Page: 1
/ 8
Total 37 questions
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
You are performing exploratory analysis of files that are encoded in a complex proprietary format. The format requires disk intensive access to several dependent files in HDFS.
You need to build an Azure Machine Learning model by using a canopy clustering algorithm. You must ensure that changes to proprietary file formats can be maintained by using the least amount of effort.
Which Machine Learning library should you use?
- A. MicrosoftML
- B. scikit-learn
- C. SparkR
- D. Mahout
Answer : C
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
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 Margies Travel sells airline tickets to customers in the United
States.
Margies 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 flights 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 :
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 designing an Azure Machine Learning workflow.
You have a dataset that contains two million large digital photographs.
You plan to detect the presence of trees in the photographs.
You need to ensure that your model supports the following:
* Hidden Layers that support a directed graph structure.
* User-defined core components on the GPU
Solution: You create an endpoint to the computer Vision APL
Does this meet the goal?
- A. YES
- B. NO
Answer : B
31 days access 