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Exam Overview
Exam Code DP-100
Exam Name Designing and Implementing a Data Science Solution on Azure
Vendor Microsoft
Total Questions 505
Last Updated 26-Oct, 2026
Free DP-100 Questions Get Premium Access
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Free DP-100 Practice Questions

Explore 4 free practice questions for the Microsoft DP-100 exam and review the answers and explanations to support your preparation.

  1. Question 1

    Drag & Drop

    DRAG DROP You need to define a process for penalty event detection. Which three actions should you perform in sequence? To answer, move the appropriate actions from the list of actionsto the answer area and arrange them in the correct order.

    Show Answer & Explanation Hide Answer & Explanation

    Correct Answer

    Actions (Available Options)
    Standardize to mono audio clips.
    Vary the length of sliding windows between modeling epochs.
    Vary the length of frequency bands between modeling epochs.
    Use an Inverse Fourier transform on frequency changes over time.
    Use a Fast Fourier transform on frequency changes over time.
    Standardize to stereo audio clips.
    Answer Area (Ordered Steps)
    Step SequenceAction to Perform
    Step 1Vary the length of frequency bands between modeling epochs.
    Step 2Standardize to mono audio clips.
    Step 3Use an Inverse Fourier transform on frequency changes over time.

    Explanation

    StepActionTechnical Significance
    Step 1Vary the length of frequency bands between modeling epochs.Adjusting or varying the configuration parameters of your frequency data distribution across epochs ensures robust data diversity and structural feature balancing before deeper processing phases.
    Step 2Standardize to mono audio clips.Standardizing multitrack or variable audio files into single-channel mono audio uniformizes inputs. This streamlines data dimensionality, prevents balance bias, and guarantees consistent array structures across a machine learning pipeline.
    Step 3Use an Inverse Fourier transform on frequency changes over time.Utilizing an Inverse Fourier transform converts processed spectrum components back into the time domain, mapping frequency-specific variations accurately across time for downstream analysis.

  2. Question 2

    Drag & Drop

    DRAG DROP You need to define a process for penalty event detection. Which three actions should you perform in sequence? To answer, move the appropriate actions from the list of actionsto the answer area and arrange them in the correct order. 

    Show Answer & Explanation Hide Answer & Explanation

    Correct Answer

    Actions (Available Options)
    Build the global model using Microsoft Cognitive Toolkit (CNTK).
    Import the global model and build a local model using Microsoft Cognitive Toolkit (CNTK).
    Export the global model using Neural Network Exchange Format (NNEF).
    Import the global model and build the local model using PyTorch.
    Build the global model using PyTorch.
    Build the global model using TensorFlow.
    Import the global model and build the local model using TensorFlow.
    Export the global model using the Open Neural Network Exchange (ONNX) format.
    Answer Area (Ordered Steps)
    Step SequenceAction to Perform
    Step 1Build the global model using PyTorch.
    Step 2Export the global model using Neural Network Exchange Format (NNEF).
    Step 3Import the global model and build the local model using TensorFlow.

    Explanation

    StepActionTechnical Significance
    Step 1Build the global model using PyTorch.The machine learning pipeline initiates by developing and training a foundational core representation (the global model) using a dynamic framework like PyTorch.
    Step 2Export the global model using Neural Network Exchange Format (NNEF).To port or hand off the model across heterogeneous environments, software layers, or edge frameworks, the global model structure is serialized into an open, cross-platform architecture such as NNEF.
    Step 3Import the global model and build the local model using TensorFlow.The standardized architecture is then pulled or imported into another production environment (TensorFlow) to adjust parameters locally or execute fine-tuning for deployment scenarios.

  3. Question 3

    Single answer

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

    • Option A:
      Azure HDInsight with Spark MLlib
      Correct
    • Option B:
      Azure Cognitive Services
      Correct
    • Option C:
      Azure Machine Learning Studio
      Correct
    • Option D:
      Microsoft Machine Learning Server
      Correct
    Show Answer & Explanation Hide Answer & Explanation

    Correct Answer: D

    Explanation

    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.

  4. Question 4

    Single answer

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

    • Option A:
      Increase Graphic Processing Units (GPUs).
      Correct
    • Option B:
      Increase the learning rate.
      Correct
    • Option C:
      Increase the training iterations,
      Correct
    • Option D:
      Increase Central Processing Units (CPUs).
      Correct
    Show Answer & Explanation Hide Answer & Explanation

    Correct Answer: A

    Explanation

    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.