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Microsoft DP-100 Exam Syllabus Topics:

SectionWeightObjectives
Explore data and run experiments20-25%- Run experiments
  • 1. Track runs with MLflow
  • 2. Use automated machine learning
  • 3. Configure experiment runs
  • 4. Define parameters and configurations
- Implement pipelines
  • 1. Build reusable components
  • 2. Create and publish pipelines
  • 3. Pass data between steps
  • 4. Schedule and monitor pipelines
- Explore and visualize data
  • 1. Profile and validate data
  • 2. Identify features and relationships
  • 3. Detect anomalies and outliers
Train and deploy models25-30%- Deploy models
  • 1. Deploy to online endpoints
  • 2. Secure endpoints and manage access
  • 3. Deploy to batch endpoints
  • 4. Configure compute and scaling
- Train models
  • 1. Apply responsible AI principles
  • 2. Configure jobs and environments
  • 3. Run training scripts
  • 4. Use HyperDrive for hyperparameter tuning
- Monitor and maintain models
  • 1. Update and retrain models
  • 2. Implement MLOps practices
  • 3. Monitor performance and data drift
- Manage models
  • 1. Register and version models
  • 2. Interpret models and explain predictions
  • 3. Package and validate models
Optimize language models for AI applications25-30%- Optimize with Retrieval Augmented Generation
  • 1. Create vector stores and indexes
  • 2. Configure Azure AI Search
  • 3. Prepare and process data
- Implement generative AI solutions
  • 1. Build prompt flows
  • 2. Apply prompt engineering
  • 3. Use Azure AI Foundry
- Evaluate and improve models
  • 1. Apply responsible generative AI
  • 2. Optimize for accuracy and safety
  • 3. Test and evaluate responses
Design and prepare a machine learning solution20-25%- 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 data assets
  • 1. Create and maintain data assets
  • 2. Select storage services
  • 3. Register and manage datastores
- Manage compute resources
  • 1. Create and configure compute targets
  • 2. Select environments
  • 3. Attach and monitor compute
- Manage Azure Machine Learning workspace
  • 1. Set up Git integration
  • 2. Use developer tools and CLI
  • 3. Work with registries
  • 4. Create and configure workspace

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Microsoft Designing and Implementing a Data Science Solution on Azure Sample Questions (Q250-Q255):

NEW QUESTION # 250
You create an Azure Machine Learning dataset containing automobile price dat a. The dataset includes 10.000 rows and 10 columns. You use the Azure Machine Learning designer to transform the dataset by using an Execute Python Script component and custom code.
The code must combine three columns to create a new column.
You need to configure the code function.
Which configurations 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 # 251
You manage an Azure Machine Learning workspace by using the Python SDK v2.
You must create a compute cluster in the workspace. The compute cluster must run workloads and properly handle interruptions. You start by calculating the maximum amount of compute resources required by the workloads and size the cluster to match the calculations.
The cluster definition includes the following properties and values:
* name="mlcluster1''
* size="STANDARD.DS3.v2"
* min_instances=1
* maxjnstances=4
* tier="dedicated"
The cost of the compute resources must be minimized when a workload is active Of idle. Cluster property changes must not affect the maximum amount of compute resources available to the workloads run on the cluster.
You need to modify the cluster properties to minimize the cost of compute resources.
Which properties should you modify? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation:


NEW QUESTION # 252
You are performing a filter based feature selection for a dataset 10 build a multi class classifies by using Azure Machine Learning Studio.
The dataset contains categorical features that are highly correlated to the output label column.
You need to select the appropriate feature scoring statistical method to identify the key predictors. Which method should you use?

Answer: A


NEW QUESTION # 253
You manage an Azure Machine Learning workspace. The workspace includes an Azure Machine Learning Kubernetes compute target configured as an Azure Kubernetes Service (AKS) cluster named AKS1. AKS1 is configured to enable the targeting of different nodes to train workloads.
You must run a command job on AKS1 by using the Azure ML Python SDK v2. The command job must select different types of compute nodes. The compute node types must be specified by using a command parameter.
You need to configure the command parameter.
Which parameter should you use?

Answer: B

Explanation:
The instance_type is optional, which is the type of VM used as supported by the compute target.
The compute is required and is the name of the compute where the command job is executed.


NEW QUESTION # 254
You are developing deep learning models to analyze semi-structured, unstructured, and structured data types.
You have the following data available for model building:
* Video recordings of sporting events
* Transcripts of radio commentary about events
* Logs from related social media feeds captured during sporting events
You need to select an environment for creating the model.
Which environment should you use?

Answer: D

Explanation:
Azure Cognitive Services expand on Microsoft's evolving portfolio of machine learning APIs and enable developers to easily add cognitive features - such as emotion and video detection; facial, speech, and vision recognition; and speech and language understanding - into their applications. The goal of Azure Cognitive Services is to help developers create applications that can see, hear, speak, understand, and even begin to reason. The catalog of services within Azure Cognitive Services can be categorized into five main pillars - Vision, Speech, Language, Search, and Knowledge.
Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/welcome


NEW QUESTION # 255
......

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