Updated Designing and Implementing a Data Science Solution on Azure Questions Cram - DP-100 Pdf Review & Designing and Implementing a Data Science Solution on Azure Examboost Vce

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Microsoft DP-100 Exam Syllabus Topics:

TopicDetails
Topic 1
  • Train and deploy models: In this topic, Microsoft Data Scientists develop the expertise to execute training scripts and implement efficient training pipelines. Participants learn to manage models effectively and deploy them using Azure services. The focus is on assessing proficiency in training processes, deployment strategies, and lifecycle management of machine learning models to ensure production-ready solutions that align with organizational goals.
Topic 2
  • Design and prepare a machine learning solution: This topic equips Microsoft Data Scientists with skills to design comprehensive machine learning solutions tailored to business needs. It delves into managing resources and assets in the Azure Machine Learning workspace, offering insights into organizing computational and storage resources effectively.
Topic 3
  • Explore data, and run experiments: This section focuses on enabling Microsoft Data Scientists to explore datasets and experiment with model configurations effectively. By leveraging automated machine learning, participants explore optimal models and streamline custom model training with notebooks. It evaluates skills in automating hyperparameter tuning to refine models for improved performance.
Topic 4
  • Optimize language models for AI applications: This advanced topic teaches Microsoft Data Scientists to enhance AI applications by optimizing language models. Skills are developed in preparing models for optimization, prompt engineering, and employing techniques like Prompt Flow and Retrieval Augmented Generation (RAG). Additionally, fine-tuning strategies are explored to maximize model performance.

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Microsoft Designing and Implementing a Data Science Solution on Azure Sample Questions (Q265-Q270):

NEW QUESTION # 265
You run an automated machine learning experiment in an Azure Machine Learning workspace. Information about the run is listed in the table below:

You need to write a script that uses the Azure Machine Learning SDK to retrieve the best iteration of the experiment run.
Which Python code segment should you use?

Answer: A

Explanation:
Explanation
Explanation:
The get_output method on automl_classifier returns the best run and the fitted model for the last invocation.
Overloads on get_output allow you to retrieve the best run and fitted model for any logged metric or for a particular iteration.
In [ ]:
best_run, fitted_model = local_run.get_output()
Reference:
https://notebooks.azure.com/azureml/projects/azureml-getting-started/html/how-to-use-azureml/automated- machine-learning/classification-with-deployment/auto-ml-classification-with-deployment.ipynb


NEW QUESTION # 266
You need to configure the Edit Metadata module so that the structure of the datasets match.
Which configuration options should you select? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation

Box 1: Floating point
Need floating point for Median values.
Scenario: An initial investigation shows that the datasets are identical in structure apart from the MedianValue column. The smaller Paris dataset contains the MedianValue in text format, whereas the larger London dataset contains the MedianValue in numerical format.
Box 2: Unchanged
Note: Select the Categorical option to specify that the values in the selected columns should be treated as categories.
For example, you might have a column that contains the numbers 0,1 and 2, but know that the numbers actually mean "Smoker", "Non smoker" and "Unknown". In that case, by flagging the column as categorical you can ensure that the values are not used in numeric calculations, only to group data.


NEW QUESTION # 267
You use the Azure Machine Learning SDK to run a training experiment that trains a classification model and calculates its accuracy metric.
The model will be retrained each month as new data is available.
You must register the model for use in a batch inference pipeline.
You need to register the model and ensure that the models created by subsequent retraining experiments are registered only if their accuracy is higher than the currently registered model.
What are two possible ways to achieve this goal? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.

Answer: C,E

Explanation:
E: Using tags, you can track useful information such as the name and version of the machine learning library used to train the model. Note that tags must be alphanumeric.
Reference:
https://notebooks.azure.com/xavierheriat/projects/azureml-getting-started/html/how-to-use-azureml/ deployment/register-model-create-image-deploy-service/register-model-create-image-deploy-service.ipynb


NEW QUESTION # 268
Drag and Drop Question
You are designing an Azure Machine Learning solution.
The model must be trained by using automated machine learning. The compute must be a shared resource with users in the Azure Machine Learning workspace. After you train the model, it must be deployed for batch scoring on a serverless compute.
You need to select the appropriate computation options for the solution.
Which compute options should you select for training and deployment? To answer, move the appropriate compute options to the correct project activities. You may use each compute option once, more than once, or not at all. You may need to move the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:


NEW QUESTION # 269
You are developing a machine learning solution by using the Azure Machine Learning designer.
You need to create a web service that applications can use to submit data feature values and retrieve a predicted label.
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:

1 - Create and run a training pipeline.
2 - Deploy a service to an inference cluster.
3 - Create and run a real-time inference pipeline.


NEW QUESTION # 270
......

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