[Jan 03, 2022] DP-100 Exam Dumps - Microsoft Practice Test Questions
New Real DP-100 Exam Dumps Questions
Career Opportunities & Salary Outlook
The demand for the certified professionals is on the increase. As an Azure Data Scientist, you can explore many employment opportunities. So, if you want to boost your career potential, pursuing the Microsoft Certified: Azure Data Scientist Associate certification is the right step. To get this certificate, you have to pass the qualifying exam, which is Microsoft DP-100. With this associate-level certification, you can get the positions, such as a Data Scientist, a Senior Data Scientist, a Data Science Manager, and a Data Science Director. The average salary for these job roles is $135,000 per annum. With advanced experience, the specialists can earn up to $170,000 per year.
Knowing Associated Certification
The Microsoft DP-100 test is associated with the Microsoft Certified: Azure Data Scientist Associate certificate. It is a recently launched certification by Microsoft trying to impart the knowledge of concepts related to machine learning techniques. As a rule, earners are known to have industry-standard expertise related to evaluation and deployment models for building ML solutions. Apart from grating this noteworthy designation, the Microsoft DP-100 exam will also help the test-taker to gain some ACE college credits.
NEW QUESTION 45
You are analyzing a dataset by using Azure Machine Learning Studio.
YOU need to generate a statistical summary that contains the p value and the unique value count for each feature column.
Which two modules can you users? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.
- A. Compute linear Correlation
- B. Summarize Data
- C. Convert to Indicator Values
- D. Export Count Table
- E. Execute Python Script
Answer: A,D
NEW QUESTION 46
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 create an Azure Machine Learning service datastore in a workspace. The datastore contains the following files:
* /data/2018/Q1.csv
* /data/2018/Q2.csv
* /data/2018/Q3.csv
* /data/2018/Q4.csv
* /data/2019/Q1.csv
All files store data in the following format:
id,f1,f2i
1,1.2,0
2,1,1,
1 3,2.1,0
You run the following code:
You need to create a dataset named training_data and load the data from all files into a single data frame by using the following code:
Solution: Run the following code:
Does the solution meet the goal?
- A. No
- B. Yes
Answer: A
NEW QUESTION 47
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. Import Data
- B. Enter Data Manually
D - C. Dataset
- D. Convert to CSV
Answer: C
Explanation:
The preferred way to provide data to a pipeline is a Dataset object. The Dataset object points to data that lives in or is accessible from a datastore or at a Web URL. The Dataset class is abstract, so you will create an instance of either a FileDataset (referring to one or more files) or a TabularDataset that's created by from one or more files with delimited columns of data.
Example:
from azureml.core import Dataset
iris_tabular_dataset = Dataset.Tabular.from_delimited_files([(def_blob_store, 'train-dataset/iris.csv')]) Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-create-your-first-pipeline
NEW QUESTION 48
You need to identify the methods for dividing the data according, to the testing requirements.
Which properties should you select? To answer, select the appropriate option-, m the answer area. NOTE:
Each correct selection is worth one point.
Answer:
Explanation:
NEW QUESTION 49
You need to implement a scaling strategy for the local penalty detection data.
Which normalization type should you use?
- A. Weight
- B. Streaming
- C. Batch
- D. Cosine
Answer: C
Explanation:
Explanation
Post batch normalization statistics (PBN) is the Microsoft Cognitive Toolkit (CNTK) version of how to evaluate the population mean and variance of Batch Normalization which could be used in inference Original Paper.
In CNTK, custom networks are defined using the BrainScriptNetworkBuilder and described in the CNTK network description language "BrainScript." Scenario:
Local penalty detection models must be written by using BrainScript.
References:
https://docs.microsoft.com/en-us/cognitive-toolkit/post-batch-normalization-statistics
Topic 1, Case Study 1
Overview
You are a data scientist in a company that provides data science for professional sporting events. Models will be global and local market data to meet the following business goals:
*Understand sentiment of mobile device users at sporting events based on audio from crowd reactions.
