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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Design and implement an MLOps infrastructure | 15–20% | - Create and manage Machine Learning workspace resources and assets
|
| Topic 2: Implement machine learning model lifecycle and operations | 25–30% | - Deploy models to production
|
| Topic 3: Optimize generative AI systems and model performance | 15–20% | - Improve efficiency and cost-effectiveness
|
| Topic 4: Implement generative AI quality assurance and observability | 10–15% | - Monitor generative AI systems
|
| Topic 5: Design and implement a GenAIOps infrastructure | 20–25% | - Set up Microsoft Foundry environment
|
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NEW QUESTION # 70
You manage an Azure Machine Learning workspace named workspace1 by using the Python SDK v2.
The default datastore of workspace1 contains a folder named sample_data.
The folder structure contains the following content:
You write Python SDK v2 code to materialize the data from the files in the sample_data folder into a Pandas data frame.
You need to complete the Python SDK v2 code to use the MLTable folder as the materialization blueprint.
How should you complete the code? To answer, select the appropriate options in the answer area. NOTE:
Each correct selection is worth one point
Answer:
Explanation:
Explanation:
The MLTable format in Azure Machine Learning Python SDK v2 is a structured abstraction over tabular data sources. It stores a YAML file alongside data files, describing how to read and transform the data. To materialize this into a Pandas DataFrame, you use the mltable library: first call mltable.load with the path to the MLTable folder to load the definition, then call to_pandas_dataframe() on the returned object. The path parameter should point to the folder containing the MLTable YAML file on the default datastore. MLTable abstracts the storage location and transformation steps, making the code storage-agnostic - the same code works whether data lives in Blob Storage, ADLS Gen2, or a local path. The SDK v2 approach with MLTable is the recommended pattern for governed, reusable data access in Azure Machine Learning.
Microsoft Learn Reference Topic: Create and use MLTable data assets in Azure Machine Learning Python SDK v2
NEW QUESTION # 71
You create an Azure Machine Learning model to include model files and a scorning script. You must deploy the model. The deployment solution must meet the following requirements:
* Provide near real-time inferencing.
* Enable endpoint and deployment level cost estimates.
* Support logging to Azure Log Analytics.
You need to configure the deployment solution.
What should you configure? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
NEW QUESTION # 72
You have a Microsoft Foundry project.
You plan to use the Microsoft Foundry portal to fine-tune a base Azure OpenAI Service model that can accept both text and images as input.
You need to choose the suitable model.
Which model should you choose?
Answer: C
Explanation:
GPT-4o is OpenAI ' s multimodal model that accepts both text and image inputs natively. The ' o ' in GPT-4o stands for ' omni, ' reflecting its ability to process multiple modalities in a single inference call. It is available in Microsoft Foundry for both inference and fine-tuning, and it supports vision inputs alongside text prompts, making it the correct choice for any fine-tuning scenario requiring multimodal input. Davinci-002 (option A) is a legacy text-completion model with no vision capabilities and limited fine-tuning support for modern chat formats. GPT-3.5-Turbo (option C) is a text-only chat model that does not accept image inputs. GPT-4 (option D) has a standard version that is text-only; the vision variant GPT-4V is distinct from GPT-4o and has more limited fine-tuning availability in Foundry. For multimodal text plus image fine-tuning, GPT-4o is the only correct choice.
Microsoft Learn Reference Topic: Azure OpenAI Service models available for fine-tuning - GPT-4o multimodal capabilities
NEW QUESTION # 73
-
A team operates a generative AI-powered customer support assistant built on Microsoft Foundry. The application serves users globally and supports both real-time chat interactions and batch summarization jobs.
The team must ensure that the application continues to meet defined service-level objectives (SLO) as usage increases.
The team requires visibility into runtime behavior to identify performance regressions that affect the user experience and system capacity.
You need to select the performance metrics that meet the requirements.
Which performance metric should you monitor for each requirement? To answer, move the appropriate performance metrics to the correct requirements. You may use each performance metric once, more than once, or not at all. You may need to move the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Detect when the system is slow at returning output: Latency
Identify when the system cannot sustain expected request volume: Throughput Measure the duration of handling a request: Response time Latency measures delay experienced while waiting for model output and is therefore the appropriate metric for identifying degradation in perceived responsiveness. Microsoft describes latency as the time required to obtain a response from the model and exposes metrics such as Time to Response/Time to First Token and Time Between Tokens for generative AI workloads. An increase indicates that users are waiting longer for output to begin or continue.
Throughput represents the amount of workload the system can process over a unit of time. Microsoft describes system-level throughput in terms such as requests per minute and tokens per minute. Consequently, throughput is the correct metric when determining whether a deployment can sustain the required request volume as demand increases.
Response time represents the overall duration required to handle an individual request. It is appropriate for measuring end-to-end request processing and validating request-duration SLOs. Microsoft specifically includes latency, throughput, and response times among the performance metrics candidates are expected to monitor for generative AI applications.
Concurrency measures simultaneous active requests and can influence capacity and latency, but none of these requirements directly asks for simultaneous-request count.
Study Guide Reference: Implement generative AI quality assurance and observability - continuous monitoring, latency, throughput, response times, production troubleshooting, and SLO monitoring.
NEW QUESTION # 74
You create an Azure Machine Learning workspace
You are developing a Python SDK v2 notebook to perform custom model training in the workspace. The notebook code imports all required packages.
You need to complete the Python SDK v2 code to include a training script. environment, and compute information.
How should you complete ten code? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point
Answer:
Explanation:
Explanation:
NEW QUESTION # 75
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