100% Pass DP-100 - Valid Designing and Implementing a Data Science Solution on Azure Cram Materials

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Microsoft DP-100 Exam Syllabus Topics:

SectionWeightObjectives
Design and prepare a machine learning solution20-25%- Design a machine learning solution
  • 1. Define compute specifications for workloads
  • 2. Plan model deployment requirements
  • 3. Determine dataset structure and format
  • 4. Select development approach
- Manage compute resources
  • 1. Select environments
  • 2. Create and configure compute targets
  • 3. Attach and monitor compute
- 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 data assets
  • 1. Select storage services
  • 2. Register and manage datastores
  • 3. Create and maintain data assets
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
- 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. Use Azure AI Foundry
  • 3. Build prompt flows
Explore data and run experiments20-25%- Run experiments
  • 1. Use automated machine learning
  • 2. Track runs with MLflow
  • 3. Define parameters and configurations
  • 4. Configure experiment runs
- Implement pipelines
  • 1. Build reusable components
  • 2. Schedule and monitor pipelines
  • 3. Create and publish pipelines
  • 4. Pass data between steps
- Explore and visualize data
  • 1. Identify features and relationships
  • 2. Profile and validate data
  • 3. Detect anomalies and outliers
Train and deploy models25-30%- Monitor and maintain models
  • 1. Implement MLOps practices
  • 2. Monitor performance and data drift
  • 3. Update and retrain models
- Deploy models
  • 1. Deploy to batch endpoints
  • 2. Secure endpoints and manage access
  • 3. Deploy to online endpoints
  • 4. Configure compute and scaling
- Train models
  • 1. Configure jobs and environments
  • 2. Use HyperDrive for hyperparameter tuning
  • 3. Run training scripts
  • 4. Apply responsible AI principles
- Manage models
  • 1. Package and validate models
  • 2. Register and version models
  • 3. Interpret models and explain predictions

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Microsoft Designing and Implementing a Data Science Solution on Azure Sample Questions (Q78-Q83):

NEW QUESTION # 78
You need to use the Python language to build a sampling strategy for the global penalty detection models.
How should you complete the code segment? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Reference:
https://github.com/pytorch/pytorch/blob/master/torch/utils/data/distributed.py


NEW QUESTION # 79
Hotspot Question
You arc I mating a deep learning model to identify cats and dogs. You have 25,000 color images.
You must meet the following requirements:
- Reduce the number of training epochs.
- Reduce the size of the neural network.
- Reduce over-fitting of the neural network.
You need to select the image modification values.
Which value should you use? To answer, select the appropriate Options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:


NEW QUESTION # 80
You download a .csv file from a notebook in an Azure Machine Learning workspace to a data/sample.csv folder on a compute instance. The file contains 10,000 records. You must generate the summary statistics for the data in the file. The statistics must include the following for each numerical column:
* number of non-empty values
* average value
* standard deviation
* minimum and maximum values
* 25th. 50th. and 75th percentiles
You need to complete the Python code that will generate the summary statistics.
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 # 81
You have the following Azure subscriptions and Azure Machine Learning service workspaces:

You need to obtain a reference to the ml-project workspace.
Solution: Run the following Python code:

Does the solution meet the goal?

Answer: A


NEW QUESTION # 82
You are creating an experiment by using Azure Machine Learning Studio.
You must divide the data into four subsets for evaluation. There is a high degree of missing values in the data.
You must prepare the data for analysis.
You need to select appropriate methods for producing the experiment.
Which three modules should you run in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.
NOTE: More than one order of answer choices is correct. You will receive credit for any of the correct orders you select.

Answer:

Explanation:

Explanation:

The Clean Missing Data module in Azure Machine Learning Studio, to remove, replace, or infer missing values.


NEW QUESTION # 83
......

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