The answer is to create a separate forecasting model specifically for the intermittent demand products. This is correct because DeepAR, while powerful for smooth or seasonal time series, struggles with sporadic sales patterns where zeros dominate; it assumes a continuous distribution that cannot effectively model the "demand vs. non-demand" probability inherent in intermittent data. By isolating these products and applying a specialized method like Croston’s, which separately models the inter-demand interval and the demand size, you directly address the structural mismatch. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this question tests your understanding that one algorithm does not fit all data patterns—a common trap is assuming more data or longer training will fix a distributional problem. Remember the memory tip: "If demand is a ghost, use Croston’s host"—meaning for sporadic (ghost-like) demand, switch to a model built for that rhythm.
MLS-C01 Modeling Practice Question
This MLS-C01 practice question tests your understanding of modeling. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
A company uses Amazon SageMaker to train a time-series forecasting model using the built-in DeepAR algorithm. The training data consists of daily sales for 1000 products over 2 years. The model performs well on most products, but for a few products with intermittent demand (sporadic sales), the predictions are poor. Which action should the data scientist take to improve predictions for these products?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Create a separate forecasting model specifically for intermittent demand products, using a model designed for such patterns (e.g., Croston's method).
Option A is correct. Creating separate models for different demand patterns allows specialized treatment. Option B is wrong because the dataset is already long enough. Option C is wrong because using a linear model may underfit. Option D is wrong because increasing training data does not help with intermittent patterns.
Key principle: NAT direction and interface roles matter as much as the IP address mapping. Inside/outside designation controls which traffic is translated.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
✓
Create a separate forecasting model specifically for intermittent demand products, using a model designed for such patterns (e.g., Croston's method).
Why this is correct
Intermittent demand requires specialized models like Croston's method or TSB.
Related concept
Static NAT maps one inside address to one outside address.
✗
Use a linear regression model for all products.
Why it's wrong here
Linear regression is unlikely to capture the complex patterns of intermittent demand.
✗
Increase the context length of the DeepAR model to capture longer history.
Why it's wrong here
More history may not help because the demand is sporadic, not dependent on long patterns.
✗
Add more training data by including additional product categories.
Why it's wrong here
More data does not address the fundamental issue of intermittent demand behavior.
Common exam traps
Common exam trap: NAT rules depend on direction and matching traffic
NAT is not only about the public address. The inside/outside interface roles and the ACL or rule that matches traffic are just as important.
Detailed technical explanation
How to think about this question
NAT questions usually test address translation, overload/PAT behaviour, static mappings and whether the right traffic is being translated. Read the interface direction and address terms carefully.
KKey Concepts to Remember
Static NAT maps one inside address to one outside address.
PAT allows many inside hosts to share one public address using ports.
Inside local and inside global describe the private and translated addresses.
NAT ACLs identify traffic for translation, not always security filtering.
TExam Day Tips
→Identify inside and outside interfaces first.
→Check whether the scenario needs static NAT, dynamic NAT or PAT.
→Do not confuse NAT matching ACLs with normal packet-filtering intent.
Key takeaway
NAT direction and interface roles matter as much as the IP address mapping. Inside/outside designation controls which traffic is translated.
Real-world example
How this comes up in practice
A cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. NAT direction and interface roles matter as much as the IP address mapping. Inside/outside designation controls which traffic is translated. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
What to study next
Got this wrong? Here's your next step.
Review the four NAT address types (inside local, inside global, outside local, outside global), PAT port overload, and static vs dynamic NAT use cases. Then practise related MLS-C01 NAT questions on configuration and troubleshooting.
Modeling — This question tests Modeling — Static NAT maps one inside address to one outside address..
What is the correct answer to this question?
The correct answer is: Create a separate forecasting model specifically for intermittent demand products, using a model designed for such patterns (e.g., Croston's method). — Option A is correct. Creating separate models for different demand patterns allows specialized treatment. Option B is wrong because the dataset is already long enough. Option C is wrong because using a linear model may underfit. Option D is wrong because increasing training data does not help with intermittent patterns.
What should I do if I get this MLS-C01 question wrong?
Review the four NAT address types (inside local, inside global, outside local, outside global), PAT port overload, and static vs dynamic NAT use cases. Then practise related MLS-C01 NAT questions on configuration and troubleshooting.
What is the key concept behind this question?
Static NAT maps one inside address to one outside address.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
This MLS-C01 practice question is part of Courseiva's free Amazon Web Services certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the MLS-C01 exam.
Question Discussion
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
Sign in to join the discussion.