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Microsoft DP-100 Exam Syllabus Topics:

Section	Weight	Objectives
Optimize language models for AI applications	25-30%	- Optimize with Retrieval Augmented Generation 1. Configure Azure AI Search 2. Create vector stores and indexes 3. Prepare and process data - Implement generative AI solutions 1. Apply prompt engineering 2. Build prompt flows 3. Use Azure AI Foundry - Evaluate and improve models 1. Test and evaluate responses 2. Apply responsible generative AI 3. Optimize for accuracy and safety
Explore data and run experiments	20-25%	- Implement pipelines 1. Schedule and monitor pipelines 2. Build reusable components 3. Pass data between steps 4. Create and publish pipelines - Explore and visualize data 1. Profile and validate data 2. Detect anomalies and outliers 3. Identify features and relationships - Run experiments 1. Define parameters and configurations 2. Configure experiment runs 3. Use automated machine learning 4. Track runs with MLflow
Train and deploy models	25-30%	- Deploy models 1. Secure endpoints and manage access 2. Configure compute and scaling 3. Deploy to online endpoints 4. Deploy to batch endpoints - Train models 1. Apply responsible AI principles 2. Run training scripts 3. Configure jobs and environments 4. Use HyperDrive for hyperparameter tuning - Monitor and maintain models 1. Monitor performance and data drift 2. Update and retrain models 3. Implement MLOps practices - Manage models 1. Register and version models 2. Package and validate models 3. Interpret models and explain predictions
Design and prepare a machine learning solution	20-25%	- Manage Azure Machine Learning workspace 1. Use developer tools and CLI 2. Set up Git integration 3. Create and configure workspace 4. Work with registries - Manage compute resources 1. Create and configure compute targets 2. Select environments 3. Attach and monitor compute - Design a machine learning solution 1. Plan model deployment requirements 2. Define compute specifications for workloads 3. Determine dataset structure and format 4. Select development approach - Manage data assets 1. Register and manage datastores 2. Select storage services 3. Create and maintain data assets

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Microsoft Designing and Implementing a Data Science Solution on Azure Sample Questions (Q418-Q423):

NEW QUESTION # 418
Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.
After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.
You create a model to forecast weather conditions based on historical data.
You need to create a pipeline that runs a processing script to load data from a datastore and pass the processed data to a machine learning model training script.
Solution: Run the following code:

Does the solution meet the goal?

A. Yes
B. No

Answer: B

Explanation:
train_step is missing.
Reference:
https://docs.microsoft.com/en-us/python/api/azureml-pipeline-core/azureml.pipeline.core.pipelinedata?view=azu

NEW QUESTION # 419
You register a file dataset named csvjolder that references a folder. The folder includes multiple com ma- separated values (CSV) files in an Azure storage blob container. You plan to use the following code to run a script that loads data from the file dataset. You create and instantiate the following variables:

You have the following code:

You need to pass the dataset to ensure that the script can read the files it references. Which code segment should you insert to replace the code comment?

Answer: B

Explanation:
Example:
from azureml.train.estimator import Estimator
script_params = {
# to mount files referenced by mnist dataset
'--data-folder': mnist_file_dataset.as_named_input('mnist_opendataset').as_mount(),
'--regularization': 0.5
}
est = Estimator(source_directory=script_folder,
script_params=script_params,
compute_target=compute_target,
environment_definition=env,
entry_script='train.py')
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/tutorial-train-models-with-aml

NEW QUESTION # 420
You register a file dataset named csvjolder that references a folder. The folder includes multiple com ma- separated values (CSV) files in an Azure storage blob container. You plan to use the following code to run a script that loads data from the file dataset. You create and instantiate the following variables:

You have the following code:

You need to pass the dataset to ensure that the script can read the files it references. Which code segment should you insert to replace the code comment?

Answer: C

Explanation:
Example:
from azureml.train.estimator import Estimator
script_params = {
# to mount files referenced by mnist dataset
' --data-folder ' : mnist_file_dataset.as_named_input( ' mnist_opendataset ' ).as_mount(),
' --regularization ' : 0.5
}
est = Estimator(source_directory=script_folder,
script_params=script_params,
compute_target=compute_target,
environment_definition=env,
entry_script= ' train.py ' )
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/tutorial-train-models-with-aml

NEW QUESTION # 421
Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.
After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.
You are using Azure Machine Learning to run an experiment that trains a classification model.
You want to use Hyperdrive to find parameters that optimize the AUC metric for the model. You configure a HyperDriveConfig for the experiment by running the following code:

You plan to use this configuration to run a script that trains a random forest model and then tests it with validation data. The label values for the validation data are stored in a variable named y_test variable, and the predicted probabilities from the model are stored in a variable named y_predicted.
You need to add logging to the script to allow Hyperdrive to optimize hyperparameters for the AUC metric.
Solution: Run the following code:

Does the solution meet the goal?

A. Yes
B. No

Answer: B

Explanation:
Use a solution with logging.info(message) instead.
Note: Python printing/logging example:
logging.info(message)
Destination: Driver logs, Azure Machine Learning designer
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-debug-pipelines

NEW QUESTION # 422
You are developing a linear regression model in Azure Machine Learning Studio. You run an experiment to compare different algorithms.
The following image displays the results dataset output:

Use the drop-down menus to select the answer choice that answers each question based on the information presented in the image.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation

Box 1: Boosted Decision Tree Regression
Mean absolute error (MAE) measures how close the predictions are to the actual outcomes; thus, a lower score is better.
Box 2:
Online Gradient Descent: If you want the algorithm to find the best parameters for you, set Create trainer mode option to Parameter Range. You can then specify multiple values for the algorithm to try.
References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/evaluate-model
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/linear-regression

NEW QUESTION # 423
......

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