SageMaker Built IN Algorithms & Xgboost — MLA-C01 Practice Question
A representative AWS Machine Learning Engineer Associate (MLA-C01) exam question on SageMaker Built IN Algorithms & Xgboost. Work through it below, then read why each option is right or wrong.
Short answer
The correct answer is C. XGBoost.
XGBoost is a gradient-boosted tree algorithm optimized for structured/tabular data, handles missing values natively, trains quickly, and is a SageMaker built-in algorithm requiring minimal custom code — ideal for tabular failure-prediction classification.
The Question
An energy utility wants to predict whether a transformer will fail within the next 30 days using structured sensor readings (voltage, temperature, load) stored in Parquet files. The team wants a fast-to-train, highly accurate model with built-in support for missing values and needs minimal custom code. Which SageMaker built-in algorithm should they choose?
Why C is correct
XGBoost is a gradient-boosted tree algorithm optimized for structured/tabular data, handles missing values natively, trains quickly, and is a SageMaker built-in algorithm requiring minimal custom code — ideal for tabular failure-prediction classification.
Why the other options are wrong
BlazingText is designed for text classification and word embeddings, not structured sensor data.
Random Cut Forest is an unsupervised anomaly detection algorithm; it does not produce a supervised failure-probability prediction from labeled outcomes.
DeepAR is built for time-series forecasting (e.g., predicting future values of a sequence), not binary classification on tabular features.
Key idea: SageMaker Built IN Algorithms & Xgboost
Why C is correct: XGBoost is a gradient-boosted tree algorithm optimized for structured/tabular data, handles missing values natively, trains quickly, and is a SageMaker built-in algorithm requiring minimal custom code — ideal for tabular failure-prediction classification. Why A is wrong: BlazingText is designed for text classification and word embeddings, not structured sensor data. Why B is wrong: Random Cut Forest is an unsupervised anomaly detection algorithm; it does not produce a supervised failure-probability prediction from labeled outcomes. Why D is wrong: DeepAR is built for time-series forecasting (e.g., predicting future values of a sequence), not binary classification on tabular features. On the MLA-C01 exam, questions in the "ML Model Development" domain test whether you can map a scenario's constraints to the right choice. Read the requirement carefully, eliminate options that violate any single constraint, and pick the one that satisfies all of them with the least operational overhead.
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