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DP-100 Exam Outline

The Microsoft DP-100 was recently renewed to meet the most current market needs and now it measures the following skills:

  • Deploying and Consuming Models;
  • Setting Up the Workspace for Azure Machine Learning;
  • Running Experiments and Training Models.
  • Optimizing and Managing Models;

The DP-100 exam domain of Setting Up the Workspace for Azure Machine Learning (ML) has three sections. The first touches on creating the workspace for ML. Here, you're to come across tasks like creating and configuring the workspace and managing it using Azure ML studio. The next part is concerning data object management within the workspace of Azure ML, where the focus goes to registering and maintaining datasets. The final aspect regards maintaining contexts for experiment compute. Under this, there will be creating instances for compute, determining the appropriate specs for compute targeting workload training, and developing targets for compute directed at experiments as well as training.

Regarding Optimizing and Managing Models, candidates will build their skills in five crucial areas. To begin is the area of creating optimal models using automated ML. This takes into account areas like Azure ML studio, Azure ML SDK, scaling options for pre-processing, algorithm determination, and getting data to be utilized in running the automated ML. The next thing goes into tuning hyperparameters using hyperdrive. Candidates need to note the sampling methods, search space, primary metric, termination options, and the right model. Another field concerns managing models where coverage includes model interpreters and feature importance data. Finally, students will learn how to manage models by exploring trained model registration, monitoring model usage, and monitoring data drift.

The Microsoft DP-100 exam also deals with the Deploying and Consuming Models. Of interest, there are four sections. It starts with the creation of targets for production compute involving security meant for deployed services compute options targeting deployment. It's followed by the part of deploying a model as a service. This touches deployment settings, consuming deployed services, and troubleshooting issues for deployment containers. The next segment is creating a batch interference pipeline. Finally, students look at publishing a web service in the form of a designer pipeline. Issues also covered are compute resource, inference pipeline, and consumption of an already deployed endpoint.

The last DP-100 exam domain talks about Running Experiments and Training Models. The first way to achieve abilities in this area is by learning how to use Azure ML Designer to create models. This will be actualized by exploring creation of a training pipeline, ingestion of data within a designer pipeline, defining data flow for a pipeline using designer modules, and using modules for custom code. The second one regards running training scripts within the Azure ML workspace. Within this sphere, the students' focus will be how to use the Azure ML SDK in consuming data from a dataset in an experiment. The third thing in this topic has to do with using an experiment run to generate metrics. Here, learning includes log metrics, retrieving and viewing experiment outputs, and troubleshooting experiment errors using logs. The fourth and final area of concern is automating the process of model training. This includes developing a pipeline by utilizing the SDK, passing data, running a pipeline, and monitoring pipeline runs.

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.

  • Prepare Data for Modeling (25%)

    This subject area revolves around cleansing and transforming data; performing EDA (Exploratory Data Analysis); transforming data into usable datasets.

  • 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.

  • 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.

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The test material sorts out the speculations and genuine factors in any case in the event that you truly need a specific limit, you want to deal with the applications or live undertakings for better execution in the DP-100 Designing and Implementing a Data Science Solution on Azure exam. You will get unprecedented information about the subject and work on it impeccably for the Microsoft DP-100 dumps.

The DP-100 exam covers a wide range of topics such as data exploration and preparation, modeling, feature engineering, and optimization, among others. Candidates who pass this certification exam demonstrate their expertise in designing and implementing data science solutions using Azure services such as Azure Machine Learning, Azure Databricks, and Azure Stream Analytics. Passing the DP-100 exam is a significant achievement for data scientists and can open up numerous career opportunities in the field of data science.

Microsoft Designing and Implementing a Data Science Solution on Azure Sample Questions (Q73-Q78):

NEW QUESTION # 73
You create a multi-class image classification deep learning model that uses a set of labeled images. You create a script file named train.py that uses the PyTorch 1.3 framework to train the model.
You must run the script by using an estimator. The code must not require any additional Python libraries to be installed in the environment for the estimator. The time required for model training must be minimized.
You need to define the estimator that will be used to run the script.
Which estimator type should you use?

  • A. Estimator
  • B. SKLearn
  • C. PyTorch
  • D. TensorFlow

Answer: C

Explanation:
For PyTorch, TensorFlow and Chainer tasks, Azure Machine Learning provides respective PyTorch, TensorFlow, and Chainer estimators to simplify using these frameworks.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-train-ml-models


NEW QUESTION # 74
You need to set up the Permutation Feature Importance module according to the model training requirements.
Which properties should you select? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

d


NEW QUESTION # 75
You are building a binary classification model by using a supplied training set.
The training set is imbalanced between two classes.
You need to resolve the data imbalance.
What are three possible ways to achieve this goal? Each correct answer presents a complete solution NOTE: Each correct selection is worth one point.

  • A. Penalize the classification
  • B. Normalize the training feature set.
  • C. Generate synthetic samples in the minority class.
  • D. Use accuracy as the evaluation metric of the model.
  • E. Resample the data set using under sampling or oversampling

Answer: C,D,E


NEW QUESTION # 76
You arc I mating a deep learning model to identify cats and dogs. You have 25,000 color images.
You must meet the following requirements:
* Reduce the number of training epochs.
* Reduce the size of the neural network.
* Reduce over-fitting of the neural network.
You need to select the image modification values.
Which value 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 # 77
You need to build a feature extraction strategy for the local models.
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:

Explanation


NEW QUESTION # 78
......

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