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Microsoft DP-100: Preparation Process
Thorough preparation is an important part of the Microsoft DP-100 exam and its associate-level certification. Here is the ultimate guide to prepare for this test with great deliberation.
Reference: https://www.microsoft.com/en-us/learning/exam-dp-100.aspx
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Microsoft DP-100 Practice Test Questions, Microsoft DP-100 Exam Practice Test Questions
The Microsoft DP-100: Designing & Implementing a Data Science Solution on Azure exam evaluates the skills and knowledge of the professionals in a range of technical tasks. These include training models and running experiments, setting up an Azure ML workspace, consuming and deploying models, and managing & optimizing models. This test is the only qualifying exam that leads to the Microsoft Certified: Azure Data Scientist Associate certification.
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Microsoft DP-100 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Train and deploy models | 25-30% | - Deploy models
|
| Topic 2: Explore data and run experiments | 20-25% | - Explore and visualize data
|
| Topic 3: Design and prepare a machine learning solution | 20-25% | - Design a machine learning solution
|
| Topic 4: Optimize language models for AI applications | 25-30% | - Optimize with Retrieval Augmented Generation
|
Microsoft Designing and Implementing a Data Science Solution on Azure Sample Questions:
You have an Azure Machine Learning workspace.
You plan to use Azure Machine Learning Python SDK v2 to register a component in the workspace The component definition is stored in the local file ./components/train/train.yml.
You write code to connect to the workspace by using the ml_client object and import all required libraries You need to complete the remaining code.
How should you complete the code? to answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Correct Answer:
Explanation:
You plan to use a Data Science Virtual Machine (DSVM) with the open source deep learning frameworks Caffe2 and Theano. You need to select a pre configured DSVM to support the framework.
What should you create?
- A. Data Science Virtual Machine for Windows 2012
- B. Geo AI Data Science Virtual Machine with ArcGIS
- C. Data Science Virtual Machine for Linux (CentOS)
- D. Data Science Virtual Machine for Linux (Ubuntu)
- E. Data Science Virtual Machine for Windows 2016
Correct Answer: D 🗳️
You are a data scientist working for a bank and have used Azure ML to train and register a machine learning model that predicts whether a customer is likely to repay a loan.
You want to understand how your model is making selections and must be sure that the model does not violate government regulations such as denying loans based on where an applicant lives.
You need to determine the extent to which each feature in the customer data is influencing predictions.
What should you do?
- A. Use the interpretability package to generate an explainer for the model.
- B. Add tags to the model registration indicating the names of the features in the training dataset.
- C. Enable data drift monitoring for the model and its training dataset.
- D. Score the model against some test data with known label values and use the results to calculate aconfusion matrix.
- E. Use the Hyperdrive library to test the model with multiple hyperparameter values.
Correct Answer: A 🗳️
Explanation: Only visible for DumpTorrent members. You can sign-up / login (it's free).
You plan to use automated machine learning by using Azure Machine Learning Python SDK v2 to train a regression model. You have data that has features with missing values, and categorical features with few distinct values.
You need to control whether automated machine learning automatically imputes missing values and encode categorical features as part of the training task. Which enemy of the autumn package should you use?
- A. FeaturizationMode
- B. RegressionModels
- C. RegressionPrimaryMetrics
- D. ForecastHorizonMode
Correct Answer: A 🗳️
Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.
After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.
You are analyzing a numerical dataset which contain missing values in several columns.
You must clean the missing values using an appropriate operation without affecting the dimensionality of the feature set.
You need to analyze a full dataset to include all values.
Solution: Use the last Observation Carried Forward (IOCF) method to impute the missing data points.
Does the solution meet the goal?
- A. Yes
- B. No
Correct Answer: B 🗳️
Explanation: Only visible for DumpTorrent members. You can sign-up / login (it's free).
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