*Access a user's tendency to respond to an advertisement.
*Customize styles of ads served on mobile devices.
*Use video to detect penalty events.
Current environment
Requirements
* Media used for penalty event detection will be provided by consumer devices. Media may include images and videos captured during the sporting event and snared using social media. The images and videos will have varying sizes and formats.
* The data available for model building comprises of seven years of sporting event media. The sporting event media includes: recorded videos, transcripts of radio commentary, and logs from related social media feeds feeds captured during the sporting events.
*Crowd sentiment will include audio recordings submitted by event attendees in both mono and stereo Formats.
Advertisements
* Ad response models must be trained at the beginning of each event and applied during the sporting event.
* Market segmentation nxxlels must optimize for similar ad resporr.r history.
* Sampling must guarantee mutual and collective exclusivity local and global segmentation models that share the same features.
* Local market segmentation models will be applied before determining a user's propensity to respond to an advertisement.
* Data scientists must be able to detect model degradation and decay.
* Ad response models must support non linear boundaries features.
* The ad propensity model uses a cut threshold is 0.45 and retrains occur if weighted Kappa deviates from 0.1
+/-5%.
* The ad propensity model uses cost factors shown in the following diagram:
The ad propensity model uses proposed cost factors shown in the following diagram:
Performance curves of current and proposed cost factor scenarios are shown in the following diagram:
Penalty detection and sentiment
Findings
*Data scientists must build an intelligent solution by using multiple machine learning models for penalty event detection.
*Data scientists must build notebooks in a local environment using automatic feature engineering and model building in machine learning pipelines.
*Notebooks must be deployed to retrain by using Spark instances with dynamic worker allocation
*Notebooks must execute with the same code on new Spark instances to recode only the source of the data.
*Global penalty detection models must be trained by using dynamic runtime graph computation during training.
*Local penalty detection models must be written by using BrainScript.
* Experiments for local crowd sentiment models must combine local penalty detection data.
* Crowd sentiment models must identify known sounds such as cheers and known catch phrases. Individual crowd sentiment models will detect similar sounds.
* All shared features for local models are continuous variables.
* Shared features must use double precision. Subsequent layers must have aggregate running mean and standard deviation metrics Available.
segments
During the initial weeks in production, the following was observed:
*Ad response rates declined.
*Drops were not consistent across ad styles.
*The distribution of features across training and production data are not consistent.
Analysis shows that of the 100 numeric features on user location and behavior, the 47 features that come from location sources are being used as raw features. A suggested experiment to remedy the bias and variance issue is to engineer 10 linearly uncorrected features.
Penalty detection and sentiment
*Initial data discovery shows a wide range of densities of target states in training data used for crowd sentiment models.
*All penalty detection models show inference phases using a Stochastic Gradient Descent (SGD) are running too stow.
*Audio samples show that the length of a catch phrase varies between 25%-47%, depending on region.
*The performance of the global penalty detection models show lower variance but higher bias when comparing training and validation sets. Before implementing any feature changes, you must confirm the bias and variance using all training and validation cases.
NEW QUESTION 50
You are analyzing the asymmetry in a statistical distribution.
The following image contains two density curves that show the probability distribution of two datasets.
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: Positive skew
Positive skew values means the distribution is skewed to the right.
Box 2: Negative skew
Negative skewness values mean the distribution is skewed to the left.
References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/compute-elementary-statistics
NEW QUESTION 51
You create a binary classification model to predict whether a person has a disease.
You need to detect possible classification errors.
Which error type should you choose for each description? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation
Box 1: True Positive
A true positive is an outcome where the model correctly predicts the positive class Box 2: True Negative A true negative is an outcome where the model correctly predicts the negative class.
Box 3: False Positive
A false positive is an outcome where the model incorrectly predicts the positive class.
Box 4: False Negative
A false negative is an outcome where the model incorrectly predicts the negative class.
