Exam AWS Certified Machine Learning - Specialty All Questions

View all questions & answers for the AWS Certified Machine Learning - Specialty exam

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.

Show Suggested Answer

Suggested Answer: C 🗳️

by AIWave at March 14, 2024, 5:35 a.m.

Disclaimers:

- ExamTopics website is not related to, affiliated with, endorsed or authorized by Amazon.
- Trademarks, certification & product names are used for reference only and belong to Amazon.

Comments

Submit Cancel

ef12052

4 months, 3 weeks 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

...

MultiCloudIronMan

10 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

9 months, 3 weeks ago

I think its 'C'

upvoted 2 times

...

Peter_Hsieh

1 year, 3 months 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

7 months, 4 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

...

vkbajoria

1 year, 4 months ago

Selected Answer: C

identify if data has any underlying patterns or trends should be the first step

upvoted 1 times

...

AIWave

1 year, 5 months 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

9 months, 3 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

...