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DP-100 Designing and Implementing a Data Science Solution on Azure

  • Microsoft
  • 4 free sample questions

About this exam

DP-100 is the Microsoft exam Designing and Implementing a Data Science Solution on Azure. Paid practice material for this exam is not available yet, but you can try its free sample questions.

Exam code
DP-100
Vendor
Microsoft
Practice questions
Not available yet
Free sample questions
4
Question bank last updated
Not available yet

Try 2 sample questions

Taken from the free sample set for DP-100.

Question 1

You need to select an environment that will meet the business and data requirements. Which environmentshould you use?

  1. Option A:
    Azure HDInsight with Spark MLlib
  2. Option B:
    Azure Cognitive Services
  3. Option C:
    Azure Machine Learning Studio
  4. Option D:
    Microsoft Machine Learning Server
Show answer and explanation

Correct answer: D

Microsoft Machine Learning Server is the best choice when an organisation needs to build and deploy machine learning solutions while meeting strict business and data requirements. It can run on-premises and process data where it resides, which is important for organisations that cannot move sensitive or large volumes of data to the cloud due to compliance, security, or regulatory requirements. It supports scalable analytics using R and Python and can integrate with existing enterprise systems. In contrast, Azure Cognitive Services provides prebuilt AI APIs, Azure Machine Learning Studio is primarily a cloud-based machine learning development environment, and Azure HDInsight with Spark MLlib focuses on big data processing rather than meeting enterprise data residency and on-premises requirements. Therefore, Microsoft Machine Learning Server is the most appropriate solution.

Question 2

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

  1. Option A:
    Increase Graphic Processing Units (GPUs).
  2. Option B:
    Increase the learning rate.
  3. Option C:
    Increase the training iterations,
  4. Option D:
    Increase Central Processing Units (CPUs).
Show answer and explanation

Correct answer: A

If the local machine learning pipeline is experiencing performance issues, increasing the number of GPUs is the most effective solution. GPUs are specifically designed to perform the large-scale parallel computations required for machine learning and deep learning workloads. They can process thousands of operations simultaneously, significantly reducing model training and inference times. In contrast, increasing the learning rate or training iterations affects the model's learning process rather than performance, and adding CPUs generally provides less improvement for machine learning workloads than GPUs. Therefore, increasing GPUs is the best way to resolve the pipeline performance issue.
See all 4 free sample questions