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Microsoft DP-100 Exam Syllabus Topics:

SectionObjectives
Topic 1: Design and prepare a machine learning solution- Select appropriate Azure services for machine learning workloads
- Manage compute and data assets
- Plan and configure Azure Machine Learning workspace
Topic 2: Explore and analyze data- Ingest and prepare data for modeling
- Perform exploratory data analysis
Topic 3: Optimize and manage models- Improve model performance
- Track experiments and manage model lifecycle
Topic 4: Deploy and consume models- Monitor deployed models and endpoints
- Deploy models to endpoints
Topic 5: Train machine learning models- Tune hyperparameters and evaluate models
- Train models using Azure Machine Learning

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High-quality Sample DP-100 Questions Answers | Reliable Valid DP-100 Test Pdf: Designing and Implementing a Data Science Solution on Azure

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Microsoft Designing and Implementing a Data Science Solution on Azure Sample Questions (Q151-Q156):

NEW QUESTION # 151
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:

Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-machine-learning-fairness-aml


NEW QUESTION # 152
You plan to use Hyperdrive to optimize the hyperparameters selected when training a model. You create the following code to define options for the hyperparameter experiment


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

Answer:

Explanation:


NEW QUESTION # 153
You manage an Azure Al Foundry project. You plan to create a vector index for a RAG solution. You need to build the index remotely by using a script.
Which two data sources can you use? Each correct answer presents a complete solution. Choose two. NOTE: Each correct selection is worth one point.

Answer: A,B


NEW QUESTION # 154
You create a datastore named training_data that references a blob container in an Azure Storage account. The blob container contains a folder named csv_files in which multiple comma-separated values (CSV) files are stored.
You have a script named train.py in a local folder named ./script that you plan to run as an experiment using an estimator. The script includes the following code to read data from the csv_files folder:

You have the following script.

You need to configure the estimator for the experiment so that the script can read the data from a data reference named data_ref that references the csv_files folder in the training_data datastore.
Which code should you use to configure the estimator?

Answer: E

Explanation:
Besides passing the dataset through the inputs parameter in the estimator, you can also pass the dataset through script_params and get the data path (mounting point) in your training script via arguments. This way, you can keep your training script independent of azureml-sdk. In other words, you will be able use the same training script for local debugging and remote training on any cloud platform.
Example:
from azureml.train.sklearn import SKLearn
script_params = {
# mount the dataset on the remote compute and pass the mounted path as an argument to the training script
'--data-folder': mnist_ds.as_named_input('mnist').as_mount(),
'--regularization': 0.5
}
est = SKLearn(source_directory=script_folder,
script_params=script_params,
compute_target=compute_target,
environment_definition=env,
entry_script='train_mnist.py')
# Run the experiment
run = experiment.submit(est)
run.wait_for_completion(show_output=True)
Incorrect Answers:
A: Pandas DataFrame not used.
Reference:
https://docs.microsoft.com/es-es/azure/machine-learning/how-to-train-with-datasets


NEW QUESTION # 155
You are analyzing the asymmetry in a statistical distribution.
The following image contains two density curves that show the probability distribution of two datasets.

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

Answer:

Explanation:

Explanation

Box 1: Positive skew
Positive skew values means the distribution is skewed to the right.
Box 2: Negative skew
Negative skewness values mean the distribution is skewed to the left.
References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/compute-elementary-statistics


NEW QUESTION # 156
......

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