Exam Name: Designing and Implementing a Data Science Solution on Azure
Certification Provider: Microsoft
Corresponding Certification: Microsoft Azure
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Microsoft DP-100 Exam Overview:
| Certification Vendor: | Microsoft |
|---|---|
| Exam Name: | Designing and Implementing a Data Science Solution on Azure |
| Exam Number: | DP-100 |
| Certificate Validity Period: | 1 year |
| Available Languages: | English, German, Japanese, Chinese (Simplified), Portuguese, Spanish, Korean, French |
| Exam Duration: | 100 minutes |
| Exam Price: | $165 USD |
| Passing Score: | 700/1000 |
| Related Certifications: | Microsoft Certified: Azure Data Scientist Associate |
| Exam Format: | Case Study, Drag and Drop, Interactive Tasks, Multiple Choice, Lab |
| Real Exam Qty: | 40-60 |
| Sample Questions: | DOWNLOAD DEMO |
| 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: Deploy and retrain models | 10-15% | - Monitor deployed models
|
| Topic 2: Prepare a model for deployment | 20-25% | - Manage deployment assets
|
| Topic 3: Explore data and train models | 35-40% | - Prepare data for modeling
|
| Topic 4: Design and prepare a machine learning solution | 20-25% | - Prepare development environments
|
Frequently Asked Questions for the Microsoft DP-100 Exam
The DP-100 exam, Designing and Implementing a Data Science Solution on Azure, assesses whether a candidate can apply Microsoft knowledge to the skills measured by this credential. It is associated with the Microsoft Azure certification. The certification is positioned at the Associate level. Related credentials include Microsoft Certified: Azure Data Scientist Associate.
The DP-100 exam includes 40-60 questions and allows 100 minutes. Plan your pacing before exam day rather than calculating it under pressure. Timed sessions with Actual4dump practice tests can help you decide when to flag a difficult item, keep moving, and reserve enough time for a final review.
The published passing score for Microsoft Designing and Implementing a Data Science Solution on Azure is 700/1000, and the official exam fee is $165 USD. A retake requires budgeting for the full official fee again, so it is sensible to complete several timed practice tests before scheduling. Consistent results across the 528 practice questions can give you a clearer picture of your readiness.
The stated prerequisite information for Microsoft Designing and Implementing a Data Science Solution on Azure is: Candidates should have experience with Azure services, Python programming, and machine learning frameworks such as Scikit-Learn, PyTorch, or TensorFlow. Before registering, review the eligibility details on the official exam page to confirm the requirements.
Yes. Actual4dump provides a free PDF demo so you can review the format and quality of the Microsoft Designing and Implementing a Data Science Solution on Azure practice questions before placing an order. Your purchase includes 365 days of free updates, and you can extend the update service after expiration at a 50% discount.
If you take the corresponding DP-100 exam within 60 days of purchase and do not pass, you may apply for a full refund under the 100% Money Back Guarantee. Claims based on an exam taken within 3 days of purchase are not eligible; free materials, expired orders, and downloaded products that were not used before sitting for the exam are also excluded. The candidate name must match the payer name.
To apply, submit a scanned enrollment slip and the official Score Report PDF within 2 days after the exam. Eligible requests are processed within 7 days. If you prefer an alternative, you may receive two free products of equal value and keep the update service for your original purchase.
Delivery is instant after payment. Your download is also sent to your email within one minute; if it has not arrived within 2 hours, contact customer service. There is no limit on the number of computers on which the material can be installed.
The published Microsoft Designing and Implementing a Data Science Solution on Azure outline contains 4 major domains. The opening domains include:
- Design and prepare a machine learning solution (20-25%)
- Explore data and train models (35-40%)
- Prepare a model for deployment (20-25%)
Review the complete Exam Topics section above for every domain and subtopic before planning your study time.
Microsoft Designing and Implementing a Data Science Solution on Azure Sample Questions:
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 have a Python script named train.py in a local folder named scripts. The script trains a regression model by using scikit-learn. The script includes code to load a training data file which is also located in the scripts folder.
You must run the script as an Azure ML experiment on a compute cluster named aml-compute.
You need to configure the run to ensure that the environment includes the required packages for model training. You have instantiated a variable named aml-compute that references the target compute cluster.
Solution: Run the following code:
Does the solution meet the goal?
- A. Yes
- B. No
Correct Answer: B 🗳️
Explanation: Only visible for Actual4dump members. You can sign-up / login (it's free).
You are building a regression model tot estimating the number of calls during an event.
You need to determine whether the feature values achieve the conditions to build a Poisson regression model.
Which two conditions must the feature set contain? I ach correct answer presents part of the solution. NOTE:
Each correct selection is worth one point.
- A. The label data can be positive or negative,
- B. The label data must be a negative value.
- C. The data must be whole numbers.
- D. The label data must be a positive value
- E. The label data must be non discrete.
Correct Answer: C,D 🗳️
Explanation: Only visible for Actual4dump members. You can sign-up / login (it's free).
You are creating a new Azure Machine Learning pipeline using the designer.
The pipeline must train a model using data in a comma-separated values (CSV) file that is published on a website. You have not created a dataset for this file.
You need to ingest the data from the CSV file into the designer pipeline using the minimal administrative effort.
Which module should you add to the pipeline in Designer?
- A. Enter Data ManuallyD
- B. Import Data
- C. Convert to CSV
- D. Dataset
Correct Answer: D 🗳️
Explanation: Only visible for Actual4dump members. You can sign-up / login (it's free).
You are moving a large dataset from Azure Machine Learning Studio to a Weka environment.
You need to format the data for the Weka environment.
Which module should you use?
- A. Convert to Dataset
- B. Convert to SVMLight
- C. Convert to ARFF
- D. Convert to CSV
Correct Answer: C 🗳️
Explanation: Only visible for Actual4dump members. You can sign-up / login (it's free).
You create a multi-class image classification deep learning experiment by using the PyTorch framework. You plan to run the experiment on an Azure Compute cluster that has nodes with GPU's.
You need to define an Azure Machine Learning service pipeline to perform the monthly retraining of the image classification model. The pipeline must run with minimal cost and minimize the time required to train the model.
Which three pipeline steps should you run 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:
Explanation:
Step 1: Configure a DataTransferStep() to fetch new image data...
Step 2: Configure a PythonScriptStep() to run image_resize.y on the cpu-compute compute target.
Step 3: Configure the EstimatorStep() to run training script on the gpu_compute computer target.
The PyTorch estimator provides a simple way of launching a PyTorch training job on a compute target.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-train-pytorch
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