DP-100 Latest Braindumps Ppt, Online DP-100 Training Materials

DOWNLOAD the newest ITExamDownload DP-100 PDF dumps from Cloud Storage for free: https://drive.google.com/open?id=1KLxg2YbR9dvWA7NZlKWOeOa-AOu-OFVm

As the saying goes, practice makes perfect. We are now engaged in the pursuit of Craftsman spirit in all walks of life. Professional and mature talents are needed in each field, similarly, only high-quality and high-precision DP-100 practice materials can enable learners to be confident to take the qualification examination so that they can get the certificate successfully, and our DP-100 Learning Materials are such high-quality learning materials, it can meet the user to learn the most popular test site knowledge.

Microsoft DP-100 Exam Syllabus Topics:

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

>> DP-100 Latest Braindumps Ppt <<

100% Free DP-100 – 100% Free Latest Braindumps Ppt | Updated Online Designing and Implementing a Data Science Solution on Azure Training Materials

Work hard and practice with our Microsoft DP-100 dumps till you are confident to pass the Microsoft DP-100 exam. And that too with flying colors and achieving the Designing and Implementing a Data Science Solution on Azure certification on the first attempt. You will identify both your strengths and shortcomings when you utilize Microsoft DP-100 Practice Exam software.

Microsoft Designing and Implementing a Data Science Solution on Azure Sample Questions (Q94-Q99):

NEW QUESTION # 94
You create an experiment in Azure Machine Learning Studio. You add a training dataset that contains 10,000 rows. The first 9,000 rows represent class 0 (90 percent).
The remaining 1,000 rows represent class 1 (10 percent).
The training set is imbalances between two classes. You must increase the number of training examples for class 1 to 4,000 by using 5 data rows. You add the Synthetic Minority Oversampling Technique (SMOTE) module to the experiment.
You need to configure the module.
Which values should you use? To answer, select the appropriate options in the dialog box in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation

Box 1: 300
You type 300 (%), the module triples the percentage of minority cases (3000) compared to the original dataset (1000).
Box 2: 5
We should use 5 data rows.
Use the Number of nearest neighbors option to determine the size of the feature space that the SMOTE algorithm uses when in building new cases. A nearest neighbor is a row of data (a case) that is very similar to some target case. The distance between any two cases is measured by combining the weighted vectors of all features.
By increasing the number of nearest neighbors, you get features from more cases.
By keeping the number of nearest neighbors low, you use features that are more like those in the original sample.
References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/smote


NEW QUESTION # 95
Hotspot Question
You train a model by using Azure Machine Learning. You use Azure Blob Storage to store production data.
The model must be re-trained when new data is uploaded to Azure Blob Storage. You need to minimize development and coding.
You need to configure Azure services to develop a re-training solution.
Which Azure services should you use? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation:
https://learn.microsoft.com/en-us/azure/data-explorer/create-event-grid-connection?tabs=portal- adx%2Cportal-2
https://learn.microsoft.com/en-us/azure/machine-learning/v1/how-to-trigger-published- pipeline?view=azureml-api-1


NEW QUESTION # 96
You use an Azure Machine Learning workspace.
You have a trained model that must be deployed as a web service. Users must authenticate by using Azure Active Directory.
What should you do?

Answer: C

Explanation:
To control token authentication, use the token_auth_enabled parameter when you create or update a deployment Token authentication is disabled by default when you deploy to Azure Kubernetes Service.
Note: The model deployments created by Azure Machine Learning can be configured to use one of two authentication methods:
key-based: A static key is used to authenticate to the web service.
token-based: A temporary token must be obtained from the Azure Machine Learning workspace (using Azure Active Directory) and used to authenticate to the web service.
Incorrect Answers:
C: Token authentication isn't supported when you deploy to Azure Container Instances.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-authenticate-web-service


NEW QUESTION # 97
You train a model by using Azure Machine Learning. You use Azure Blob Storage to store production data.
The model must be re-trained when new data is uploaded to Azure Blob Storage. You need to minimize development and coding.
You need to configure Azure services to develop a re-training solution.
Which Azure services 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 # 98
You have several machine learning models registered in an Azure Machine Learning workspace.
You must use the Fairlearn dashboard to assess fairness in a selected model.
Which three actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.

Answer:

Explanation:

Explanation:

Step 1: Select a model feature to be evaluated.
Step 2: Select a binary classification or regression model.
Register your models within Azure Machine Learning. For convenience, store the results in a dictionary, which maps the id of the registered model (a string in name:version format) to the predictor itself.
Example:
model_dict = {}
lr_reg_id = register_model("fairness_logistic_regression", lr_predictor) model_dict[lr_reg_id] = lr_predictor svm_reg_id = register_model("fairness_svm", svm_predictor) model_dict[svm_reg_id] = svm_predictor Step 3: Select a metric to be measured Precompute fairness metrics.
Create a dashboard dictionary using Fairlearn's metrics package.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-machine-learning-fairness-aml


NEW QUESTION # 99
......

We have three different versions of Designing and Implementing a Data Science Solution on Azure prep torrent for you to choose, including PDF version, PC version and APP online version. Different versions have their own advantages and user population, and we would like to introduce features of these versions for you. There is no doubt that PDF of DP-100 exam torrent is the most prevalent version among youngsters, mainly due to its convenience for a demo, through which you can have a general understanding and simulation about our DP-100 Test Braindumps to decide whether you are willing to purchase or not, and also convenience for paper printing for you to do some note-taking.

Online DP-100 Training Materials: https://www.itexamdownload.com/DP-100-valid-questions.html

P.S. Free & New DP-100 dumps are available on Google Drive shared by ITExamDownload: https://drive.google.com/open?id=1KLxg2YbR9dvWA7NZlKWOeOa-AOu-OFVm