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Exam AWS Certified Machine Learning - Specialty topic 1 question 319 discussion

Exam question from Amazon's AWS Certified Machine Learning - Specialty
Question #: 319
Topic #: 1
[All AWS Certified Machine Learning - Specialty Questions]

A developer at a retail company is creating a daily demand forecasting model. The company stores the historical hourly demand data in an Amazon S3 bucket. However, the historical data does not include demand data for some hours.

The developer wants to verify that an autoregressive integrated moving average (ARIMA) approach will be a suitable model for the use case.

How should the developer verify the suitability of an ARIMA approach?

  • A. Use Amazon SageMaker Data Wrangler. Import the data from Amazon S3. Impute hourly missing data. Perform a Seasonal Trend decomposition.
  • B. Use Amazon SageMaker Autopilot. Create a new experiment that specifies the S3 data location. Choose ARIMA as the machine learning (ML) problem. Check the model performance.
  • C. Use Amazon SageMaker Data Wrangler. Import the data from Amazon S3. Resample data by using the aggregate daily total. Perform a Seasonal Trend decomposition.
  • D. Use Amazon SageMaker Autopilot. Create a new experiment that specifies the S3 data location. Impute missing hourly values. Choose ARIMA as the machine learning (ML) problem. Check the model performance.
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Suggested Answer: C 🗳️
by AIWave at March 14, 2024, 5:35 a.m.
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ef12052
3 weeks, 1 day ago
Selected Answer: A
https://docs.aws.amazon.com/sagemaker/latest/dg/data-wrangler-transform.html#data-wrangler-transform-handle-missing-time-series
upvoted 2 times
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MultiCloudIronMan
6 months ago
Selected Answer: A
Imputing Missing Data: ARIMA models require a complete time series without missing values. Imputing the missing hourly data ensures the dataset is suitable for ARIMA modeling1. Seasonal Trend Decomposition: This step helps in understanding the underlying patterns in the data, such as seasonality and trends, which are crucial for verifying the suitability of ARIMA2.
upvoted 1 times
MultiCloudIronMan
5 months, 3 weeks ago
I think its 'C'
upvoted 2 times
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Peter_Hsieh
11 months, 3 weeks ago
Selected Answer: C
Resample is needed. https://docs.aws.amazon.com/sagemaker/latest/dg/data-wrangler-transform.html#data-wrangler-resample-time-series
upvoted 2 times
LeoD
3 months, 3 weeks ago
https://docs.aws.amazon.com/sagemaker/latest/dg/data-wrangler-transform.html#data-wrangler-transform-handle-missing-time-series Option A
upvoted 1 times
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vkbajoria
1 year ago
Selected Answer: C
identify if data has any underlying patterns or trends should be the first step
upvoted 1 times
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AIWave
1 year, 1 month ago
Selected Answer: C
- Daily aggregation is needed to forecast daily demand and also takes care of missing hourly values. - Seasonal Trend decomposition on the daily aggregated data helps in understanding the underlying patterns, trends, and seasonality, which is essential for determining whether an ARIMA model would be appropriate.
upvoted 1 times
MultiCloudIronMan
5 months, 2 weeks ago
You make a valid point about the importance of daily aggregation and understanding seasonal trends. However, the key issue here is the missing hourly data. By aggregating the data daily, you might lose valuable information that could impact the accuracy of the ARIMA model.
upvoted 1 times
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