Microsoft DP-100 Exam Practice Test Instant Access

Question 1

You need to select a pre built development environment for a series of data science experiments. You must use the R language for the experiments.

Which three environments can you use? Each correct answer presents a complete solution. NOTE: Each correct selection is worth one point.

AMI.NET Library on a local environment

BAzure Machine Learning Studio

CData Science Virtual Machine (OSVM)

DAzure Data bricks

EAzure Cognitive Services

Answer : A, B, D

Question 2

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 plan to use a Python script to run an Azure Machine Learning experiment. The script creates a reference to the experiment run context, loads data from a file, identifies the set of unique values for the label column, and completes the experiment run:

from azureml.core import Run

import pandas as pd

run = Run.get_context()

data = pd.read_csv('data.csv')

label_vals = data['label'].unique()

# Add code to record metrics here

run.complete()

The experiment must record the unique labels in the data as metrics for the run that can be reviewed later.

You must add code to the script to record the unique label values as run metrics at the point indicated by the comment.

Solution: Replace the comment with the following code:

run.log_table('Label Values', label_vals)

Does the solution meet the goal?

AYes

BNo

Answer : B

Instead use the run_log function to log the contents in label_vals:

for label_val in label_vals:

run.log('Label Values', label_val)

https://www.element61.be/en/resource/azure-machine-learning-services-complete-toolbox-ai

Question 3

You have an Azure Machine Learning workspace. You plan to tune model hyperparameters by using a sweep job.

You need to find a sampling method that supports early termination of low-performance jobs and continuous hyperpara meters.

Solution: Use the Sobol sampling method over the hyperpara meter space.

Does the solution meet the goal?

AYes

BNo

Answer : B

Question 4

Your team is building a data engineering and data science development environment.

The environment must support the following requirements:

support Python and Scala

compose data storage, movement, and processing services into automated data pipelines

the same tool should be used for the orchestration of both data engineering and data science

support workload isolation and interactive workloads

enable scaling across a cluster of machines

You need to create the environment.

What should you do?

ABuild the environment in Apache Hive for HDInsight and use Azure Data Factory for orchestration.

BBuild the environment in Azure Databricks and use Azure Data Factory for orchestration.

CBuild the environment in Apache Spark for HDInsight and use Azure Container Instances for orchestration.

DBuild the environment in Azure Databricks and use Azure Container Instances for orchestration.

Answer : B

In Azure Databricks, we can create two different types of clusters.

Standard, these are the default clusters and can be used with Python, R, Scala and SQL

High-concurrency

Azure Databricks is fully integrated with Azure Data Factory.

Incorrect Answers:

D: Azure Container Instances is good for development or testing. Not suitable for production workloads.

https://docs.microsoft.com/en-us/azure/architecture/data-guide/technology-choices/data-science-and-machinelearning

Question 5

You have a dataset that includes confidential dat

a. You use the dataset to train a model.

You must use a differential privacy parameter to keep the data of individuals safe and private.

You need to reduce the effect of user data on aggregated results.

What should you do?

ADecrease the value of the epsilon parameter to reduce the amount of noise added to the data

BIncrease the value of the epsilon parameter to decrease privacy and increase accuracy

CDecrease the value of the epsilon parameter to increase privacy and reduce accuracy

DSet the value of the epsilon parameter to 1 to ensure maximum privacy

Answer : C

Differential privacy tries to protect against the possibility that a user can produce an indefinite number of reports to eventually reveal sensitive data. A value known as epsilon measures how noisy, or private, a report is. Epsilon has an inverse relationship to noise or privacy. The lower the epsilon, the more noisy (and private) the data is.

https://docs.microsoft.com/en-us/azure/machine-learning/concept-differential-privacy

Question 6

You are a data scientist working for a hotel booking website company. You use the Azure Machine Learning service to train a model that identifies fraudulent transactions.

You must deploy the model as an Azure Machine Learning real-time web service using the Model.deploy method in the Azure Machine Learning SDK. The deployed web service must return real-time predictions of fraud based on transaction data input.

You need to create the script that is specified as the entry_script parameter for the InferenceConfig class used to deploy the model.

What should the entry script do?

AStart a node on the inference cluster where the web service is deployed.

BRegister the model with appropriate tags and properties.

CCreate a Conda environment for the web service compute and install the necessary Python packages.

DLoad the model and use it to predict labels from input data.

ESpecify the number of cores and the amount of memory required for the inference compute.

Answer : D

The entry script receives data submitted to a deployed web service and passes it to the model. It then takes the response returned by the model and returns that to the client. The script is specific to your model. It must understand the data that the model expects and returns.

The two things you need to accomplish in your entry script are:

Loading your model (using a function called init())

Running your model on input data (using a function called run())

https://docs.microsoft.com/en-us/azure/machine-learning/how-to-deploy-and-where

Question 7

You are conducting feature engineering to prepuce data for further analysis.

The data includes seasonal patterns on inventory requirements.

You need to select the appropriate method to conduct feature engineering on the data.

Which method should you use?

AExponential Smoothing (ETS) function.

BOne Class Support Vector Machine module

CTime Series Anomaly Detection module

DFinite Impulse Response (FIR) Filter module.

Answer : D

Microsoft DP-100 Designing and Implementing a Data Science Solution on Azure Exam Practice Test