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Microsoft DP-100 certification exam is a valuable credential for professionals looking to advance their career in data science. It demonstrates to employers that the candidate has the skills and knowledge required to design and implement data science solutions on Azure. Designing and Implementing a Data Science Solution on Azure certification is recognized by Microsoft and is highly respected within the industry.

The DP-100 certification is an essential certification for data professionals who want to develop their skills in designing and implementing data solutions using Microsoft Azure. Designing and Implementing a Data Science Solution on Azure certification demonstrates the candidate's proficiency in various aspects of data science, including data exploration, data preparation, data modeling, machine learning, and data visualization. Designing and Implementing a Data Science Solution on Azure certification is recognized globally and can help the candidate to gain better job opportunities and career growth.

The DP-100 Exam covers a wide range of topics, including data exploration and preparation, modeling, visualization, and deployment. Candidates will be tested on their ability to use various Azure tools and services, such as Azure Machine Learning, Azure Databricks, and Azure Cognitive Services, to build end-to-end data science solutions. They will also need to demonstrate their understanding of the principles of machine learning, statistical analysis, and data visualization, as well as their ability to communicate insights and findings to stakeholders. Passing DP-100 exam is a significant achievement for data science professionals who want to showcase their expertise in Azure-based data science solutions.

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Microsoft Designing and Implementing a Data Science Solution on Azure Sample Questions (Q39-Q44):

NEW QUESTION # 39
You have a dataset that contains over 150 features. You use the dataset to train a Support Vector Machine (SVM) binary classifier.
You need to use the Permutation Feature Importance module in Azure Machine Learning Studio to compute a set of feature importance scores for the dataset.
In which order should you perform the actions? To answer, move all actions from the list of actions to the answer area and arrange them in the correct order.

Answer:

Explanation:

Explanation:
Step 1: Add a Two-Class Support Vector Machine module to initialize the SVM classifier.
Step 2: Add a dataset to the experiment
Step 3: Add a Split Data module to create training and test dataset.
To generate a set of feature scores requires that you have an already trained model, as well as a test dataset.
Step 4: Add a Permutation Feature Importance module and connect to the trained model and test dataset.
Step 5: Set the Metric for measuring performance property to Classification - Accuracy and then run the experiment.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/two-class-support-vector-machine
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/permutation-feature-importance

NEW QUESTION # 40
You need to record the row count as a metric named row_count that can be returned using the get_metrics method of the Run object after the experiment run completes. Which code should you use?

A. run.tag('row_count', rows)
B. run.log('row_count', rows)
C. run.upload_file('row_count', './data.csv')
D. run.log_table('row_count', rows)
E. run.log_row('row_count', rows)

Answer: B

Explanation:
Log a numerical or string value to the run with the given name using log(name, value, description=''). Logging a metric to a run causes that metric to be stored in the run record in the experiment. You can log the same metric multiple times within a run, the result being considered a vector of that metric.
Example: run.log("accuracy", 0.95)
Incorrect Answers:
E: Using log_row(name, description=None, **kwargs) creates a metric with multiple columns as described in kwargs. Each named parameter generates a column with the value specified. log_row can be called once to log an arbitrary tuple, or multiple times in a loop to generate a complete table.
Example: run.log_row("Y over X", x=1, y=0.4)
Reference:
https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.run

NEW QUESTION # 41
You are performing feature scaling by using the scikit-learn Python library for x.1 x2, and x3 features.
Original and scaled data is shown in the following image.

Use the drop-down menus to select the answer choice that answers each question based on the information presented in the graphic.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation:
Box 1: StandardScaler
The StandardScaler assumes your data is normally distributed within each feature and will scale them such that the distribution is now centred around 0, with a standard deviation of 1.
Example:

All features are now on the same scale relative to one another.
Box 2: Min Max Scaler

Notice that the skewness of the distribution is maintained but the 3 distributions are brought into the same scale so that they overlap.
Box 3: Normalizer
References:
http://benalexkeen.com/feature-scaling-with-scikit-learn/

NEW QUESTION # 42
You plan to create a speech recognition deep learning model.
The model must support the latest version of Python.
You need to recommend a deep learning framework for speech recognition to include in the Data Science Virtual Machine (DSVM).
What should you recommend?

A. Rattle
B. Weka
C. Apache Drill
D. Tensorflow

Answer: D

Explanation:
Explanation
TensorFlow is an open source library for numerical computation and large-scale machine learning. It uses Python to provide a convenient front-end API for building applications with the framework TensorFlow can train and run deep neural networks for handwritten digit classification, image recognition, word embeddings, recurrent neural networks, sequence-to-sequence models for machine translation, natural language processing, and PDE (partial differential equation) based simulations.
References:
https://www.infoworld.com/article/3278008/what-is-tensorflow-the-machine-learning-library-explained.html

NEW QUESTION # 43
You need to produce a visualization for the diagnostic test evaluation according to the data visualization requirements.
Which three modules should you recommend be used in sequence? To answer, move the appropriate modules from the list of modules to the answer area and arrange them in the correct order.

Answer:

Explanation:

1 - SweepClustering
2 - Train Model
3 - Evaluate Model

NEW QUESTION # 44
......

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