Note: Let's make the following definitions:
"Wolf" is a positive class.
"No wolf" is a negative class.
We can summarize our "wolf-prediction" model using a 2x2 confusion matrix that depicts all four possible outcomes:
Reference:
https://developers.google.com/machine-learning/crash-course/classification/true-false-positive-negative
NEW QUESTION 52
You are analyzing the asymmetry in a statistical distribution.
The following image contains two density curves that show the probability distribution of two datasets.
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
Explanation:
Box 1: Positive skew
Positive skew values means the distribution is skewed to the right.
Box 2: Negative skew
Negative skewness values mean the distribution is skewed to the left.
References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/compute-elementary-statistics
NEW QUESTION 53
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 ARFF
- B. Convert to SVMLight
- C. Convert to CSV
- D. Convert to Dataset
Answer: A
Explanation:
Explanation/Reference:
Explanation:
Use the Convert to ARFF module in Azure Machine Learning Studio, to convert datasets and results in Azure Machine Learning to the attribute-relation file format used by the Weka toolset. This format is known as ARFF.
The ARFF data specification for Weka supports multiple machine learning tasks, including data preprocessing, classification, and feature selection. In this format, data is organized by entites and their attributes, and is contained in a single text file.
References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/convert-to-arff Testlet 1 Case study Overview You are a data scientist in a company that provides data science for professional sporting events. Models will use global and local market data to meet the following business goals:
Understand sentiment of mobile device users at sporting events based on audio from crowd reactions.
Assess a user's tendency to respond to an advertisement.
Customize styles of ads served on mobile devices.
Use video to detect penalty events
Current environment
Media used for penalty event detection will be provided by consumer devices. Media may include
images and videos captured during the sporting event and shared using social media. The images and videos will have varying sizes and formats.
The data available for model building comprises of seven years of sporting event media. The sporting
event media includes; recorded video transcripts or radio commentary, and logs from related social media feeds captured during the sporting events.
Crowd sentiment will include audio recordings submitted by event attendees in both mono and stereo
formats.
Penalty detection and sentiment
Data scientists must build an intelligent solution by using multiple machine learning models for penalty
event detection.
Data scientists must build notebooks in a local environment using automatic feature engineering and
model building in machine learning pipelines.
Notebooks must be deployed to retrain by using Spark instances with dynamic worker allocation.
Notebooks must execute with the same code on new Spark instances to recode only the source of the
data.
Global penalty detection models must be trained by using dynamic runtime graph computation during
training.
Local penalty detection models must be written by using BrainScript.
Experiments for local crowd sentiment models must combine local penalty detection data.
Crowd sentiment models must identify known sounds such as cheers and known catch phrases.
Individual crowd sentiment models will detect similar sounds.
All shared features for local models are continuous variables.
Shared features must use double precision. Subsequent layers must have aggregate running mean
and standard deviation metrics available.
Advertisements
During the initial weeks in production, the following was observed:
Ad response rated declined.
Drops were not consistent across ad styles.
The distribution of features across training and production data are not consistent
Analysis shows that, of the 100 numeric features on user location and behavior, the 47 features that come from location sources are being used as raw features. A suggested experiment to remedy the bias and variance issue is to engineer 10 linearly uncorrelated features.
Initial data discovery shows a wide range of densities of target states in training data used for crowd
sentiment models.
All penalty detection models show inference phases using a Stochastic Gradient Descent (SGD) are
running too slow.
Audio samples show that the length of a catch phrase varies between 25%-47% depending on region
The performance of the global penalty detection models shows lower variance but higher bias when
comparing training and validation sets. Before implementing any feature changes, you must confirm the bias and variance using all training and validation cases.
Ad response models must be trained at the beginning of each event and applied during the sporting
event.
Market segmentation models must optimize for similar ad response history.
Sampling must guarantee mutual and collective exclusively between local and global segmentation
models that share the same features.
