Microsoft DP-100 Questions Boost Your Exam Preparation 2026

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Microsoft DP-100 Exam Syllabus Topics:

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

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Microsoft Designing and Implementing a Data Science Solution on Azure Sample Questions (Q231-Q236):

NEW QUESTION # 231
You are performing a classification task in Azure Machine learning Studio.
You must prepare balanced testing and training samples based on a provided data set.
Warning samples based on a provided data set.
You need to split the data with a 0.75:0.25.
Which value should you use for each parameter? To answer, select the appropriate options m the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

NEW QUESTION # 232
You create a binary classification model using Azure Machine Learning Studio.
You must use a Receiver Operating Characteristic (RO C) curve and an F1 score to evaluate the model.
You need to create the required business metrics.
How should you complete the experiment? To answer, select the appropriate options in the dialog box in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

NEW QUESTION # 233
You create an experiment in Azure Machine Learning Studio- You add a training dataset that contains 10.000 rows. The first 9.000 rows represent class 0 (90 percent). The first 1.000 rows represent class 1 (10 percent).
The training set is unbalanced between two Classes. You must increase the number of training examples for class 1 to 4,000 by using data rows. You add the Synthetic Minority Oversampling Technique (SMOTE) module to the experiment.
You need to configure the module.
Which values should you use? To answer, select the appropriate options in the dialog box in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

NEW QUESTION # 234
You manage an Azure Machine Learning workspace. You submit a training job with the Azure Machine Learning Python SDK v2. You must use MLflow to log metrics, model parameters, and mode! artifacts automatically when training a model.
You start by writing the following code segment:

For each of the following statements, select Yes If the statement is true. Otherwise, select No.

Answer:

Explanation:

Explanation:

NEW QUESTION # 235
You create a datastore named training_data that references a blob container in an Azure Storage account. The blob container contains a folder named csv_files in which multiple comma-separated values (CSV) files are stored.
You have a script named train.py in a local folder named ./script that you plan to run as an experiment using an estimator. The script includes the following code to read data from the csv_files folder:

You have the following script.

You need to configure the estimator for the experiment so that the script can read the data from a data reference named data_ref that references the csv_files folder in the training_data datastore.
Which code should you use to configure the estimator?

A. Option A
B. Option D
C. Option B
D. Option C
E. Option E

Answer: C

Explanation:
Besides passing the dataset through the inputs parameter in the estimator, you can also pass the dataset through script_params and get the data path (mounting point) in your training script via arguments. This way, you can keep your training script independent of azureml-sdk. In other words, you will be able use the same training script for local debugging and remote training on any cloud platform.
Example:
from azureml.train.sklearn import SKLearn
script_params = {
# mount the dataset on the remote compute and pass the mounted path as an argument to the training script
'--data-folder': mnist_ds.as_named_input('mnist').as_mount(),
'--regularization': 0.5
}
est = SKLearn(source_directory=script_folder,
script_params=script_params,
compute_target=compute_target,
environment_definition=env,
entry_script='train_mnist.py')
# Run the experiment
run = experiment.submit(est)
run.wait_for_completion(show_output=True)
Incorrect Answers:
A: Pandas DataFrame not used.
Reference:
https://docs.microsoft.com/es-es/azure/machine-learning/how-to-train-with-datasets

NEW QUESTION # 236
......

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