Next, create a Foundry Search resource to provide the search infrastructure that will store and serve the indexed document content. Microsoft documents Azure AI Search as a supported vector store for Foundry and Azure Machine Learning RAG workloads.
Third, create a vector index from the source documents. During vector-index creation, the content is processed into chunks and embeddings, which enables semantic similarity retrieval. Microsoft documents that vector indexes can be created from local files, folders, or registered data assets and then consumed by an Index Lookup operation.
Finally, create a flow and configure it to query the vector index and supply the retrieved context to the connected Azure OpenAI model. Microsoft specifically documents adding an existing vector index to a prompt flow through the Index Lookup tool.
Create a connection is the unused action in this question because the scenario already specifies a connected Azure OpenAI Service model.
Study Guide Reference: Design and implement a GenAIOps infrastructure - RAG architecture, project data, vector indexing, Azure AI Search, prompt flow, and grounding with enterprise content.
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