How to Register For Exam DP-100: Designing and Implementing a Data Science Solution on Azure?
Reference: https://www.microsoft.com/en-us/learning/exam-dp-100.aspx
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2. Train Models & Run Experiments (25-30%):
- Metrics generation from experiment runs: The candidates must be able to use logs for troubleshooting errors in experiment runs, log metrics from experiment run, and view and retrieve experiment outputs.
- Training scripts run within Azure ML workspaces: The students should have the expertise in creating and running experiments utilizing Azure ML SDK as well configuring run settings for the scripts. This subject area also requires their skills in data consumption from datasets for an experiment using Azure ML SDK.
- Models creation with Azure ML Designer: This domain covers the examinees’ skills in using custom code modules within the design and using designer modules for the definition of pipeline data flows. It also requires one’s competence in ingesting data within designer pipelines and creating training pipelines utilizing ML Designer.
- Model training process automation: The individuals need the relevant skills in running pipelines, passing data within steps in pipelines, monitoring pipeline runs, and creating pipelines with the use of SDK.
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Microsoft DP-100 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Design and prepare a machine learning solution | 20-25% | - Manage compute resources
|
| Topic 2: Train and deploy models | 25-30% | - Manage models
|
| Topic 3: Explore data and run experiments | 20-25% | - Implement pipelines
|
| Topic 4: Optimize language models for AI applications | 25-30% | - Evaluate and improve models
|
Microsoft Designing and Implementing a Data Science Solution on Azure Sample Questions:
You are using an Azure Machine Learning workspace. You set up an environment for model testing and an environment for production.
The compute target for testing must minimize cost and deployment efforts. The compute target for production must provide fast response time, autoscaling of the deployed service, and support real-time inferencing.
You need to configure compute targets for model testing and production.
Which compute targets should you use? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Correct Answer:
Explanation:
Box 1: Local web service
The Local web service compute target is used for testing/debugging. Use it for limited testing and troubleshooting. Hardware acceleration depends on use of libraries in the local system.
Box 2: Azure Kubernetes Service (AKS)
Azure Kubernetes Service (AKS) is used for Real-time inference.
Recommended for production workloads.
Use it for high-scale production deployments. Provides fast response time and autoscaling of the deployed service Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/concept-compute-target
You need to implement a feature engineering strategy for the crowd sentiment local models.
What should you do?
- A. Apply an analysis of variance (ANOVA).
- B. Apply a Pearson correlation coefficient.
- C. Apply a linear discriminant analysis.
- D. Apply a Spearman correlation coefficient.
Correct Answer: C 🗳️
Explanation: Only visible for DumpsTorrent members. You can sign-up / login (it's free).
You manage an Azure Machine Learning workspace. The titanic.csv file is available in an Azure Blob Storage account named storage1. The container name is container " !. The folder name is data.
You perform interactive data wrangling by using a serverless Spark compute.
You need to load the data from Blob Storage into a Pandas dataframe.
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 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 actions to the answer area and arrange them in the correct order.
Correct Answer:
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. Data Science Virtual Machine for Windows 2016
- C. Data Science Virtual Machine for Linux (Ubuntu)
- D. Geo AI Data Science Virtual Machine with ArcGIS
- E. Data Science Virtual Machine for Linux (CentOS)
Correct Answer: C 🗳️
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