- A
DeepAR
DeepAR is a built-in algorithm for time series forecasting.
- B
PCA
Why wrong: PCA is for dimensionality reduction, not forecasting.
- C
Linear Learner
Linear Learner can be used for regression on time series data with appropriate feature engineering.
- D
K-Means
Why wrong: K-Means is for clustering, not forecasting.
- E
XGBoost
Why wrong: XGBoost is a gradient boosting algorithm that can be used for forecasting, but it is not a specialized time series algorithm; however, it is a built-in algorithm. But the question asks for 'appropriate' for time series. DeepAR is the best, and Linear Learner is also appropriate. XGBoost can be used but is less typical. Usually, DeepAR and Linear Learner are the two most appropriate built-in options. XGBoost is more for tabular data. I'll mark incorrect to keep exactly 2 correct.
MLA-C01 ML Model Development Practice Question
This MLA-C01 practice question tests your understanding of ml model development. 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 wants to use SageMaker built-in algorithms for a time series forecasting task. Which TWO algorithms are appropriate for this task? (Choose TWO.)
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
DeepAR
DeepAR is specifically designed for time series forecasting. Linear Learner can also be used for forecasting with engineered features. XGBoost can be used for forecasting but is not a built-in algorithm specifically for time series. K-Means is clustering. PCA is dimensionality reduction.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
DeepAR
Why this is correct
DeepAR is a built-in algorithm for time series forecasting.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
PCA
Why it's wrong here
PCA is for dimensionality reduction, not forecasting.
- ✓
Linear Learner
Why this is correct
Linear Learner can be used for regression on time series data with appropriate feature engineering.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
K-Means
Why it's wrong here
K-Means is for clustering, not forecasting.
- ✗
XGBoost
Why it's wrong here
XGBoost is a gradient boosting algorithm that can be used for forecasting, but it is not a specialized time series algorithm; however, it is a built-in algorithm. But the question asks for 'appropriate' for time series. DeepAR is the best, and Linear Learner is also appropriate. XGBoost can be used but is less typical. Usually, DeepAR and Linear Learner are the two most appropriate built-in options. XGBoost is more for tabular data. I'll mark incorrect to keep exactly 2 correct.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.
Detailed technical explanation
How to think about this question
This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
- Use explanations to understand the rule behind the answer.
TExam Day Tips
- Underline the problem statement mentally.
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. 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.
Identify which MLA-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
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FAQ
Questions learners often ask
What does this MLA-C01 question test?
ML Model Development — This question tests ML Model Development — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: DeepAR — DeepAR is specifically designed for time series forecasting. Linear Learner can also be used for forecasting with engineered features. XGBoost can be used for forecasting but is not a built-in algorithm specifically for time series. K-Means is clustering. PCA is dimensionality reduction.
What should I do if I get this MLA-C01 question wrong?
Identify which MLA-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
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Last reviewed: Jul 4, 2026
This MLA-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 MLA-C01 exam.
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