DP-100인증시험공부자료, DP-100최고덤프데모

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Microsoft DP-100 Exam Syllabus Topics:

Section	Weight	Objectives
Explore data and run experiments	20-25%	- Implement pipelines 1. Build reusable components 2. Pass data between steps 3. Schedule and monitor pipelines 4. Create and publish pipelines - Explore and visualize data 1. Profile and validate data 2. Identify features and relationships 3. Detect anomalies and outliers - Run experiments 1. Define parameters and configurations 2. Use automated machine learning 3. Configure experiment runs 4. Track runs with MLflow
Design and prepare a machine learning solution	20-25%	- Manage data assets 1. Select storage services 2. Register and manage datastores 3. Create and maintain data assets - Manage compute resources 1. Select environments 2. Attach and monitor compute 3. Create and configure compute targets - Manage Azure Machine Learning workspace 1. Use developer tools and CLI 2. Work with registries 3. Set up Git integration 4. Create and configure workspace - Design a machine learning solution 1. Plan model deployment requirements 2. Select development approach 3. Define compute specifications for workloads 4. Determine dataset structure and format
Train and deploy models	25-30%	- Train models 1. Apply responsible AI principles 2. Run training scripts 3. Configure jobs and environments 4. Use HyperDrive for hyperparameter tuning - Monitor and maintain models 1. Monitor performance and data drift 2. Update and retrain models 3. Implement MLOps practices - Deploy models 1. Secure endpoints and manage access 2. Configure compute and scaling 3. Deploy to batch endpoints 4. Deploy to online endpoints - Manage models 1. Interpret models and explain predictions 2. Package and validate models 3. Register and version models
Optimize language models for AI applications	25-30%	- Evaluate and improve models 1. Apply responsible generative AI 2. Test and evaluate responses 3. Optimize for accuracy and safety - Optimize with Retrieval Augmented Generation 1. Configure Azure AI Search 2. Prepare and process data 3. Create vector stores and indexes - Implement generative AI solutions 1. Use Azure AI Foundry 2. Apply prompt engineering 3. Build prompt flows

>> DP-100인증시험 공부자료 <<

DP-100인증시험 공부자료 완벽한 덤프

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최신 Microsoft Azure DP-100 무료샘플문제 (Q308-Q313):

질문 # 308
You run an experiment that uses an AutoMLConfig class to define an automated machine learning task with a maximum of ten model training iterations. The task will attempt to find the best performing model based on a metric named accuracy.
You submit the experiment with the following code:
You need to create Python code that returns the best model that is generated by the automated machine learning task. Which code segment should you use?
A)

B)

C)

D)

A. Option C
B. Option B
C. Option A
D. Option D

정답：B

설명：
The get_output method returns the best run and the fitted model.
Reference:
https://notebooks.azure.com/azureml/projects/azureml-getting-started/html/how-to-use-azureml/automated-machine-learning/classification/auto-ml-classification.ipynb

질문 # 309
You are building an experiment using the Azure Machine Learning designer.
You split a dataset into training and testing sets. You select the Two-Class Boosted Decision Tree as the algorithm.
You need to determine the Area Under the Curve (AUC) of the model.
Which three modules should you use in sequence? To answer, move the appropriate modules from the list of modules to the answer area and arrange them in the correct order.

정답：

설명：

Explanation

Step 1: Train Model
Two-Class Boosted Decision Tree
First, set up the boosted decision tree model.
1. Find the Two-Class Boosted Decision Tree module in the module palette and drag it onto the canvas.
2. Find the Train Model module, drag it onto the canvas, and then connect the output of the Two-Class Boosted Decision Tree module to the left input port of the Train Model module.
The Two-Class Boosted Decision Tree module initializes the generic model, and Train Model uses training data to train the model.
3. Connect the left output of the left Execute R Script module to the right input port of the Train Model module (in this tutorial you used the data coming from the left side of the Split Data module for training).
This portion of the experiment now looks something like this:

Step 2: Score Model
Score and evaluate the models
You use the testing data that was separated out by the Split Data module to score our trained models. You can then compare the results of the two models to see which generated better results.
Add the Score Model modules
1. Find the Score Model module and drag it onto the canvas.
2. Connect the Train Model module that's connected to the Two-Class Boosted Decision Tree module to the left input port of the Score Model module.
3. Connect the right Execute R Script module (our testing data) to the right input port of the Score Model module.

Step 3: Evaluate Model
To evaluate the two scoring results and compare them, you use an Evaluate Model module.
1. Find the Evaluate Model module and drag it onto the canvas.
2. Connect the output port of the Score Model module associated with the boosted decision tree model to the left input port of the Evaluate Model module.
3. Connect the other Score Model module to the right input port.

질문 # 310
You have a Python data frame named salesData in the following format:

The data frame must be unpivoted to a long data format as follows:

You need to use the pandas.melt() function in Python to perform the transformation.
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.

정답：

설명：

Explanation:

Box 1: dataFrame
Syntax: pandas.melt(frame, id_vars=None, value_vars=None, var_name=None, value_name='value', col_level=None)[source] Where frame is a DataFrame Box 2: shop Paramter id_vars id_vars : tuple, list, or ndarray, optional Column(s) to use as identifier variables.
Box 3: ['2017','2018']
value_vars : tuple, list, or ndarray, optional
Column(s) to unpivot. If not specified, uses all columns that are not set as id_vars.
Example:
df = pd.DataFrame({'A': {0: 'a', 1: 'b', 2: 'c'},
'B': {0: 1, 1: 3, 2: 5},
'C': {0: 2, 1: 4, 2: 6}})
pd.melt(df, id_vars=['A'], value_vars=['B', 'C'])
A variable value
0 a B 1
1 b B 3
2 c B 5
3 a C 2
4 b C 4
5 c C 6
References:
https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.melt.html

질문 # 311
You are developing a linear regression model in Azure Machine Learning Studio. You run an experiment to compare different algorithms.
The following image displays the results dataset output:

Use the drop-down menus to select the answer choice that answers each question based on the information presented in the image.
NOTE: Each correct selection is worth one point.

정답：

설명：

Explanation:
Box 1: Boosted Decision Tree Regression
Mean absolute error (MAE) measures how close the predictions are to the actual outcomes; thus, a lower score is better.
Box 2:
Online Gradient Descent: If you want the algorithm to find the best parameters for you, set Create trainer mode option to Parameter Range. You can then specify multiple values for the algorithm to try.
References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/evaluate-model
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/linear-regression

질문 # 312

For each of the following statements, select Yes if the statement is true. Otherwise, select No. NOTE: Each correct selection is worth one point.

정답：

설명：

질문 # 313
......

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