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A Machine Learning Specialist is using Apache Spark for pre-processing training data As part of the Spark pipeline, the Specialist wants to use Amazon SageMaker for training a model and hosting it Which of the following would the Specialist do to integrate the Spark application with SageMaker? (Select THREE)
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Question 50 - MLS-C01 discussion

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A company supplies wholesale clothing to thousands of retail stores. A data scientist must create a model that predicts the daily sales volume for each item for each store. The data scientist discovers that more than half of the stores have been in business for less than 6 months. Sales data is highly consistent from week to week. Daily data from the database has been aggregated weekly, and weeks with no sales are omitted from the current dataset. Five years (100 MB) of sales data is available in Amazon S3.

Which factors will adversely impact the performance of the forecast model to be developed, and which actions should the data scientist take to mitigate them? (Choose two.)

A.
Detecting seasonality for the majority of stores will be an issue. Request categorical data to relate new stores with similar stores that have more historical data.
Answers
A.
Detecting seasonality for the majority of stores will be an issue. Request categorical data to relate new stores with similar stores that have more historical data.
B.
The sales data does not have enough variance. Request external sales data from other industries to improve the model's ability to generalize.
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B.
The sales data does not have enough variance. Request external sales data from other industries to improve the model's ability to generalize.
C.
Sales data is aggregated by week. Request daily sales data from the source database to enable building a daily model.
Answers
C.
Sales data is aggregated by week. Request daily sales data from the source database to enable building a daily model.
D.
The sales data is missing zero entries for item sales. Request that item sales data from the source database include zero entries to enable building the model.
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D.
The sales data is missing zero entries for item sales. Request that item sales data from the source database include zero entries to enable building the model.
E.
Only 100 MB of sales data is available in Amazon S3.
Answers
E.
Only 100 MB of sales data is available in Amazon S3.
Suggested answer: C, D

Explanation:

Request 10 years of sales data, which would provide 200 MB of training data for the model. The factors that will adversely impact the performance of the forecast model are: Sales data is aggregated by week. This will reduce the granularity and resolution of the data, and make it harder to capture the daily patterns and variations in sales volume. The data scientist should request daily sales data from the source database to enable building a daily model, which will be more accurate and useful for the prediction task. Sales data is missing zero entries for item sales. This will introduce bias and incompleteness in the data, and make it difficult to account for the items that have no demand or are out of stock. The data scientist should request that item sales data from the source database include zero entries to enable building the model, which will be more robust and realistic. The other options are not valid because: Detecting seasonality for the majority of stores will not be an issue, as sales data is highly consistent from week to week. Requesting categorical data to relate new stores with similar stores that have more historical data may not improve the model performance significantly, and may introduce unnecessary complexity and noise. The sales data does not need to have more variance, as it reflects the actual demand and behavior of the customers. Requesting external sales data from other industries will not improve the model's ability to generalize, but may introduce irrelevant and misleading information. Only 100 MB of sales data is not a problem, as it is sufficient to train a forecast model with Amazon S3 and Amazon Forecast. Requesting 10 years of sales data will not provide much benefit, as it may contain outdated and obsolete information that does not reflect the current market trends and customer preferences. References: Amazon Forecast Forecasting: Principles and Practice

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asked 16/09/2024
Venish Arumugam
35 questions
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