Microsoft DP-100 Exam: Free Questions and Answers [2023]

Are you getting ready for the Microsoft DP-100 certification exam? In this article, we have prepared 40 free DP-100 questions, accompanied by detailed explanations, to enrich your comprehension of Microsoft Azure data science and machine learning concepts. Each question will be followed by its correct answer, and we will provide context based on the exam curriculum and Microsoft's recommended approaches.

The Microsoft DP-100 exam goes beyond assessing your theoretical knowledge; it also evaluates your ability to apply data science and machine learning concepts in practical scenarios. It measures your resilience and capacity to stay composed under pressure. The exam emphasizes practical skills rather than memorization of the content found in Microsoft's official documentation and other educational resources.

Starting from June 2, 2023, a new exam curriculum has been introduced for the Microsoft DP-100 exam. This curriculum outlines the key documents, domains, tasks, and enablers that shape the structure of the exam. The Exam Content Outline (ECO) serves as a blueprint for the DP-100 exam. It is a valuable resource for candidates, providing comprehensive details about the topics, activities, and tools covered in the Microsoft DP-100 certification exam. These areas align with the categories, activities, and enablers outlined in the Microsoft Azure Data Science and Machine Learning Best Practice Guide, 6th Edition.

Prepare for Success with Free Microsoft DP-100 Questions and Answers

In this section, we will present 40 free Microsoft DP-100 questions and provide extensive explanations to enhance your knowledge of Microsoft Azure data science and machine learning. Each correct answer will be thoroughly explained to give you a deeper understanding of the concepts. All the questions and answers are contextualized within the framework of the Microsoft DP-100 exam curriculum and the best practices recommended in the Microsoft Azure documentation.

By practising these free Microsoft DP-100 questions, you will not only assess your knowledge but also gain valuable insights into the application of data science and machine learning in real-world scenarios using Microsoft Azure. This will significantly contribute to your data science education and help you excel in the Microsoft DP-100 certification exam.

Are you ready to boost your preparation for the Microsoft DP-100 exam? Let's dive into the free questions and answers to enhance your understanding of Microsoft Azure data science and machine learning.

Case Study: 1

One case study 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 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 nucleus must optimize for similar ad reports.r history.

• Sampling must guarantee mutual and collective exclusivity of 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 boundary 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 catchphrases. 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 an 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 to stow.

•Audio samples show that the length of a catchphrase varies between 25%-47%, depending on the 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.

Question# 1

You need to resolve the local machine learning pipeline performance issue. What should you do?

A. Increase Graphic Processing Units (GPUs).

B. Increase the learning rate.

C. Increase the training iterations, D. Increase Central Processing Units (CPUs).

Question# 2

You need to modify the inputs for the global penalty event model to address the bias and variance issue.

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.

You need to select an environment that will meet the business and data requirements. Which environment should you use?

A. Azure HDInsight with Spark MLlib

B. Azure Cognitive Services

C. Azure Machine Learning Studio

D. Microsoft Machine Learning Server

Answer: D Question:4

You need to define a process for penalty event detection.

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.

You need to define a process for penalty event detection.

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.

You need to define an evaluation strategy for the crowd sentiment models.

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.

Scenario:

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.

Note: Evaluate the changed in correlation between model error rate and centroid distance

In machine learning, a nearest centroid classifier or nearest prototype classifier is a classification model that assigns to observations the label of the class of training samples whose mean (centroid) is closest to the observation.

References:

https://en.wikipedia.org/wiki/Nearest_centroid_classifierhttps://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/sweep-clustering

Question: 7

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.

You need to implement a scaling strategy for the local penalty detection data. Which normalization type should you use?

A. Streaming

B. Weight

C. Batch

D. Cosine

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

Question# 9

You need to use Python language to build a sampling strategy for the global penalty detection models.

How should you complete the code segment? To answer, select the appropriate options in the answer area.

