Ask Question
DP-100: Designing and Implementing a Data Science Solution on Azure
.png)
The Microsoft Certified: Designing and Implementing a Data Science Solution on Microsoft Azure (DP-100) exam is a crucial certification for anyone aiming to advance their career in data science. Our topic is your ultimate resource for DP-100 practice test shared by individuals who have successfully passed the exam. These practice tests provide real-world scenarios and invaluable insights to help you ace your preparation.
Why Use DP-100 Practice Test?
-
Real Exam Experience: Our practice test accurately replicates the format and difficulty of the actual Microsoft DP-100 exam, providing you with a realistic preparation experience.
-
Identify Knowledge Gaps: Practicing with these tests helps you identify areas where you need more study, allowing you to focus your efforts effectively.
-
Boost Confidence: Regular practice with exam-like questions builds your confidence and reduces test anxiety.
-
Track Your Progress: Monitor your performance over time to see your improvement and adjust your study plan accordingly.
Key Features of DP-100 Practice Test:
-
Up-to-Date Content: Our community ensures that the questions are regularly updated to reflect the latest exam objectives and technology trends.
-
Detailed Explanations: Each question comes with detailed explanations, helping you understand the correct answers and learn from any mistakes.
-
Comprehensive Coverage: The practice test covers all key topics of the Microsoft DP-100 exam, including designing and implementing data science solutions using Azure Machine Learning, deploying and operationalizing machine learning solutions, and managing and monitoring models.
-
Customizable Practice: Create your own practice sessions based on specific topics or difficulty levels to tailor your study experience to your needs.
Exam number: DP-100
Exam name: Designing and Implementing a Data Science Solution on Microsoft Azure
Length of test: 120 minutes
Exam format: Multiple-choice and multiple-response questions.
Exam language: English
Number of questions in the actual exam: Maximum of 40-60 questions
Passing score: 700/1000
Use the member-shared Microsoft DP-100 Practice Test to ensure you’re fully prepared for your certification exam. Start practicing today and take a significant step towards achieving your certification goals!
Microsoft DP-100 Practice Tests
Related questions
HOTSPOT
You create an Azure Machine Learning dataset containing automobile price data The dataset includes 10,000 rows and 10 columns You use Azure Machine Learning Designer to transform the dataset by using an Execute Python Script component and custom code.
The code must combine three columns to create a new column.
You need to configure the code function.
Which configurations should you use? lo answer, select the appropriate options in the answer area
NOTE: Each correct selection is worth one point.
Unlock Premium Member
DRAG DROP
You create an Azure Machine Learning workspace and an Azure Synapse Analytics workspace with a Spark pool. The workspaces are contained within the same Azure subscription.
You must manage the Synapse Spark pool from the Azure Machine Learning workspace.
You need to attach the Synapse Spark pool in Azure Machine Learning by usinq the Python SDK v2.
Which three actions should you perform in sequence? To answer move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.
You use Azure Machine Learning studio to analyze an mltable data asset containing a decimal column named column1. You need to verify that the column1 values are normally distributed.
Which statistic should you use?
You register a model that you plan to use in a batch inference pipeline.
The batch inference pipeline must use a ParallelRunStep step to process files in a file dataset. The script has the ParallelRunStep step runs must process six input files each time the inferencing function is called.
You need to configure the pipeline.
Which configuration setting should you specify in the ParallelRunConfig object for the PrallelRunStep step?
Explanation:
node_count is the number of nodes in the compute target used for running the ParallelRunStep.
Incorrect Answers:
A: process_count_per_node
Number of processes executed on each node. (optional, default value is number of cores on node.)
C: mini_batch_size
For FileDataset input, this field is the number of files user script can process in one run() call. For TabularDataset input, this field is the approximate size of data the user script can process in one run() call. Example values are 1024, 1024KB, 10MB, and 1GB.
D: error_threshold
The number of record failures for TabularDataset and file failures for FileDataset that should be ignored during processing. If the error count goes above this value, then the job will be aborted.
