The answer is that classifying text documents using word embeddings is an appropriate use case for Amazon SageMaker built-in algorithms, alongside tabular classification with XGBoost. BlazingText, a built-in algorithm, directly handles text classification by learning word embeddings from raw text, making it ideal for document categorization tasks. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this question tests your ability to distinguish which algorithms are natively available in SageMaker versus those requiring custom code or separate services. A common trap is assuming image classification or time series forecasting are built-in, but custom CNNs are not standard built-in algorithms, and forecasting uses the separate DeepAR algorithm. Remember the memory tip: "BlazingText for text, XGBoost for tables, DeepAR for time, and custom CNNs for images."
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.
Which TWO of the following are appropriate use cases for Amazon SageMaker built-in algorithms?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Classifying customer churn using tabular data
XGBoost is suitable for tabular classification. BlazingText is for text classification on word embeddings. Image classification using custom CNNs may use built-in but not necessarily. Time series forecasting is not a built-in algorithm (use DeepAR). Reinforcement learning is not a built-in algorithm.
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.
✓
Classifying customer churn using tabular data
Why this is correct
XGBoost or Linear Learner can be used.
Related concept
Read the scenario before looking for a memorised answer.
Read the scenario before looking for a memorised answer.
✗
Image classification using a custom CNN architecture
Why it's wrong here
Built-in algorithms do not support custom architectures; use Bring Your Own Model.
✗
Time series forecasting using ARIMA
Why it's wrong here
Built-in algorithm DeepAR is for time series, not ARIMA.
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 MLS-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.
Modeling — This question tests Modeling — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Classifying customer churn using tabular data — XGBoost is suitable for tabular classification. BlazingText is for text classification on word embeddings. Image classification using custom CNNs may use built-in but not necessarily. Time series forecasting is not a built-in algorithm (use DeepAR). Reinforcement learning is not a built-in algorithm.
What should I do if I get this MLS-C01 question wrong?
Identify which MLS-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.
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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
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