The answer is to decrease the window size from 5 to 2. This is correct because BlazingText’s default window size of 5 captures broad context, which is ineffective for short documents averaging 50 words; a smaller window forces the model to learn tight, local word co-occurrences that are far more discriminative for short text classification. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this scenario tests your understanding of how hyperparameters interact with data characteristics—specifically, that window size controls the span of context words, and a mismatch can dilute semantic signals. A common trap is assuming a larger window always improves performance, but for short text, it introduces noise. Remember the memory tip: “Short text, short window”—if your documents are brief, shrink the window to capture only the most relevant local patterns.
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 SageMaker built-in BlazingText algorithm for text classification. The model performance is poor on the validation set. The data consists of short documents (average 50 words). Which hyperparameter tuning strategy is most likely to improve performance?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "most likely"
Why it matters: Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Decrease window size from 5 to 2
BlazingText's default window size of 5 may be too large for short documents (average 50 words), causing the model to learn overly broad context that dilutes local semantic patterns. Decreasing the window size to 2 forces the model to focus on tighter word co-occurrences, which is more effective for short text classification where local n-gram signals are critical.
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.
✗
Increase bucket size from 0 to 1000000
Why it's wrong here
Bucket size is for subword information; not directly relevant for short text performance.
✗
Increase vector dimension from 100 to 300
Why it's wrong here
Increasing dimension may lead to overfitting on short documents without improving performance.
✗
Increase minCount from 1 to 5
Why it's wrong here
Increasing minCount filters out rare words, which may be important for short texts.
✓
Decrease window size from 5 to 2
Why this is correct
Smaller window size captures local context better for short documents.
Clue confirmation
The clue word "most likely" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often assume increasing model capacity (e.g., vector dimension) or filtering rare words (minCount) always helps, but for short documents, the hyperparameter controlling context granularity (window size) is the most impactful lever.
Detailed technical explanation
How to think about this question
BlazingText uses a hierarchical softmax or negative sampling approach where the window size defines the radius of context words for each target word. For short documents, a smaller window (e.g., 2) captures local syntactic patterns like bigrams and trigrams, which are often more predictive for classification than distant dependencies. In production, tuning window size is a common first step when dealing with tweets, headlines, or other brief texts where context is limited.
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.
TExam Day Tips
→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 media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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: Decrease window size from 5 to 2 — BlazingText's default window size of 5 may be too large for short documents (average 50 words), causing the model to learn overly broad context that dilutes local semantic patterns. Decreasing the window size to 2 forces the model to focus on tighter word co-occurrences, which is more effective for short text classification where local n-gram signals are critical.
What should I do if I get this MLS-C01 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Are there clue words in this question I should notice?
Yes — watch for: "most likely". Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
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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