70-774 Exam
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Q1. Note: This question is part of a series of questions that use the same scenario. For your convenience, the scenario is repeated in each question. Each question presents a different goal and answer choices, but the text of the scenario is exactly the same in each question in this series.
Start of repeated scenario
You plan to create a predictive analytics solution for credit risk assessment and fraud prediction in Azure Machine Learning. The Machine Learning workspace for the solution will be shared with other users in your organization. You will add assets to projects and conduct experiments in the workspace.
The experiments will be used for training models that will be published to provide scoring from web services.
The experiment tor fraud prediction will use Machine Learning modules and APIs to train the models and will predict probabilities in an Apache Hadoop ecosystem.
End of repeated scenario.
You need to alter the list of columns that will be used for predicting fraud for an input web service endpoint. The columns from the original data source must be retained while running the Machine Learning experiment.
Which module should you add after the web service input module and before the prediction module?
A. Edit Metadata
B. Import Data
C. SMOTE
D. Select Columns in Dataset
Answer: A
Q2. You are analyzing taxi trips in New York City. You leverage the Azure Data Factory to create data pipelines and to orchestrate data movement.
You plan to develop a predictive model for 170 million rows (37 GB) of raw data in Apache Hive by using Microsoft R Serve to identify which factors contributes to the passenger tipping behavior.
All of the platforms that are used for the analysis are the same. Each worker node has eight processor cores and 28 GB Of memory.
Which type of Azure HDInsight cluster should you use to produce results as quickly as possible?
A. Hadoop
B. HBase
C. Interactive Hive
D. Spark
Answer: A
Q3. Note: This question is part of a series of questions that use the same scenario. For your convenience, the scenario is repeated in each question. Each question presents a different goal and answer choices, but the text of the scenario is exactly the same in each question in this series.
Start of repeated scenario
You plan to create a predictive analytics solution for credit risk assessment and fraud prediction in Azure Machine Learning. The Machine Learning workspace for the solution will be shared with other users in your organization. You will add assets to projects and conduct experiments in the workspace.
The experiments will be used for training models that will be published to provide scoring from web services.
The experiment tor fraud prediction will use Machine Learning modules and APIs to train the models and will predict probabilities in an Apache Hadoop ecosystem.
End of repeated scenario.
You need to alter the list of columns that will be used for predicting fraud for an input web service endpoint. The columns from the original data source must be retained while running the Machine Learning experiment.
Which module should you add after the web service input module and before the prediction module?
A. Edit Metadata
B. Import Data
C. SMOTE
D. Select Columns in Dataset
Answer: A
Q4. Note: This question is part of a series of questions that present the same Scenario. Each question I 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 correct solution.
You are working on an Azure Machine Learning Experiment. You have the dataset configured as shown in the following table:
You need to ensure that you can compare the performance of the models and add annotations to the results.
Solution: You connect the Score Model modules from each trained model as inputs for the Evaluate Model module, and then save the result as a dataset.
Does this meet the goal?
A. YES
B. NO
Answer: A
Q5. Note: This question is part of a series of questions that present the same Scenario. Each question I 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 correct solution.
You are working on an Azure Machine Learning Experiment. You have the dataset configured as shown in the following table:
You need to ensure that you can compare the performance of the models and add annotations to the results.
Solution: You connect the Score Model modules from each trained model as inputs for the Evaluate Model module, and then save the result as a dataset.
Does this meet the goal?
A. YES
B. NO
Answer: A
Q6. You have data about the following:
* Users
* Movies
* User ratings of the movies
You need to predict whether a user will like a particular movie. Which Matchbox recommender should you use?
A. Item Recommendation
B. Related items
C. Rating Prediction
D. Related Users.
Answer: B
Q7. Note: This question is part of a series of questions that use the same scenario. For your convenience, the scenario is repeated in each question. Each question presents a different goal and answer choices, but the text of the scenario is exactly the same in each question in this series.
Start of repeated scenario
You plan to create a predictive analytics solution for credit risk assessment and fraud prediction in Azure Machine Learning. The Machine Learning workspace for the solution will be shared with other users in your organization. You will add assets to projects and conduct experiments in the workspace.
The experiments will be used for training models that will be published to provide scoring from web services.
The experiment tor fraud prediction will use Machine Learning modules and APIs to train the models and will predict probabilities in an Apache Hadoop ecosystem.
End of repeated scenario.
You plan to configure the resources for part of a workflow that will be used to preprocess data from files stored in Azure Blob storage. You plan to use Python to preprocess and store the data in Hadoop.
You need to get the data into Hadoop as quickly as possible.
Which three actions should you perform? Each correct answer presents pan of the solution.
NOTE: Each correct selection is worth one point.
A. Create an Azure virtual machine (VM), and then configure MapReduce on the VM.
B. Create an Azure HDInsight Hadoop cluster.
C. Create an Azure virtual machine (VM), and then install an IPython Notebook server.
D. Process the files by using Python to store the data to a Hadoop instance.
E. Create the Machine Learning experiment, and then add an Execute Python Script module.
Answer: A,C,E
Q8. DRAG DROP
Note: This question is part of a series of questions that present the same Scenario. Each question I 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 correct solution.
Start of repeated Scenario:
A Travel agency named Margie’s Travel sells airline tickets to customers in the United States.
Margie’s Travel wants you to provide insights and predictions on flight delays. The agency
is considering implementing a system that will communicate to its customers as the flight departure near about possible delays due to weather conditions.
The flight data contains the following attributes:
* DepartureDate: The departure date aggregated at a per hour granularity.
* Carrier: The code assigned by the IATA and commonly used to identify a carrier.
* OriginAirportID: An identification number assigned by the USDOT to identify a unique airport (the flight’s Origin)
* DestAirportID: The departure delay in minutes.
*DepDet30: A Boolean value indicating whether the departure was delayed by 30 minutes or more ( a value of 1 indicates that the departure was delayed by 30 minutes or more)
The weather data contains the following Attributes: AirportID, ReadingDate (YYYY/MM/DD HH), SKYConditionVisibility, WeatherType, Windspeed, StationPressure, PressureChange and HourlyPrecip.
End of repeated Scenario:
You need to remove the bias and to identify the columns in the input dataset that have the greatest predictive power.
Which module should you use for each requirement? To answer drag the appropriate modules to the correct requirements.
Answer:
Q9. Note: This question is part of a series of questions that present the same Scenario. Each question I 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 correct solution.
You are working on an Azure Machine Learning Experiment. You have the dataset configured as shown in the following table:
You need to ensure that you can compare the performance of the models and add annotations to the results.
Solution: You save the output of the Score Model modules as a combined set, and then use the Project Columns modules to select the MAE.
Does this meet the goal?
A. YES
B. NO
Answer: A
Q10. You have the following three training datasets for a restaurant:
* User Feature
* Item feature
* Ratings of items by users
You must recommend restaurants to a particular user based only on the users features. You need to use a Matchbox Recommender to make recommendations.
How many input parameters should you specify?
A. 1
B. 2
C. 3
D. 4
Answer: D