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Microsoft DP-100 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Train and deploy models25-30%- Monitor and maintain models
  • 1. Update and retrain models
  • 2. Implement MLOps practices
  • 3. Monitor performance and data drift
- Train models
  • 1. Use HyperDrive for hyperparameter tuning
  • 2. Configure jobs and environments
  • 3. Run training scripts
  • 4. Apply responsible AI principles
- Deploy models
  • 1. Secure endpoints and manage access
  • 2. Deploy to online endpoints
  • 3. Configure compute and scaling
  • 4. Deploy to batch endpoints
- Manage models
  • 1. Package and validate models
  • 2. Register and version models
  • 3. Interpret models and explain predictions
Topic 2: Optimize language models for AI applications25-30%- Evaluate and improve models
  • 1. Apply responsible generative AI
  • 2. Optimize for accuracy and safety
  • 3. Test and evaluate responses
- Optimize with Retrieval Augmented Generation
  • 1. Create vector stores and indexes
  • 2. Configure Azure AI Search
  • 3. Prepare and process data
- Implement generative AI solutions
  • 1. Build prompt flows
  • 2. Use Azure AI Foundry
  • 3. Apply prompt engineering
Topic 3: Design and prepare a machine learning solution20-25%- Manage data assets
  • 1. Select storage services
  • 2. Create and maintain data assets
  • 3. Register and manage datastores
- Design a machine learning solution
  • 1. Select development approach
  • 2. Plan model deployment requirements
  • 3. Define compute specifications for workloads
  • 4. Determine dataset structure and format
- 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
Topic 4: Explore data and run experiments20-25%- Explore and visualize data
  • 1. Profile and validate data
  • 2. Detect anomalies and outliers
  • 3. Identify features and relationships
- Run experiments
  • 1. Track runs with MLflow
  • 2. Configure experiment runs
  • 3. Define parameters and configurations
  • 4. Use automated machine learning
- Implement pipelines
  • 1. Pass data between steps
  • 2. Build reusable components
  • 3. Create and publish pipelines
  • 4. Schedule and monitor pipelines

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Pass Guaranteed Quiz DP-100 - Designing and Implementing a Data Science Solution on Azure Useful Guaranteed Questions Answers

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Microsoft Designing and Implementing a Data Science Solution on Azure Sample Questions (Q277-Q282):

NEW QUESTION # 277
You are using the Hyperdrive feature in Azure Machine Learning to train a model.
You configure the Hyperdrive experiment by running the following code:

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:

Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-tune-hyperparameters


NEW QUESTION # 278
You create a multi-class image classification deep learning model.
You train the model by using PyTorch version 1.2.
You need to ensure that the correct version of PyTorch can be identified for the inferencing environment when the model is deployed.
What should you do?

Answer: D

Explanation:
Explanation
framework_version: The PyTorch version to be used for executing training code.
Reference:
https://docs.microsoft.com/en-us/python/api/azureml-train-core/azureml.train.dnn.pytorch?view=azure-ml-py


NEW QUESTION # 279
You are creating a machine learning model that can predict the species of a penguin from its measurements. You have a file that contains measurements for free species of penguin in comma delimited format.
The model must be optimized for area under the received operating characteristic curve performance metric averaged for each class.
You need to use the Automated Machine Learning user interface in Azure Machine Learning studio to run an experiment and find the best performing model.
Which five actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the collect order.

Answer:

Explanation:

1 - Create and select a new dataset...
2 - Select the Classification task type.
3 - Set the Primary metric configuration setting to Accuracy.
4 - Configure the automated machine learning...
5 - Run the automated machine learning experiment and review the results.


NEW QUESTION # 280
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 have a Python script named train.py in a local folder named scripts. The script trains a regression model by using scikit-learn. The script includes code to load a training data file which is also located in the scripts folder.
You must run the script as an Azure ML experiment on a compute cluster named aml-compute.
You need to configure the run to ensure that the environment includes the required packages for model training. You have instantiated a variable named aml-compute that references the target compute cluster.
Solution: Run the following code:

Does the solution meet the goal?

Answer: B

Explanation:
The scikit-learn estimator provides a simple way of launching a scikit-learn training job on a compute target. It is implemented through the SKLearn class, which can be used to support single-node CPU training.
Example:
from azureml.train.sklearn import SKLearn
}
estimator = SKLearn(source_directory=project_folder,
compute_target=compute_target,
entry_script='train_iris.py'
)
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-train-scikit-learn


NEW QUESTION # 281
You create an Azure Machine Learning workspace. The workspace contains a dataset named sample.dataset, a compute instance, and a compute cluster. You must create a two-stage pipeline that will prepare data in the dataset and then train and register a model based on the prepared dat a. The first stage of the pipeline contains the following code:

You need to identify the location containing the output of the first stage of the script that you can use as input for the second stage. Which storage location should you use?

Answer: B


NEW QUESTION # 282
......

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