Local market segmentation models will be applied before determining a user's propensity to respond to
an advertisement.
Ad response models must support non-linear boundaries of features.
The ad propensity model uses a cut threshold is 0.45 and retrains occur if weighted Kappa deviated
from 0.1 +/- 5%.
The ad propensity model uses cost factors shown in the following diagram:
The ad propensity model uses proposed cost factors shown in the following diagram:
Performance curves of current and proposed cost factor scenarios are shown in the following diagram:
NEW QUESTION 54
You are using C-Support Vector classification to do a multi-class classification with an unbalanced training dataset. The C-Support Vector classification using Python code shown below:
You need to evaluate the C-Support Vector classification code.
Which evaluation statement should you use? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation
Box 1: Automatically adjust weights inversely proportional to class frequencies in the input data The "balanced" mode uses the values of y to automatically adjust weights inversely proportional to class frequencies in the input data as n_samples / (n_classes * np.bincount(y)).
Box 2: Penalty parameter
Parameter: C : float, optional (default=1.0)
Penalty parameter C of the error term.
References:
https://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html
NEW QUESTION 55
You write code to retrieve an experiment that is run from your Azure Machine Learning workspace.
The run used the model interpretation support in Azure Machine Learning to generate and upload a model explanation.
Business managers in your organization want to see the importance of the features in the model.
You need to print out the model features and their relative importance in an output that looks similar to the following.
How should you complete the code? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Reference:
https://docs.microsoft.com/en-us/python/api/azureml-contrib-interpret/azureml.contrib.interpret.explanation.explanation_client.explanationclient?view=azure-ml-py
NEW QUESTION 56
You train a classification model by using a decision tree algorithm.
You create an estimator by running the following Python code. The variable feature_names is a list of all feature names, and class_names is a list of all class names.
from interpret.ext.blackbox import TabularExplainer
You need to explain the predictions made by the model for all classes by determining the importance of all features.
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:
Explanation:
Box 1: Yes
TabularExplainer calls one of the three SHAP explainers underneath (TreeExplainer, DeepExplainer, or KernelExplainer).
Box 2: Yes
To make your explanations and visualizations more informative, you can choose to pass in feature names and output class names if doing classification.
Box 3: No
TabularExplainer automatically selects the most appropriate one for your use case, but you can call each of its three underlying explainers underneath (TreeExplainer, DeepExplainer, or KernelExplainer) directly.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-machine-learning-interpretability-aml
NEW QUESTION 57
You need to resolve the local machine learning pipeline performance issue. What should you do?
- A. Increase Central Processing Units (CPUs).
- B. Increase Graphic Processing Units (GPUs).
- C. Increase the training iterations,
- D. Increase the learning rate.
Answer: B
NEW QUESTION 58
You are building an experiment using the Azure Machine Learning designer.
You split a dataset into training and testing sets. You select the Two-Class Boosted Decision Tree as the algorithm.
You need to determine the Area Under the Curve (AUC) of the model.
Which three modules should you use 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:
Explanation:
Step 1: Train Model
Two-Class Boosted Decision Tree
First, set up the boosted decision tree model.
1. Find the Two-Class Boosted Decision Tree module in the module palette and drag it onto the canvas.
2. Find the Train Model module, drag it onto the canvas, and then connect the output of the Two-Class Boosted Decision Tree module to the left input port of the Train Model module.
The Two-Class Boosted Decision Tree module initializes the generic model, and Train Model uses training data to train the model.
3. Connect the left output of the left Execute R Script module to the right input port of the Train Model module (in this tutorial you used the data coming from the left side of the Split Data module for training).
This portion of the experiment now looks something like this:
Step 2: Score Model
Score and evaluate the models
You use the testing data that was separated out by the Split Data module to score our trained models. You can then compare the results of the two models to see which generated better results.
