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

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Microsoft Designing and Implementing a Data Science Solution on Azure Sample Questions (Q434-Q439):

NEW QUESTION # 434
You need to correct the model fit issue.
Which three 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 correct order.

Answer:

Explanation:

Explanation

Step 1: Augment the data
Scenario: Columns in each dataset contain missing and null values. The datasets also contain many outliers.
Step 2: Add the Bayesian Linear Regression module.
Scenario: You produce a regression model to predict property prices by using the Linear Regression and Bayesian Linear Regression modules.
Step 3: Configure the regularization weight.
Regularization typically is used to avoid overfitting. For example, in L2 regularization weight, type the value to use as the weight for L2 regularization. We recommend that you use a non-zero value to avoid overfitting.
Scenario:
Model fit: The model shows signs of overfitting. You need to produce a more refined regression model that reduces the overfitting.


NEW QUESTION # 435
Hotspot Question
You create an Azure Machine Learning dataset containing automobile price data. The dataset includes 10,000 rows and 10 columns. You use the Azure Machine Learning designer to transform the dataset by using an Execute Python Script component and custom code.
The code must combine three columns to create a new column.
You need to configure the code function.
Which configurations should you use? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation:
https://learn.microsoft.com/en-us/azure/machine-learning/how-to-designer-python#write-your- python-code


NEW QUESTION # 436
You use the Azure Machine Learning service to create a tabular dataset named training.dat a. You plan to use this dataset in a training script.
You create a variable that references the dataset using the following code:
training_ds = workspace.datasets.get("training_data")
You define an estimator to run the script.
You need to set the correct property of the estimator to ensure that your script can access the training.data dataset Which property should you set?

Answer: B

Explanation:
Example:
# Get the training dataset
diabetes_ds = ws.datasets.get("Diabetes Dataset")
# Create an estimator that uses the remote compute
hyper_estimator = SKLearn(source_directory=experiment_folder,
inputs=[diabetes_ds.as_named_input('diabetes')], # Pass the dataset as an input compute_target = cpu_cluster, conda_packages=['pandas','ipykernel','matplotlib'], pip_packages=['azureml-sdk','argparse','pyarrow'], entry_script='diabetes_training.py') Reference:
https://notebooks.azure.com/GraemeMalcolm/projects/azureml-primers/html/04%20-%20Optimizing%20Model%20Training.ipynb


NEW QUESTION # 437
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 creating a model to predict the price of a student's artwork depending on the following variables: the student's length of education, degree type, and art form.
You start by creating a linear regression model.
You need to evaluate the linear regression model.
Solution: Use the following metrics: Accuracy, Precision, Recall, F1 score and AUC.
Does the solution meet the goal?

Answer: A


NEW QUESTION # 438
Hotspot Question
You manage an Azure Machine Learning workspace by using the Python SDK v2.
You must create an automated machine learning job to generate a classification model by using data files stored in Parquet format.
You must configure an autoscaling compute target and a data asset for the job.
You need to configure the resources for the job.
Which resource configuration should you use? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation:
Compute target: Azure Databricks
This is because Azure Databricks supports autoscaling of workers required to run your job, which dynamically reallocates workers to match the computational demands of your job, thereby achieving high cluster utilization without the need for provisioning the cluster to match a specific workload.
Data asset: uri_folder
This option allows the machine learning job to access all the Parquet files stored in the specified directory. If you have multiple Parquet data files, you would use a URI that points to a folder containing all these files.


NEW QUESTION # 439
......

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