Explanation:

Box 1: import pytorch as deeplearninglib Box 2: ..DistributedSampler(Sampler)..

DistributedSampler(Sampler):

Sampler that restricts data loading to a subset of the dataset.

It is especially useful in conjunction with class:`torch.nn.parallel.DistributedDataParallel`. In such case, each process can pass a DistributedSampler instance as a DataLoader sampler, and load a subset of the original dataset that is exclusive to it.

Scenario: Sampling must guarantee mutual and collective exclusively between local and global segmentation models that share the same features.

Box 3: optimizer = deeplearninglib.train. GradientDescentOptimizer(learning_rate=0.10) Incorrect Answers: ..SGD..

Scenario: All penalty detection models show inference phases using a Stochastic Gradient Descent (SGD) are running too slow.

Box 4: .. nn.parallel.DistributedDataParallel..

DistributedSampler(Sampler): The sampler that restricts data loading to a subset of the dataset. It is especially useful in conjunction with :class:`torch.nn.parallel.DistributedDataParallel`.

References:

https://github.com/pytorch/pytorch/blob/master/torch/utils/data/distributed.py

Question# 10

You need to implement a feature engineering strategy for the crowd sentiment local models. What should you do?

A. Apply an analysis of variance (ANOVA).

B. Apply a Pearson correlation coefficient.

C. Apply a Spearman correlation coefficient.

D. Apply a linear discriminant analysis.

Explanation:

The linear discriminant analysis method works only on continuous variables, not categorical or ordinal variables.

Linear discriminant analysis is similar to analysis of variance (ANOVA) in that it works by comparing the means of the variables.

Scenario:

Data scientists must build notebooks in a local environment using automatic feature engineering and model building in machine learning pipelines.

Experiments for local crowd sentiment models must combine local penalty detection data. All shared features for local models are continuous variables.

Incorrect Answers:

B: The Pearson correlation coefficient, sometimes called Pearson’s R test, is a statistical value that measures the linear relationship between two variables. By examining the coefficient values, you can infer something about the strength of the relationship between the two variables, and whether they are positively correlated or negatively correlated.

C: Spearman’s correlation coefficient is designed for use with non-parametric and non-normally distributed data. Spearman's coefficient is a nonparametric measure of statistical dependence between two variables, and is sometimes denoted by the Greek letter rho. The Spearman’s coefficient expresses the degree to which two variables are monotonically related. It is also called Spearman rank correlation, because it can be used with ordinal variables.

References:

https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/fisher-linear-discriminant-analysis

https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/compute-linear-correlation

Question#11

You need to define a modelling strategy for ad 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.

Step 1: Implement a K-Means Clustering model

Step 2: Use the cluster as a feature in a Decision jungle model.

Decision jungles are non-parametric models, which can represent non-linear decision boundaries. Step 3: Use the raw score as a feature in a Score Matchbox Recommender model

The goal of creating a recommendation system is to recommend one or more "items" to "users" of the system. Examples of an item could be a movie, restaurant, book, or song. A user could be a person, group of persons, or other entity with item preferences.

Scenario:

Ad response rated declined.

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.

Ad response models must support non-linear boundaries of features.

References: https://docs.microsoft.com/en-us/azure/machine-learning/studio-modulereference/multiclassdecision-jungle

https://docs.microsoft.com/en-us/azure/machine-learning/studio-modulereference/scorematchbox-recommender

Question# 12

You need to define an evaluation strategy for the crowd sentiment models.

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.

Step 1: Define a cross-entropy function activation

When using a neural network to perform classification and prediction, it is usually better to use crossentropy error than classification error, and somewhat better to use cross-entropy error than mean squared error to evaluate the quality of the neural network.

Step 2: Add cost functions for each target state.

Step 3: Evaluated the distance error metric.

References: https://www.analyticsvidhya.com/blog/2018/04/fundamentals-deep-learningregularizationtechniques/