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100% Pass Microsoft - Newest DP-100 - Designing and Implementing a Data Science Solution on Azure Valid Dumps Pdf

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Microsoft DP-100: Exam Details

Microsoft doesn’t provide detailed information regarding its exams, including the DP-100 certification test. The total number of questions in exam is not published on the official website, but the candidates can expect to encounter about 40-60 items in their delivery of their test. There is also a high probability that they will face scenario-based questions with single-answer and multiple-answer options. The duration is all about 120 minutes. The exam is available in several languages, including Japanese, English, Simplified Chinese, and Korean.

The Microsoft DP-100 exam can be taken at any nearest Pearson VUE testing center or online as a proctored option. The cost for the exam is $165 in the United States but the price is usually different in other countries due to taxation. To find out the actual pricing for your country, simply check the official webpage.

Microsoft DP-100 Exam is a certification exam that focuses on designing and implementing data science solutions on Azure. DP-100 exam is designed for data professionals who want to demonstrate their skills in implementing machine learning models, processing and transforming data, and designing and implementing data science workflows. DP-100 exam is part of the Microsoft Certified: Azure Data Scientist Associate certification, which validates the skills required to design and implement AI solutions that leverage Microsoft Azure services.

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Microsoft Designing and Implementing a Data Science Solution on Azure Sample Questions (Q153-Q158):

NEW QUESTION # 153
You are hired as a data scientist at a winery. The previous data scientist used Azure Machine Learning.
You need to review the models and explain how each model makes decisions.
Which explainer modules should you use? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Reference:
https://medium.com/microsoftazure/automated-and-interpretable-machine-learning-d07975741298

NEW QUESTION # 154
You create a deep learning model for image recognition on Azure Machine Learning service using GPU- based training.
You must deploy the model to a context that allows for real-time GPU-based inferencing.
You need to configure compute resources for model inferencing.
Which compute type should you use?

A. Field Programmable Gate Array
B. Machine Learning Compute
C. Azure Container Instance
D. Azure Kubernetes Service

Answer: D

Explanation:
You can use Azure Machine Learning to deploy a GPU-enabled model as a web service. Deploying a model on Azure Kubernetes Service (AKS) is one option. The AKS cluster provides a GPU resource that is used by the model for inference.
Inference, or model scoring, is the phase where the deployed model is used to make predictions. Using GPUs instead of CPUs offers performance advantages on highly parallelizable computation.
ence:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-deploy-inferencing-gpus

NEW QUESTION # 155
You are a data scientist building a deep convolutional neural network (CNN) for image classification.
The CNN model you build shows signs of overfitting.
You need to reduce overfitting and converge the model to an optimal fit.
Which two actions should you perform? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.

A. Add L1/L2 regularization.
B. Reduce the amount of training data.
C. Add an additional dense layer with 64 input units.
D. Use training data augmentation.
E. Add an additional dense layer with 512 input units.

Answer: A,B

Explanation:
B: Weight regularization provides an approach to reduce the overfitting of a deep learning neural network model on the training data and improve the performance of the model on new data, such as the holdout test set.
Keras provides a weight regularization API that allows you to add a penalty for weight size to the loss function.
Three different regularizer instances are provided; they are:
* L1: Sum of the absolute weights.
* L2: Sum of the squared weights.
* L1L2: Sum of the absolute and the squared weights.
D: Because a fully connected layer occupies most of the parameters, it is prone to overfitting. One method to reduce overfitting is dropout. At each training stage, individual nodes are either "dropped out" of the net with probability 1-p or kept with probability p, so that a reduced network is left; incoming and outgoing edges to a dropped-out node are also removed.
By avoiding training all nodes on all training data, dropout decreases overfitting.
References:
https://machinelearningmastery.com/how-to-reduce-overfitting-in-deep-learning-with-weight-regularization/
https://en.wikipedia.org/wiki/Convolutional_neural_network

NEW QUESTION # 156
You have a model with a large difference between the training and validation error values.
You must create a new model and perform cross-validation.
You need to identify a parameter set for the new model using Azure Machine Learning Studio.
Which module you should use for each step? To answer, drag the appropriate modules to the correct steps. Each module may be used once or more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/partition-and-sample

NEW QUESTION # 157
You need to implement early stopping criteria as suited in the model training requirements.
Which three code segments should you use to develop the solution? To answer, move the appropriate code segments from the list of code segments to the answer area and arrange them in the correct order.
NOTE: More than one order of answer choices is correct. You will receive credit for any of the correct orders you select.

Answer:

Explanation:

Explanation:
You need to implement an early stopping criterion on models that provides savings without terminating promising jobs.
Truncation selection cancels a given percentage of lowest performing runs at each evaluation interval. Runs are compared based on their performance on the primary metric and the lowest X% are terminated.
Example:
from azureml.train.hyperdrive import TruncationSelectionPolicy
early_termination_policy = TruncationSelectionPolicy(evaluation_interval=1, truncation_percentage=20, delay_evaluation=5) Incorrect Answers:
Bandit is a termination policy based on slack factor/slack amount and evaluation interval. The policy early terminates any runs where the primary metric is not within the specified slack factor / slack amount with respect to the best performing training run.
Example:
from azureml.train.hyperdrive import BanditPolicy
early_termination_policy = BanditPolicy(slack_factor = 0.1, evaluation_interval=1, delay_evaluation=5 References:
https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-tune-hyperparameters

NEW QUESTION # 158
......

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