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Describe Dynamics 365 Supply Chain ManagementmediumMultiple ChoiceObjective-mapped

MB-920 Describe Dynamics 365 Supply Chain Management Practice Question

Exhibit

"DemandForecast": {
  "ForecastModelId": "DEFAULT",
  "DemandForecastMethod": "ARIMA",
  "HistoricalHorizon": 12
}

Refer to the exhibit. A company uses demand forecasting with ARIMA method and a historical horizon of 12 months. What is the primary advantage of using ARIMA for forecasting?

Answer choices

Why each option matters

Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.

Correct answer & explanation

It models trends and seasonality in time series data

ARIMA (AutoRegressive Integrated Moving Average) is a statistical time series forecasting method that models trends and seasonality in historical data. Option A is wrong because machine learning pattern recognition is not inherent to ARIMA; ARIMA is a statistical method, not a machine learning algorithm. Option B is wrong because ARIMA does not inherently allow real-time forecast updates; it relies on historical data and requires model refitting. Option D is wrong because ARIMA does not incorporate external factors like weather; it only uses the past values of the time series itself.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

  • It uses machine learning for pattern recognition

    Why it's wrong here

    ARIMA is a statistical method, not machine learning.

  • It allows real-time forecast updates

    Why it's wrong here

    ARIMA is based on historical data, not real-time.

  • It models trends and seasonality in time series data

    Why this is correct

    ARIMA is designed to capture trends and seasonality.

  • It incorporates external factors like weather

    Why it's wrong here

    ARIMA only uses historical data.

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