- A
The evaluation step must be split into two steps: one for evaluation and one for condition check
Why wrong: The condition can be a separate step after evaluation; no need to split further.
- B
The evaluation script outputs the F1 score as a string, and string comparison '0.94' >= '0.95' evaluates to true because it is lexicographically compared
If the F1 score is a string, the comparison may be lexicographic; '0.94' is not >= '0.95' lexicographically, but the actual cause could be that the script outputs the score as a string and the condition fails to parse it as a number, causing unexpected behavior. The most likely fix is to ensure numeric output.
- C
The Condition step cannot be used to check metric values; it can only check step status
Why wrong: Condition steps can evaluate expressions from step outputs, including metrics.
- D
The threshold should be set to 0.95 but the Condition step uses a less than or equal operator
Why wrong: The condition uses >=, which is correct for pass.
MLA-C01 Practice Question: A financial services company has a SageMaker…
This MLA-C01 practice question tests your understanding of mla-c01 exam topics. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. 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 financial services company has a SageMaker pipeline that trains a fraud detection model daily. The pipeline consists of three steps: preprocessing (using a Spark script), training (XGBoost), and evaluation. The evaluation step calculates the F1 score and compares it to a threshold of 0.95. If the F1 score is below 0.95, the pipeline should fail and notify the team via email. The team implemented this using a Condition step that checks if the F1 score is greater than or equal to 0.95. If true, the pipeline proceeds to register the model; if false, the pipeline fails. However, the team notices that even when the F1 score is 0.94, the pipeline continues to the registration step. The evaluation script outputs the F1 score as a float with two decimal places in a JSON file. The Condition step uses the expression: $.evaluation.metrics.f1_score >= 0.95. What is the most likely cause of the issue?
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 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
The evaluation script outputs the F1 score as a string, and string comparison '0.94' >= '0.95' evaluates to true because it is lexicographically compared
The most likely cause is that the evaluation script outputs the F1 score as a string (e.g., '0.94') rather than a numeric value. In AWS SageMaker Pipelines, the Condition step evaluates expressions using JSONPath, and when comparing a string to a numeric literal, both are typically treated as strings for comparison. Lexicographically, the string '0.94' is considered greater than or equal to '0.95' because of the character-by-character comparison, which causes the condition to pass incorrectly when the score is below the threshold.
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.
- ✗
The evaluation step must be split into two steps: one for evaluation and one for condition check
Why it's wrong here
The condition can be a separate step after evaluation; no need to split further.
- ✓
The evaluation script outputs the F1 score as a string, and string comparison '0.94' >= '0.95' evaluates to true because it is lexicographically compared
Why this is correct
If the F1 score is a string, the comparison may be lexicographic; '0.94' is not >= '0.95' lexicographically, but the actual cause could be that the script outputs the score as a string and the condition fails to parse it as a number, causing unexpected behavior. The most likely fix is to ensure numeric output.
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.
- ✗
The Condition step cannot be used to check metric values; it can only check step status
Why it's wrong here
Condition steps can evaluate expressions from step outputs, including metrics.
- ✗
The threshold should be set to 0.95 but the Condition step uses a less than or equal operator
Why it's wrong here
The condition uses >=, which is correct for pass.
Common exam traps
Common exam trap: answer the scenario, not the keyword
AWS often tests the subtle distinction between numeric and string comparisons in AWS Step Functions and SageMaker Pipelines, where candidates assume that a value that looks like a number will be compared numerically, but the actual behavior depends on the data type in the JSON output.
Trap categories for this question
Command / output trap
Condition steps can evaluate expressions from step outputs, including metrics.
Detailed technical explanation
How to think about this question
In SageMaker Pipelines, the Condition step uses JSONPath to extract values from step outputs, and comparisons are type-sensitive. If the output JSON contains a numeric value (e.g., 0.94 without quotes), the comparison is numeric; if it contains a quoted string (e.g., "0.94"), the comparison is lexicographic. Lexicographic comparison of strings works by comparing ASCII values character by character: '0' equals '0', '.' equals '.', then '9' (ASCII 57) vs '5' (ASCII 53), so '9' > '5' makes the entire string "0.94" >= "0.95" evaluate to true. This is a common pitfall when outputs are serialized as strings in JSON files.
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 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 exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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FAQ
Questions learners often ask
What does this MLA-C01 question test?
Read the scenario before looking for a memorised answer.
What is the correct answer to this question?
The correct answer is: The evaluation script outputs the F1 score as a string, and string comparison '0.94' >= '0.95' evaluates to true because it is lexicographically compared — The most likely cause is that the evaluation script outputs the F1 score as a string (e.g., '0.94') rather than a numeric value. In AWS SageMaker Pipelines, the Condition step evaluates expressions using JSONPath, and when comparing a string to a numeric literal, both are typically treated as strings for comparison. Lexicographically, the string '0.94' is considered greater than or equal to '0.95' because of the character-by-character comparison, which causes the condition to pass incorrectly when the score is below the threshold.
What should I do if I get this MLA-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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Last reviewed: Jun 30, 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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