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Microsoft DP-100: Skills Measured

Microsoft provides you with the elaborate outline of the skills that you need to acquire before attempting the test. The specific topics of the exam along with the main subtopics are enumerated below:

Perform Feature Engineering (15%)
Answering the questions that are drawn from this domain, the test takers should be able to perform the tasks such as performing feature selection as well as performing feature extraction.
Define and Prepare the Development Environment (15%)
To answer the questions within this objective, the applicants should have the professional ability to accomplish such technical tasks as selecting a development environment; quantifying the business problem; setting up a development environment; etc.
Prepare Data for Modeling (25%)
This subject area revolves around cleansing and transforming data; performing EDA (Exploratory Data Analysis); transforming data into usable datasets.
Develop Models (40%)
The skills measured within this topic include selecting an algorithmic approach; evaluating model performance; training the model; identifying data imbalances; splitting datasets, and so on.

The benefit in Obtaining the DP-100 Exam Certification

When Candidates applying for a job or looking to promotion in their current position, a Microsoft Certified Azure Data Scientist Associate certification in the field in which Candidates are applying will put you at the top of the list and make them a desirable candidate for employers.
After completion of Microsoft Certified Azure Data Scientist Associate Certification candidates receive official confirmation from Microsoft that you are now fully certified in their chosen field. This can be now added to their CV, cover letters and job applications.
Candidates will get in-depth knowledge by completing the courses along with the access to revision materials for 6 months upon completion means they will have a wider skill set when it comes to the various technologies and systems than an uncertified professional. Certified Professional in this particular skill set is 74% more efficient when it comes to completing their tasks in a timely well-executed manner.
Becoming Microsoft Certified Azure Data Scientist Associate means one thing you are worth more to the company and therefore more to yourself in the form of an upgraded pay package. On average a Microsoft Certified Azure Data Scientist Associate member of staff is estimated to be worth 30% more to a company than their uncertified professionals.
Organization owners invest a lot in their employees when it comes to their training with the goal of making them quicker, more efficient, and more knowledgeable about their role. Certified Professional will reduce the time he spends on tasks, meaning he can get more done this could help reduce company downtime when repairing faults on a system or fixing hardware problems.

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Microsoft Designing and Implementing a Data Science Solution on Azure Sample Questions (Q518-Q523):

NEW QUESTION # 518
You configure a Deep Learning Virtual Machine for Windows.
You need to recommend tools and frameworks to perform the following:
Build deep neural network (DNN) models
Perform interactive data exploration and visualization
Which tools and frameworks should you recommend? To answer, drag the appropriate tools to the correct tasks. Each tool 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.

Answer:

Explanation:

Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/train-vowpal-wabbit-version-8-model
https://docs.microsoft.com/en-us/azure/architecture/data-guide/scenarios/interactive-data-exploration

NEW QUESTION # 519
You are preparing to use the Azure ML SDK to run an experiment and need to create compute. You run the following code:

For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Reference:
https://notebooks.azure.com/azureml/projects/azureml-getting-started/html/how-to-use-azureml/training/train-on-amlcompute/train-on-amlcompute.ipynb
https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.compute.computetarget

NEW QUESTION # 520
You have an Azure Machine Learning workspace that contains a CPU-based compute cluster and an Azure Kubernetes Services (AKS) inference cluster. You create a tabular dataset containing data that you plan to use to create a classification model.
You need to use the Azure Machine Learning designer to create a web service through which client applications can consume the classification model by submitting new data and getting an immediate prediction as a response.
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.

Answer:

Explanation:

Explanation:
Step 1: Create and start a Compute Instance
To train and deploy models using Azure Machine Learning designer, you need compute on which to run the training process, test the model, and host the model in a deployed service.
There are four kinds of compute resource you can create:
Compute Instances: Development workstations that data scientists can use to work with data and models.
Compute Clusters: Scalable clusters of virtual machines for on-demand processing of experiment code.
Inference Clusters: Deployment targets for predictive services that use your trained models.
Attached Compute: Links to existing Azure compute resources, such as Virtual Machines or Azure Databricks clusters.
Step 2: Create and run a training pipeline..
After you've used data transformations to prepare the data, you can use it to train a machine learning model. Create and run a training pipeline Step 3: Create and run a real-time inference pipeline After creating and running a pipeline to train the model, you need a second pipeline that performs the same data transformations for new data, and then uses the trained model to inference (in other words, predict) label values based on its features. This pipeline will form the basis for a predictive service that you can publish for applications to use.
Reference:
https://docs.microsoft.com/en-us/learn/modules/create-classification-model-azure-machine-learning-designer/

NEW QUESTION # 521
You use Data Science Virtual Machines (DSVMs) for Windows and Linux in Azure.
You need to access the DSVMs.
Which utilities should you use? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

NEW QUESTION # 522
You are using the Azure Machine Learning Service to automate hyper par a meter exploration of your neural network classification model.
You must define the hyper parameter space to automatically tune hyper parameters using random sampling according to following requirements:
* Learning rate must be selected from a normal distribution with a mean value of 10 and a standard deviation of 3.
* Batch size must be 16, 32 and 64.
* Keep probability must be a value selected from a uniform distribution between the range of 0.05 and 0.1.
You need to use the par am .sampling method of the Python API for the Azure Machine Learning Service.
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.

Answer:

Explanation:

NEW QUESTION # 523
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