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Microsoft DP-100 Exam Syllabus Topics:

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

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Microsoft Designing and Implementing a Data Science Solution on Azure Sample Questions (Q297-Q302):

NEW QUESTION # 297
You are developing a linear regression model in Azure Machine Learning Studio. You run an experiment to compare different algorithms.
The following image displays the results dataset output:

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

Answer:

Explanation:

Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/evaluate-model
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/linear-regression

NEW QUESTION # 298
You create a pipeline in designer to train a model that predicts automobile prices.
Because of non-linear relationships in the data, the pipeline calculates the natural log (Ln) of the prices in the training data, trains a model to predict this natural log of price value, and then calculates the exponential of the scored label to get the predicted price.
The training pipeline is shown in the exhibit. (Click the Training pipeline tab.) Training pipeline

You create a real-time inference pipeline from the training pipeline, as shown in the exhibit. (Click the Real-time pipeline tab.) Real-time pipeline

You need to modify the inference pipeline to ensure that the web service returns the exponential of the scored label as the predicted automobile price and that client applications are not required to include a price value in the input values.
Which three modifications must you make to the inference pipeline? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.

A. Add a Select Columns module before the Score Model module to select all columns other than price.
B. Remove the Apply SQL Transformation module from the data flow.
C. Connect the output of the Apply SQL Transformation to the Web Service Output module.
D. Remove the Apply Math Operation module that replaces price with its natural log from the data flow.
E. Replace the training dataset module with a data input that does not include the price column.
F. Replace the Web Service Input module with a data input that does not include the price column.

Answer: A,C,D

NEW QUESTION # 299
Drag and Drop Question
You configure a Deep Learning Virtual Machine for Windows.
You need to recommend tools and frameworks to perform the following:
Build deep rwur.il network (DNN) models.
Perform interactive data exploration and visualization.
Which tools and frameworks should you recommend? To answer, drag the appropriate tools to the correct tasks. Each tool 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 # 300
You have an Azure Machine Learning workspace and a collection of image files stored in two Azure Blob Storage accounts. You need to configure data asset properties Which values should you use in your configuration? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

NEW QUESTION # 301
You load data from a notebook in an Azure Machine Learning workspace into a pandas dataframe named df.
The data contains 10.000 patient records. Each record includes the Age property for the corresponding patient.
You must identify the mean age value from the differentially private data generated by SmartNoise SDK.
You need to complete the Python code that will generate the mean age value from the differentially private data.
Which code segments should you use? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation:

NEW QUESTION # 302
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