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Microsoft DP-100 Exam Syllabus Topics:

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

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Microsoft Designing and Implementing a Data Science Solution on Azure Sample Questions (Q102-Q107):

NEW QUESTION # 102

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:

NEW QUESTION # 103
You manage an Azure Machine Learning workspace.
You must log multiple metrics by using MLflow.
You need to maximize logging performance.
What are two possible ways to achieve this goal? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.

A. mlflow.log. metric
B. mlflow.log_param
C. mlflowlog_metrics
D. MLflowClient.log_batch

Answer: C,D

NEW QUESTION # 104
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 use Azure Machine Learning designer to load the following datasets into an experiment:

You need to create a dataset that has the same columns and header row as the input datasets and contains all rows from both input datasets.
Solution: Use the Join Data module.
Does the solution meet the goal?

A. No
B. Yes

Answer: A

NEW QUESTION # 105
You create an Azure Machine learning workspace and load a Python training script named tram.py in the src subfolder. The dataset used to train your model is available locally. You run the following script to tram the model:

instructions: 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:

Explanation

NEW QUESTION # 106
You need to define a modeling strategy for ad response.
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: Implement a K-Means Clustering model
Step 2: Use the cluster as a feature in a Decision jungle model.
Decision jungles are non-parametric models, which can represent non-linear decision boundaries.
Step 3: Use the raw score as a feature in a Score Matchbox Recommender model The goal of creating a recommendation system is to recommend one or more "items" to "users" of the system.
Examples of an item could be a movie, restaurant, book, or song. A user could be a person, group of persons, or other entity with item preferences.
Scenario:
Ad response rated declined.
Ad response models must be trained at the beginning of each event and applied during the sporting event.
Market segmentation models must optimize for similar ad response history.
Ad response models must support non-linear boundaries of features.
References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/multiclass-decision-jungle
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/score-matchbox- recommender

NEW QUESTION # 107
......

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