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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Implement generative AI quality assurance and observability | 10–15% | - Evaluate and test generative AI applications
|
| Topic 2: Design and implement a GenAIOps infrastructure | 20–25% | - Set up Microsoft Foundry environment
|
| Topic 3: Implement machine learning model lifecycle and operations | 25–30% | - Monitor and maintain models in production
|
| Topic 4: Design and implement an MLOps infrastructure | 15–20% | - Create and manage Machine Learning workspace resources and assets
|
| Topic 5: Optimize generative AI systems and model performance | 15–20% | - Improve efficiency and cost-effectiveness
|
>> New AI-300 Practice Questions <<
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NEW QUESTION # 136
You manage an Azure Machine Learning workspace. You have a folder that contains a CSV file. The folder is registered as a folder data asset.
You plan to use the folder data asset for data wrangling during interactive development.
You need to access and load the folder data asset into a Pandas data frame.
Which method should you use to achieve this goal?
Answer: D
Explanation:
Azure Machine Learning distinguishes between uri_folder, uri_file, and mltable data assets. Because the registered asset in this scenario is a folder data asset containing CSV data , the appropriate MLTable API is mltable.from_delimited_files(). Microsoft documents that a registered uri_folder containing CSV files can be retrieved through MLClient, its storage path supplied as a folder path, and then passed to mltable.
from_delimited_files(). The resulting MLTable object can subsequently be materialized into a Pandas DataFrame by calling to_pandas_dataframe().
A representative workflow is conceptually:
tbl = mltable.from_delimited_files(paths=[{ " folder " : data_asset.path}]) followed by:
df = tbl.to_pandas_dataframe()
mltable.load() is primarily appropriate when loading an existing MLTable definition , such as a registered table data asset containing an MLTable file. from_parquet_files() is intended for Parquet-formatted data, while from_delta_lake() targets Delta Lake data sources. Neither matches the CSV-based folder described here. Microsoft also identifies uri_folder as the appropriate asset type for folders containing CSV or Parquet files used with Pandas or Spark.
Study Guide Reference: Design and implement an MLOps infrastructure - Azure Machine Learning data assets, MLTable APIs, interactive data access, and Pandas-based data wrangling.
Continue with Azure ML data access
NEW QUESTION # 137
Hotspot Question
You manage a Retrieval-Augmented Generation (RAG) system that retrieves internal policy documents from a vector index.
Recent analysis shows that:
- Retrieved results frequently include duplicated content from the same document.
- Retrieved chunks sometimes span unrelated policy sections.
You review the following retrieval and ingestion configurations:
You need to reduce duplicated retrieval results and improve chunk relevance across policy sections. For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
NEW QUESTION # 138
Hotspot Question
You manage a Microsoft Foundry project. You build a solution that uses a set of PDF documents.
You require two large language models (LLMs):
- An embedding model must help categorize the documents.
- A general-purpose model must generate semantically and contextually
accurate output based on the documents.
You need to select benchmarks to observe the quality of the models.
Which metrics should you use for the benchmarks? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Box 1: Accuracy
Embedding model
An embedding model must help categorize the documents.
For an embedding model designed to categorize documents within Azure AI Foundry, cosine similarity is a crucial benchmark metric. It measures the semantic similarity between document embeddings, allowing you to assess how well the model groups related documents together.
Additionally, classification accuracy is important to evaluate how effectively the model assigns documents to predefined categories.
Classification Accuracy:
This metric measures the percentage of documents that are correctly categorized by the model. It directly reflects the model's ability to assign documents to the appropriate category based on their embeddings Box 2: Coherence General-purpose model A general-purpose model must generate semantically and contextually accurate output based on the documents.
Coherence evaluates how well the language model can produce output that flows smoothly, reads naturally, and resembles human-like language.
Incorrect:
* GPTsimilarity
GPT similarity refers to the ability of GPT models to assess and quantify the semantic similarity between pieces of text. This can be achieved by using the model to generate embeddings (numerical representations) of the text and then calculating the similarity (e.g., using cosine similarity) between these embeddings. Essentially, it allows you to determine how closely related two texts are in terms of their meaning.
Reference:
https://learn.microsoft.com/en-us/azure/ai-foundry/concepts/model-benchmarks
https://learn.microsoft.com/en-us/azure/machine-learning/prompt-flow/concept-model-monitoring-generative-ai-evaluation-metrics
NEW QUESTION # 139
You create an Azure Machine Learning workspace.
You must use the Python SDK v2 to implement an experiment from a Jupiter notebook in the workspace. The experiment must log string metrics.
You need to implement the method to log the string metrics.
Which method should you use?
Answer: B
NEW QUESTION # 140
A team is developing a generative AI assistant. The team is experimenting with multiple prompt variants to improve the user experience.
When comparing prompt variants, the team plans to assess whether the generated responses are grammatically correct.
You need to evaluate the quality of the language from the generated responses.
Which evaluator should you use?
Answer: C
Explanation:
The best evaluator within the Microsoft ecosystem for checking the grammatical correctness of generative AI responses--especially when testing multiple prompt variants--is the Azure AI Evaluator for Fluency, available within Azure AI Foundry.
Fluency Evaluator (builtin.fluency)
Purpose: Specifically designed to measure the effectiveness and clarity of written communication.
Grammatical Focus: It assesses grammatical accuracy, sentence structure, punctuation, and vocabulary usage in AI-generated text.
Result: It provides a 1-5 Likert scale score, allowing you to compare which prompt variants produce the most grammatically correct, natural-sounding responses.
Reference:
https://learn.microsoft.com/en-us/azure/machine-learning/prompt-flow/concept-model-monitoring- generative-ai-evaluation-metrics
NEW QUESTION # 141
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