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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Design and implement a GenAIOps infrastructure | 20–25% | - Implement infrastructure for generative AI workloads
|
| Topic 2: Implement machine learning model lifecycle and operations | 25–30% | - Monitor and maintain models in production
|
| Topic 3: Implement generative AI quality assurance and observability | 10–15% | - Monitor generative AI systems
|
| 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% | - Optimize model selection and configuration
|
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NEW QUESTION # 126
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?
Answer: D
Explanation:
Azure OpenAI Service fine-tuning requires training data in JSON Lines (JSONL) format - a text file where each line is a self-contained, valid JSON object representing one training example. For chat fine-tuning, each line contains a messages array with system, user, and assistant turns. JSONL is ideal for machine-learning datasets because it is streamable, easy to generate programmatically, and directly supported by the Azure OpenAI fine-tuning API. CSV (option A) and TSV (option B) are tabular formats that cannot natively represent the nested, multi-turn conversation structure required by chat models. Plain JSON (option D) would require loading the entire file as a single object, which is impractical for large datasets and is not accepted by the Azure OpenAI fine-tuning endpoint. Always use JSONL with UTF-8 encoding for Azure OpenAI training files.
Microsoft Learn Reference Topic: Prepare training data for Azure OpenAI fine-tuning - JSONL file format
NEW QUESTION # 127
You are training machine learning models in Azure Machine Learning. You use Hyperdrive to tune the hyperparameters.
In previous model training and tuning runs, many models showed similar performance.
You need to select an early termination policy that meets the following requirements:
* Accounts for the performance of all previous runs when evaluating the current run.
* Avoids comparing the current run with only the best performing run to date.
Which two early termination policies should you use? Each correct answer presents part of the solution.
Answer: A,C
Explanation:
Median stopping and Truncation selection both evaluate a run relative to the broader population of training runs rather than comparing it only against the single best run.
The Median Stopping Policy calculates running averages of the primary metric across all runs . At an evaluation interval, a run can be terminated when its best primary-metric performance is worse than the median of the running averages across the population. Microsoft explicitly defines this policy as being based on running averages of the primary metric of all runs.
The Truncation Selection Policy periodically ranks runs by their primary metric and terminates a configured percentage of the lowest-performing runs . For example, a 20-percent truncation policy terminates runs falling within the lowest 20 percent at the applicable evaluation interval. It therefore compares each candidate with the population of comparable runs rather than only the current leader.
Bandit is specifically unsuitable because it uses an allowable slack relative to the best-performing run , which directly violates the requirement. The Default behavior applies no early termination policy and allows runs to execute to completion.
Study Guide Reference: Implement machine learning model lifecycle and operations - automated hyperparameter tuning, early termination policies, Median Stopping Policy, Truncation Selection Policy, and efficient experiment execution.
NEW QUESTION # 128
You manage an Azure Machine Learning workspace.
You must create and configure a compute cluster for a training job by using Python SDK v2.
You need to create a persistent Azure Machine Learning compute resource, specifying the fewest possible properties.
Which two properties should you define? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
Answer: B,E
NEW QUESTION # 129
Hotspot Question
You download a .csv file from a notebook in an Azure Machine Learning workspace to a data/sample.csv folder on a compute instance. The file contains 10,000 records.
You must generate the summary statistics for the data in the file. The statistics must include the following for each numerical column:
- number of non-empty values
- average value
- standard deviation
- minimum and maximum values
- 25th, 50th, and 75th percentiles
You need to complete the Python code that will generate the summary statistics.
Which code segments should you use? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Box 1: pandas
Pandas read_csv() - How to read a csv file in Python
Most of the data is available in a tabular format of CSV files. It is very popular. You can convert them to a pandas DataFrame using the read_csv function. The pandas.read_csv is used to load a CSV file as a pandas dataframe.
Step 1: pandas
import pandas as pd
Step 2: Read the CSV
# Read the csv file
df = pd.read_csv("data1.csv")
Box 2: describe
The pandas.describe function is used to get a descriptive statistics summary of a given dataframe. This includes mean, count, std deviation, percentiles, and min-max values of all the features.
Pandas Describe function on DataFrame
On applying pandas describe function to a dataframe, the result is also returned as a dataframe .
This dataframe will consist of a statistics summary for all the numeric features of the dataframe.
# Create a dataframe
df = pd.DataFrame({
'Subject_1_Marks': [14, 42, 21, 12, 45],
'Subject_2_Marks': [32, 43, 23, 50, 21],
'Subject_3_Marks': [45.0, 34.0, 23.0, 8.0, 21.0],
'Names': ['Saksham', 'Ayushi', 'Abhishek', 'Saksham', 'Saumya']
}
)
# Apply describe function
df.describe()
Incorrect:
* rank
Reference:
https://www.machinelearningplus.com/pandas/pandas-read_csv-completed/
https://www.machinelearningplus.com/pandas/pandas-describe/
NEW QUESTION # 130
You create an Azure Machine Learning workspace.
You must use the Python SDK v2 to implement an experiment from a Jupyter notebook in the workspace. The experiment must log a table in the following format:
You need to complete the Python code to log the table.
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:
NEW QUESTION # 131
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