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

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Microsoft Designing and Implementing a Data Science Solution on Azure Sample Questions (Q314-Q319):

NEW QUESTION # 314
You create an Azure Machine Learning workspace. You use the Azure Machine Learning SDK for Python.
You must create a dataset from remote paths. The dataset must be reusable within the workspace.
You need to create the dataset.
How should you complete the following code segment? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation:

NEW QUESTION # 315
You are developing a machine learning model by using Azure Machine Learning. You are using multiple text files in tabular format for model dat a. You have the following requirements:
* You must use AutoML jobs to train the model.
* You must use data from specified columns.
* The data concept must support lazy evaluation.
You need to load data into a Pandas dataframe.
Which data concept should you use?

A. MLTable
B. URI
C. Data asset
D. Datastore

Answer: A

NEW QUESTION # 316
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.
You need to add logging to the script to allow Hyperdrive to optimize hyperparameters for the AUC metric.
Solution: Run the following code:

Does the solution meet the goal?

A. Yes
B. No

Answer: A

Explanation:
Python printing/logging example:
logging.info(message)
Destination: Driver logs, Azure Machine Learning designer
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-debug-pipelines

NEW QUESTION # 317
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 create an Azure Machine Learning service datastore in a workspace. The datastore contains the following files:
* /data/2018/Q1 .csv
* /data/2018/Q2.csv
* /data/2018/Q3.csv
* /data/2018/Q4.csv
* /data/2019/Q1.csv
All files store data in the following format:
id,f1,f2,l
1,1,2,0
2,1,1,1
3.2.1.0
You run the following code:

You need to create a dataset named training_data and load the data from all files into a single data frame by using the following code:

Solution: Run the following code:

Does the solution meet the goal?

A. Yes
B. No

Answer: A

Explanation:
Use two file paths.
Use Dataset.Tabular_from_delimeted as the data isn ' t cleansed.
Note:
A TabularDataset represents data in a tabular format by parsing the provided file or list of files. This provides you with the ability to materialize the data into a pandas or Spark DataFrame so you can work with familiar data preparation and training libraries without having to leave your notebook. You can create a TabularDataset object from .csv, .tsv, .parquet, .jsonl files, and from SQL query results.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-create-register-datasets

NEW QUESTION # 318
You are preparing to build a deep learning convolutional neural network model for image classification. You create a script to train the model using CUDA devices. You must submit an experiment that runs this script in the Azure Machine Learning workspace. The following compute resources are available:
* a Microsoft Surface device on which Microsoft Office has been installed. Corporate IT policies prevent the installation of additional software
* a Compute Instance named ds-workstation in the workspace with 2 CPUs and 8 GB of memory
* an Azure Machine Learning compute target named cpu-cluster with eight CPU-based nodes
* an Azure Machine Learning compute target named gpu-cluster with four CPU and GPU-based nodes

Answer:

Explanation:

NEW QUESTION # 319
......

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