Reference:
https://docs.microsoft.com/en-us/python/api/azureml-contrib-pipeline-steps/azureml.contrib.pipeline.steps.parallelrunconfig?view=azure-ml-py
HOTSPOT
You create an Azure Machine Learning model to include model files and a scorning script. You must deploy the model. The deployment solution must meet the following requirements:
* Provide near real-time inferencing.
* Enable endpoint and deployment level cost estimates.
* Support logging to Azure Log Analytics.
You need to configure the deployment solution.
What should you configure? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
You train and publish a machine teaming model.
You need to run a pipeline that retrains the model based on a trigger from an external system.
What should you configure?
You create a multi-class image classification deep learning model.
You train the model by using PyTorch version 1.2.
You need to ensure that the correct version of PyTorch can be identified for the inferencing environment when the model is deployed.
What should you do?
Explanation:
framework_version: The PyTorch version to be used for executing training code.
Reference: https://docs.microsoft.com/en-us/python/api/azureml-train-core/azureml.train.dnn.pytorch?view=azure-ml-py
You plan to create a speech recognition deep learning model.
The model must support the latest version of Python.
You need to recommend a deep learning framework for speech recognition to include in the Data Science Virtual Machine (DSVM).
What should you recommend?
Explanation:
TensorFlow is an open-source library for numerical computation and large-scale machine learning. It uses Python to provide a convenient front-end API for building applications with the framework TensorFlow can train and run deep neural networks for handwritten digit classification, image recognition, word embeddings, recurrent neural networks, sequence-to-sequence models for machine translation, natural language processing, and PDE (partial differential equation) based simulations.
Incorrect Answers:
A: Rattle is the R analytical tool that gets you started with data analytics and machine learning.
C: Weka is used for visual data mining and machine learning software in Java.
D: Scikit-learn is one of the most useful libraries for machine learning in Python. It is on NumPy, SciPy and matplotlib, this library contains a lot of efficient tools for machine learning and statistical modeling including classification, regression, clustering and dimensionality reduction.
Reference:
https://www.infoworld.com/article/3278008/what-is-tensorflow-the-machine-learning-library-explained.html
You deploy a real-time inference service for a trained model.
The deployed model supports a business-critical application, and it is important to be able to monitor the data submitted to the web service and the predictions the data generates.
You need to implement a monitoring solution for the deployed model using minimal administrative effort.
What should you do?
Explanation:
B. Enable Azure Application Insights for the service endpoint and view logged data in the Azure portal.
C. View the log files generated by the experiment used to train the model.
D. Create an ML Flow tracking URI that references the endpoint, and view the data logged by ML Flow.
Answer: B
Explanation:
Configure logging with Azure Machine Learning studio
You can also enable Azure Application Insights from Azure Machine Learning studio. When you're ready to deploy your model as a web service, use the following steps to enable Application Insights:
1. Sign in to the studio at https://ml.azure.com.
2. Go to Models and select the model you want to deploy.
3. Select +Deploy.
4. Populate the Deploy model form.
5. Expand the Advanced menu.
6. Select Enable Application Insights diagnostics and data collection.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-enable-app-insights
DRAG DROP
You are analyzing a raw dataset that requires cleaning.
You must perform transformations and manipulations by using Azure Machine Learning Studio.
You need to identify the correct modules to perform the transformations.
Which modules should you choose? To answer, drag the appropriate modules to the correct scenarios. Each module may be used once, 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.
Explanation:
Box 1: Clean Missing Data
Box 2: SMOTE
Use the SMOTE module in Azure Machine Learning Studio to increase the number of underepresented cases in a dataset used for machine learning. SMOTE is a better way of increasing the number of rare cases than simply duplicating existing cases.
Box 3: Convert to Indicator Values
Use the Convert to Indicator Values module in Azure Machine Learning Studio. The purpose of this module is to convert columns that contain categorical values into a series of binary indicator columns that can more easily be used as features in a machine learning model.
Box 4: Remove Duplicate Rows
References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/smote
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/convert-to-indicator-values
Question