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Microsoft DP-100 exam covers a range of topics related to data science, including understanding business requirements, data exploration and preparation, modeling, training and evaluation of machine learning models, and deployment and management of models. DP-100 exam also covers topics such as feature engineering, model interpretability, and automation of machine learning workflows. DP-100 exam is designed to test the candidate's ability to design and implement end-to-end machine learning solutions that meet business requirements and are scalable, reliable, and secure.

Microsoft DP-100 Exam is a certification exam designed for individuals who are interested in developing their skills in data science and machine learning, particularly in the context of Microsoft Azure. DP-100 exam is aimed at individuals who are responsible for designing and implementing data solutions using Azure technologies, and who have a solid understanding of data science concepts, data preparation, and data visualization.

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Microsoft Designing and Implementing a Data Science Solution on Azure Sample Questions (Q43-Q48):

NEW QUESTION # 43
You need to produce a visualization for the diagnostic test evaluation according to the data visualization requirements.
Which three modules should you recommend be used in sequence? To answer, move the appropriate modules from the list of modules to the answer area and arrange them in the correct order.

Answer:

Explanation:

1 - Sweep Clustering
2 - Train Model
3 - Evaluate Model
Reference:
http://breaking-bi.blogspot.com/2017/01/azure-machine-learning-model-evaluation.html

NEW QUESTION # 44
You are using a ScriptRunConfig object to configure an experiment that uses a script to train a machine learning model.
The script must apply a regularization rate hyperparameter to the algorithm that is used to train the model.
You need to pass the regularization rate in a variable named reg_rate to the script.
Which code segment should you use?

Answer: A

NEW QUESTION # 45
You plan to run a script as an experiment. The script uses modules from the SciPy library and several Python packages that are not typically installed in a default conda environment.
You plan to run the experiment on your local workstation for small datasets and scale out the experiment by running it on more powerful remote compute dusters for larger datasets.
You need to ensure that the experiment runs successfully on local and remote compute with the least administrative effort.
What should you do?

A. Create and register an environment that includes the required packages. Use this environment for all experiment jobs.
B. Create a config.yaml file that defines the required conda packages and save the file in the experiment folder.
C. Create a virtual machine (VM) by using the required Python configuration and attach the VM as a compute target. Use this compute target for all experiment runs.
D. Leave the environment unspecified for the experiment. Run the expenment by using the default environment.

Answer: A

NEW QUESTION # 46
You are using The Azure Machine Learning designer to transform a dataset containing the census data of all nations.
You must use the Split Data component to separate the dataset into two datasets. The first dataset must contain the census data of the United States. The second dataset must include the census data of the remaining nations.
You need to configure the component to create the datasets.
Which configuration values 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 # 47
space and set up a development environment. You plan to train a deep neural network (DNN) by using the Tensorflow framework and by using estimators to submit training scripts.
You must optimize computation speed for training runs.
You need to choose the appropriate estimator to use as well as the appropriate training compute target configuration.
Which values should you use? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation

Box 1: Tensorflow
TensorFlow represents an estimator for training in TensorFlow experiments.
Box 2: 12 vCPU, 112 GB memory..,2 GPU,..
Use GPUs for the deep neural network.
Reference:
https://docs.microsoft.com/en-us/python/api/azureml-train-core/azureml.train.dnn

NEW QUESTION # 48
......

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