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Quiz 2023 Microsoft DP-100: Professional Designing and Implementing a Data Science Solution on Azure Valid Test Book
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NEW QUESTION 33
You have an Azure Machine Learning workspace that contains a CPU-based compute cluster and an Azure Kubernetes Service (AKS) inference cluster. You create a tabular dataset containing data that you plan to use to create a classification model.
You need to use the Azure Machine Learning designer to create a web service through which client applications can consume the classification model by submitting new data and getting an immediate prediction as a response.
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 – Deploy a real-time endpoint on the inference cluster.
2 – Create and run a batch inference pipeline on the compute cluster.
3 – Create and run a real-time inference pipeline on the compute cluster.
4 – Use the automated ML user interface to train a classification model on the compute cluster.
NEW QUESTION 34
You are building a binary classification model by using a supplied training set.
The training set is imbalanced between two classes.
You need to resolve the data imbalance.
What are three possible ways to achieve this goal? Each correct answer presents a complete solution NOTE:
Each correct selection is worth one point.
- A. Use accuracy as the evaluation metric of the model.
- B. Resample the data set using under sampling or oversampling
- C. Normalize the training feature set.
- D. Generate synthetic samples in the minority class.
- E. Penalize the classification
Answer: A,B,E
Explanation:
Explanation
References:
https://machinelearningmastery.com/tactics-to-combat-imbalanced-classes-in-your-machine-learning-dataset/
NEW QUESTION 35
Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.
After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.
You have a Python script named train.py in a local folder named scripts. The script trains a regression model by using scikit-learn. The script includes code to load a training data file which is also located in the scripts folder.
You must run the script as an Azure ML experiment on a compute cluster named aml-compute.
You need to configure the run to ensure that the environment includes the required packages for model training. You have instantiated a variable named aml-compute that references the target compute cluster.
Solution: Run the following code:
Does the solution meet the goal?
- A. Yes
- B. No
Answer: B
Explanation:
The scikit-learn estimator provides a simple way of launching a scikit-learn training job on a compute target. It is implemented through the SKLearn class, which can be used to support single-node CPU training.
Example:
from azureml.train.sklearn import SKLearn
}
estimator = SKLearn(source_directory=project_folder,
compute_target=compute_target,
entry_script=’train_iris.py’
)
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-train-scikit-learn
NEW QUESTION 36
……
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