ユニーク-正確的なDP-100関連復習問題集試験-試験の準備方法DP-100全真模擬試験

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Microsoft DP-100 Exam Syllabus Topics:

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

>> DP-100関連復習問題集 <<

試験の準備方法-効率的なDP-100関連復習問題集試験-実用的なDP-100全真模擬試験

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Microsoft Designing and Implementing a Data Science Solution on Azure 認定 DP-100 試験問題 (Q36-Q41):

質問 # 36
A coworker registers a datastore in a Machine Learning services workspace by using the following code:

You need to write code to access the datastore from a notebook.

正解：

解説：

Explanation:

Box 1: DataStore
To get a specific datastore registered in the current workspace, use the get() static method on the Datastore class:
# Get a named datastore from the current workspace
datastore = Datastore.get(ws, datastore_name='your datastore name')
Box 2: ws
Box 3: demo_datastore
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-access-data


質問 # 37
You create an Azure Machine Learning workspace. The workspace contains a dataset named sample.dataset, a compute instance, and a compute cluster. You must create a two-stage pipeline that will prepare data in the dataset and then train and register a model based on the prepared data. The first stage of the pipeline contains the following code:

You need to identify the location containing the output of the first stage of the script that you can use as input for the second stage. Which storage location should you use?

正解：B


質問 # 38
Drag and Drop Question
An organization uses Azure Machine Learning service and wants to expand their use of machine learning.
You have the following compute environments. The organization does not want to create another compute environment.

You need to determine which compute environment to use for the following scenarios.
Which compute types should you use? To answer, drag the appropriate compute environments to the correct scenarios. Each compute environment 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.

正解：

解説：

Explanation:
Box 1: nb_server

Box 2: mlc_cluster
With Azure Machine Learning, you can train your model on a variety of resources or environments, collectively referred to as compute targets. A compute target can be a local machine or a cloud resource, such as an Azure Machine Learning Compute, Azure HDInsight or a remote virtual machine.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/concept-compute-target
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-set-up-training-targets


質問 # 39
You manage an Azure Machine Learning workspace named workspace1 by using the Python SDK v2.
The default datastore of workspace1 contains a folder named sample_data. The folder structure contains the following content:

You write Python SDK v2 code to materialize the data from the files in the sample.data folder into a Pandas data frame. You need to complete the Python SDK v2 code to use the MLTaWe folder as the materialization blueprint. How should you complete the code? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

正解：

解説：

Explanation


質問 # 40
You are implementing hyperparameter tuning for a model training from a notebook. The notebook is in an Azure Machine Learning workspace. You add code that imports all relevant Python libraries.
You must configure Bayesian sampling over the search space for the num_hidden_layers and batch_size hyperparameters.
You need to complete the following Python code to configure Bayesian sampling.
Which code segments should you use? To answer, select the appropriate options in the answer area NOTE: Each correct selection is worth one point.

正解：

解説：


質問 # 41
......

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