Most Recent Microsoft DP-100 Exam Dumps

Prepare for the Microsoft Designing and Implementing a Data Science Solution on Azure exam with our extensive collection of questions and answers. These practice Q&A are updated according to the latest syllabus, providing you with the tools needed to review and test your knowledge.

QA4Exam focus on the latest syllabus and exam objectives, our practice Q&A are designed to help you identify key topics and solidify your understanding. By focusing on the core curriculum, These Questions & Answers helps you cover all the essential topics, ensuring you're well-prepared for every section of the exam. Each question comes with a detailed explanation, offering valuable insights and helping you to learn from your mistakes. Whether you're looking to assess your progress or dive deeper into complex topics, our updated Q&A will provide the support you need to confidently approach the Microsoft DP-100 exam and achieve success.

The questions for DP-100 were last updated on Mar 28, 2025.

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Question No. 1

You manage an Azure Machine Learning workspace.

You must provide explanations for the behavior of the models with feature importance measures.

You need to configure a Responsible Al dashboard in Azure Machine Learning.

Which dashboard component should you configure?

AFairness assessment

BCounterfactual what-if

CInterpretability

DCasual inference

Show Answer

Correct Answer: C

Question No. 2

You need to resolve the local machine learning pipeline performance issue. What should you do?

AIncrease Graphic Processing Units (GPUs).

BIncrease the learning rate.

CIncrease the training iterations,

DIncrease Central Processing Units (CPUs).

Show Answer

Correct Answer: A

Question No. 3

You are a data scientist building a deep convolutional neural network (CNN) for image classification.

The CNN model you built 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.

AReduce the amount of training data.

BAdd an additional dense layer with 64 input units

CAdd L1/L2 regularization.

DUse training data augmentation

EAdd an additional dense layer with 512 input units.

Show Answer

Correct Answer: A, C

https://machinelearningmastery.com/how-to-reduce-overfitting-in-deep-learning-with-weight-regularization/

https://en.wikipedia.org/wiki/Convolutional_neural_network

Question No. 4

Your team is building a data engineering and data science development environment.

The environment must support the following requirements:

support Python and Scala

compose data storage, movement, and processing services into automated data pipelines

the same tool should be used for the orchestration of both data engineering and data science

support workload isolation and interactive workloads

enable scaling across a cluster of machines

You need to create the environment.

What should you do?

ABuild the environment in Apache Hive for HDInsight and use Azure Data Factory for orchestration.

BBuild the environment in Azure Databricks and use Azure Data Factory for orchestration.

CBuild the environment in Apache Spark for HDInsight and use Azure Container Instances for orchestration.

DBuild the environment in Azure Databricks and use Azure Container Instances for orchestration.

Show Answer

Correct Answer: B

In Azure Databricks, we can create two different types of clusters.

Standard, these are the default clusters and can be used with Python, R, Scala and SQL

High-concurrency

Azure Databricks is fully integrated with Azure Data Factory.

Incorrect Answers:

D: Azure Container Instances is good for development or testing. Not suitable for production workloads.

https://docs.microsoft.com/en-us/azure/architecture/data-guide/technology-choices/data-science-and-machinelearning

Question No. 5

You plan to run a script as an experiment using a Script Run Configuration. The script uses modules from the scipy library as well as 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 clusters 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?

ACreate and register an Environment that includes the required packages. Use this Environment for all experiment runs.

BAlways run the experiment with an Estimator by using the default packages.

CDo not specify an environment in the run configuration for the experiment. Run the experiment by using the default environment.

DCreate a config. yaml file defining the conda packages that are required and save the file in the experiment folder.

ECreate a virtual machine (VM) with the required Python configuration and attach the VM as a compute target. Use this compute target for all experiment runs.

Show Answer

Correct Answer: A

If you have an existing Conda environment on your local computer, then you can use the service to create an environment object. By using this strategy, you can reuse your local interactive environment on remote runs.

https://docs.microsoft.com/en-us/azure/machine-learning/how-to-use-environments

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