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| Section | Weight | Objectives |
|---|
| Topic 1: Explore data and run experiments | 20-25% | - Explore and visualize data
- 1. Profile and validate data
- 2. Identify features and relationships
- 3. Detect anomalies and outliers
- Implement pipelines
- 1. Build reusable components
- 2. Schedule and monitor pipelines
- 3. Pass data between steps
- 4. Create and publish pipelines
- Run experiments
- 1. Use automated machine learning
- 2. Define parameters and configurations
- 3. Track runs with MLflow
- 4. Configure experiment runs
|
| Topic 2: 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
- Manage models
- 1. Interpret models and explain predictions
- 2. Package and validate models
- 3. Register and version models
- Monitor and maintain models
- 1. Update and retrain models
- 2. Monitor performance and data drift
- 3. Implement MLOps practices
- Train models
- 1. Configure jobs and environments
- 2. Use HyperDrive for hyperparameter tuning
- 3. Run training scripts
- 4. Apply responsible AI principles
|
| Topic 3: Optimize language models for AI applications | 25-30% | - Optimize with Retrieval Augmented Generation
- 1. Prepare and process data
- 2. Create vector stores and indexes
- 3. Configure Azure AI Search
- Implement generative AI solutions
- 1. Use Azure AI Foundry
- 2. Apply prompt engineering
- 3. Build prompt flows
- Evaluate and improve models
- 1. Apply responsible generative AI
- 2. Optimize for accuracy and safety
- 3. Test and evaluate responses
|
| Topic 4: Design and prepare a machine learning solution | 20-25% | - Design a machine learning solution
- 1. Determine dataset structure and format
- 2. Plan model deployment requirements
- 3. Select development approach
- 4. Define compute specifications for workloads
- Manage compute resources
- 1. Attach and monitor compute
- 2. Select environments
- 3. Create and configure compute targets
- Manage data assets
- 1. Register and manage datastores
- 2. Select storage services
- 3. Create and maintain data assets
- Manage Azure Machine Learning workspace
- 1. Work with registries
- 2. Use developer tools and CLI
- 3. Create and configure workspace
- 4. Set up Git integration
|
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For Microsoft professionals, passing the Designing and Implementing a Data Science Solution on Azure exams such as the DP-100 Exam is essential to achieve their dream professional life. However, passing the Designing and Implementing a Data Science Solution on Azure (DP-100) Exam is not an easy task, especially for those with busy schedules who need time to prepare well for the DP-100 Exam. To ensure success on the DP-100 Exam, you need Microsoft DP-100 Exam Questions that contain all the relevant information about the exam.
Microsoft Designing and Implementing a Data Science Solution on Azure Sample Questions (Q338-Q343):
NEW QUESTION # 338
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.
Solution: Run the following code:

Does the solution meet the goal?
Answer: B
NEW QUESTION # 339
You are building a machine learning model for translating English language textual content into French language textual content.
You need to build and train the machine learning model to learn the sequence of the textual content.
Which type of neural network should you use?
- A. Multilayer Perceptions (MLPs)
- B. Generative Adversarial Networks (GANs)
- C. Convolutional Neural Networks (CNNs)
- D. Recurrent Neural Networks (RNNs)
Answer: D
Explanation:
To translate a corpus of English text to French, we need to build a recurrent neural network (RNN).
Note: RNNs are designed to take sequences of text as inputs or return sequences of text as outputs, or both. They're called recurrent because the network's hidden layers have a loop in which the output and cell state from each time step become inputs at the next time step. This recurrence serves as a form of memory.
It allows contextual information to flow through the network so that relevant outputs from previous time steps can be applied to network operations at the current time step.
References:
https://towardsdatascience.com/language-translation-with-rnns-d84d43b40571
NEW QUESTION # 340
You create an Azure Data Lake Storage Gen2 stowage account named storage1 containing a file system named fsi and a folder named folder1.
The contents of folder1 must be accessible from jobs on compute targets in the Azure Machine Learning workspace.
You need to construct a URl to reference folder1.
How should you construct the URI? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:
Explanation:

Explanation:

NEW QUESTION # 341
You are analyzing the asymmetry in a statistical distribution.
The following image contains two density curves that show the probability distribution of two datasets.

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

Answer:
Explanation:

Explanation:
Box 1: Positive skew
Positive skew values means the distribution is skewed to the right.
Box 2: Negative skew
Negative skewness values mean the distribution is skewed to the left.
References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/compute-elementary-statistics
NEW QUESTION # 342
You use Azure Machine Learning to deploy a model as a real-time web service.
You need to create an entry script for the service that ensures that the model is loaded when the service starts and is used to score new data as it is received.
Which functions should you include in the script? To answer, drag the appropriate functions to the correct actions. Each function may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content NOTE: Each correct selection is worth one point.

Answer:
Explanation:

NEW QUESTION # 343
......
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