Practice should feel like the real thing. The PC and online versions from GuideTorrent simulate the DP-100 exam environment with intelligent, interactive testing, so you can assess your pace, adjust your timing, and walk into the Microsoft Designing and Implementing a Data Science Solution on Azure exam already knowing what to expect.
Microsoft DP-100 Exam Overview:
| Certification Vendor: | Microsoft |
|---|---|
| Exam Name: | Designing and Implementing a Data Science Solution on Azure |
| Exam Number: | DP-100 |
| Available Languages: | German, French, English, Chinese (Simplified), Japanese, Korean, Spanish, Portuguese |
| Exam Price: | $165 USD |
| Exam Duration: | 100 minutes |
| Certificate Validity Period: | 1 year |
| Related Certifications: | Microsoft Certified: Azure Data Scientist Associate |
| Passing Score: | 700/1000 |
| Real Exam Qty: | 40-60 |
| Exam Format: | Lab, Interactive Tasks, Multiple Choice, Case Study, Drag and Drop |
| Sample Questions: |  |
| Exam Way: | Online proctored exam or test center delivery through Pearson VUE. |
| Pre Condition: | Candidates should have experience with Azure services, Python programming, and machine learning frameworks such as Scikit-Learn, PyTorch, or TensorFlow. |
| Official Syllabus URL: | https://learn.microsoft.com/en-us/credentials/certifications/exams/dp-100/ |
Microsoft DP-100 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Explore data and train models | 35-40% | - Optimize model performance
|
| Topic 2: Design and prepare a machine learning solution | 20-25% | - Prepare development environments
|
| Topic 3: Prepare a model for deployment | 20-25% | - Deploy machine learning models
|
| Topic 4: Deploy and retrain models | 10-15% | - Implement retraining pipelines
|
Common Questions About Microsoft Designing and Implementing a Data Science Solution on Azure and GuideTorrent
The Microsoft Designing and Implementing a Data Science Solution on Azure blueprint breaks down into these main domains:
- Explore data and train models (35-40%)
- Design and prepare a machine learning solution (20-25%)
- Deploy and retrain models (10-15%)
Additional domains follow in the official outline, and our question bank covers every one of them.
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Based on the latest exam information, the DP-100 exam contains 40-60 questions and gives you 100 minutes minutes to complete them. Practicing under the same time limit in our PC or online version is a good way to assess your pace before the real thing.
Microsoft lists the following prerequisites for the Microsoft Designing and Implementing a Data Science Solution on Azure: Candidates should have experience with Azure services, Python programming, and machine learning frameworks such as Scikit-Learn, PyTorch, or TensorFlow..
You can verify the details on the official certification page.
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Because it was built for people with little time to spare. The bank covers the important knowledge points of the current Microsoft Designing and Implementing a Data Science Solution on Azure blueprint with expert-verified answers, and you study in the format that fits you: a printable PDF you can mark up with a pen, or PC and online versions that simulate the real exam interactively and let you assess your pace. A free demo lets you confirm the quality before you buy, and every purchase includes 365 days of free updates plus a 50% renewal discount afterward.
The current passing score for the DP-100 exam is 700/1000, and the exam fee is $165 USD. Fees and cut scores are set by Microsoft, so confirm the latest figures on the official page before you register.
Microsoft Designing and Implementing a Data Science Solution on Azure Sample Questions:
You use Azure Machine Learning to tram a model.
You must use Baylean sampling to Tune hyperparaters.
You need to select a learning_rate parameter distribution.
Which two distributions can you use? Each correct answer presents a complete solution.
NOTE Each correct selection is worth one point.
- A. Choice
- B. Uniform
- C. Normal
- D. LogUniform
- E. QNormal
Correct Answer: A,B 🗳️
You have a dataset created for multiclass classification tasks that contains a normalized numerical feature set with 10,000 data points and 150 features.
You use 75 percent of the data points for training and 25 percent for testing. You are using the scikit-learn machine learning library in Python. You use X to denote the feature set and Y to denote class labels.
You create the following Python data frames:
You need to apply the Principal Component Analysis (PCA) method to reduce the dimensionality of the feature set to 10 features in both training and testing sets.
How should you complete the code segment? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Correct Answer:
Explanation:
Box 1: PCA(n_components = 10)
Need to reduce the dimensionality of the feature set to 10 features in both training and testing sets.
Example:
from sklearn.decomposition import PCA
pca = PCA(n_components=2) ;2 dimensions
principalComponents = pca.fit_transform(x)
Box 2: pca
fit_transform(X[, y])fits the model with X and apply the dimensionality reduction on X.
Box 3: transform(x_test)
transform(X) applies dimensionality reduction to X.
References:
https://scikit-learn.org/stable/modules/generated/sklearn.decomposition.PCA.html
You create a new Azure Machine Learning workspace with a compute cluster.
You need to create the compute cluster asynchronously by using the Azure Machine Learning Python SDK v2.
How should you complete the code segment? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point
Correct Answer:
Explanation:
You need to select an environment that will meet the business and data requirements.
Which environment should you use?
- A. Azure HDInsight with Spark MLlib
- B. Azure Cognitive Services
- C. Microsoft Machine Learning Server
- D. Azure Machine Learning Studio
Correct Answer: C 🗳️
A set of CSV files contains sales records. All the CSV files have the same data schema.
Each CSV file contains the sales record for a particular month and has the filename sales.csv. Each file in stored in a folder that indicates the month and year when the data was recorded. The folders are in an Azure blob container for which a datastore has been defined in an Azure Machine Learning workspace. The folders are organized in a parent folder named sales to create the following hierarchical structure:
At the end of each month, a new folder with that month's sales file is added to the sales folder.
You plan to use the sales data to train a machine learning model based on the following requirements:
You must define a dataset that loads all of the sales data to date into a structure that can be easily converted to a dataframe.
You must be able to create experiments that use only data that was created before a specific previous month, ignoring any data that was added after that month.
You must register the minimum number of datasets possible.
You need to register the sales data as a dataset in Azure Machine Learning service workspace.
What should you do?
- A. Create a tabular dataset that references the datastore and explicitly specifies each ' sales/mm-yyyy/sales.csv ' file. Register the dataset with the name sales_dataset each month as a new version and with a tag named month indicating the month and year it was registered. Use this dataset for all experiments, identifying the version to be used based on the month tag as necessary.
- B. Create a new tabular dataset that references the datastore and explicitly specifies each ' sales/mm-yyyy/ sales.csv ' file every month. Register the dataset with the name sales_dataset_MM-YYYY each month with appropriate MM and YYYY values for the month and year. Use the appropriate month-specific dataset for experiments.
- C. Create a tabular dataset that references the datastore and explicitly specifies each ' sales/mm-yyyy/sales.
csv ' file every month. Register the dataset with the name sales_dataset each month, replacing theexisting dataset and specifying a tag named month indicating the month and year it was registered.
Usethis dataset for all experiments. - D. Create a tabular dataset that references the datastore and specifies the path ' sales/*/sales.csv ' , register the dataset with the name sales_dataset and a tag named month indicating the month and year it was registered, and use this dataset for all experiments.
Correct Answer: D 🗳️
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