Add the Score Model modules
1. Find the Score Model module and drag it onto the canvas.
2. Connect the Train Model module that's connected to the Two-Class Boosted Decision Tree module to the left input port of the Score Model module.
3. Connect the right Execute R Script module (our testing data) to the right input port of the Score Model module.
Step 3: Evaluate Model
To evaluate the two scoring results and compare them, you use an Evaluate Model module.
1. Find the Evaluate Model module and drag it onto the canvas.
2. Connect the output port of the Score Model module associated with the boosted decision tree model to the left input port of the Evaluate Model module.
3. Connect the other Score Model module to the right input port.
NEW QUESTION 59
You are creating a binary classification by using a two-class logistic regression model.
You need to evaluate the model results for imbalance.
Which evaluation metric should you use?
- A. AUC Curve
- B. Relative Absolute Error
- C. Relative Squared Error
- D. Mean Absolute Error
Answer: A
Explanation:
One can inspect the true positive rate vs. the false positive rate in the Receiver Operating Characteristic (ROC) curve and the corresponding Area Under the Curve (AUC) value. The closer this curve is to the upper left corner, the better the classifier's performance is (that is maximizing the true positive rate while minimizing the false positive rate). Curves that are close to the diagonal of the plot, result from classifiers that tend to make predictions that are close to random guessing.
References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio/evaluate-model-performance#evaluating-a-binary-classification-model
NEW QUESTION 60
You have a dataset that includes home sales data for a city. The dataset includes the following columns.
Each row in the dataset corresponds to an individual home sales transaction.
You need to use automated machine learning to generate the best model for predicting the sales price based on the features of the house.
Which values should you use? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Reference:
https://docs.microsoft.com/en-us/learn/modules/create-regression-model-azure-machine-learning-designer
NEW QUESTION 61
You are analyzing a dataset by using Azure Machine Learning Studio.
You need to generate a statistical summary that contains the p-value and the unique count for each feature column.
Which two modules can you use? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.
- A. Convert to Indicator Values
- B. Export Count Table
- C. Computer Linear Correlation
- D. Execute Python Script
- E. Summarize Data
Answer: B,E
Explanation:
The Export Count Table module is provided for backward compatibility with experiments that use the Build Count Table (deprecated) and Count Featurizer (deprecated) modules.
E: Summarize Data statistics are useful when you want to understand the characteristics of the complete dataset. For example, you might need to know:
How many missing values are there in each column?
How many unique values are there in a feature column?
What is the mean and standard deviation for each column?
The module calculates the important scores for each column, and returns a row of summary statistics for each variable (data column) provided as input.
Incorrect Answers:
A: The Compute Linear Correlation module in Azure Machine Learning Studio is used to compute a set of Pearson correlation coefficients for each possible pair of variables in the input dataset.
C: With Python, you can perform tasks that aren't currently supported by existing Studio modules such as:
Visualizing data using matplotlib
Using Python libraries to enumerate datasets and models in your workspace Reading, loading, and manipulating data from sources not supported by the Import Data module D: The purpose of the Convert to Indicator Values module is to convert columns that contain categorical values into a series of binary indicator columns that can more easily be used as features in a machine learning model.
References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/export-count-table
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/summarize-data
NEW QUESTION 62
......
Microsoft DP-100: Career Prospects
After successfully passing the Microsoft DP-100 exam, you will obtain the Microsoft Certified: Azure Data Scientist Associate certification. Getting certified will allow you to qualify for several positions, including the following titles:
- Software Developer
- Data Engineer
- Administrative Analyst
- Data Analyst
Obtaining this certification is also beneficial from a financial point of view. In fact, the average salary that a certified professional can earn is $96,642 per year.
DP-100 Certification Exam Dumps Questions in here: https://drive.google.com/open?id=1CM39t12DD4j62PyAqaxEuPa0qY8Ea4QQ
Pass Your DP-100 Exam Easily with Accurate PDF Questions: https://www.torrentvalid.com/DP-100-valid-braindumps-torrent.html