MLA-C01 Practice Test 4 — 10 Questions

Question 1

A machine learning team trains a model in SageMaker and wants to track every step — from dataset version to hyperparameters to final model artifact — for reproducibility and audit compliance. Which SageMaker feature should they use?

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

SageMaker ML Lineage Tracking. SageMaker ML Lineage Tracking is the correct choice because it is specifically designed to create a directed acyclic graph (DAG) of every step in the ML workflow, including dataset versions, hyperparameters, training jobs, and model artifacts. This enables full reproducibility and audit compliance by capturing the provenance of each entity and their relationships, which is exactly what the question requires.

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SageMaker Feature Store

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SageMaker Experiments

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SageMaker Model Registry

Question 2

A financial services company needs to deploy a machine learning model for real-time fraud detection. The model must be highly available across multiple Availability Zones and must support automatic scaling based on request volume. The company also needs to perform canary deployments to test new model versions with a small percentage of traffic before full rollout. Which SageMaker feature should they use?

Accepted Answer

SageMaker real-time endpoint with production variants. SageMaker real-time endpoints with production variants enable canary deployments by routing a small percentage of traffic to a new model version while the majority goes to the current version. This feature also supports multi-AZ deployment for high availability and automatic scaling based on request volume via Application Auto Scaling, meeting all the stated requirements.

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SageMaker Multi-Model Endpoint

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SageMaker Batch Transform

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SageMaker Serverless Inference

Question 3

A data science team has trained a PyTorch model for real-time inference and needs to deploy it on AWS with GPU acceleration while minimizing cold-start latency. Which SageMaker inference option should they choose?

Accepted Answer

Real-time endpoint with ml.g4dn instance. Real-time endpoints with GPU instances (e.g., ml.g4dn) provide low latency and support GPU acceleration, suitable for interactive inference. Serverless inference does not support GPU instances, asynchronous inference is for non-real-time, and batch transform is for offline predictions.

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Serverless inference

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Batch transform

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Asynchronous inference endpoint

Question 4

A financial services firm is training a fraud detection model using SageMaker. The dataset is highly imbalanced (0.1% fraudulent transactions). The model currently achieves 99.9% accuracy but only catches 5% of fraud cases. Which metric should the team prioritize to evaluate model performance?

Accepted Answer

Recall. Recall is the proportion of actual fraud cases that the model correctly identifies, so it directly measures the model's ability to catch fraud. With only 5% of fraud cases caught, recall is extremely low, and improving it is the priority. Accuracy is misleading here because a model that predicts 'not fraud' for every transaction would still achieve 99.9% accuracy on a 0.1% fraud dataset.

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Accuracy

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Precision

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F1-score

Question 5

A machine learning team needs to deploy a model that was built using scikit-learn. They want to use SageMaker for hosting. Which approach should they take?

Accepted Answer

Package the model artifacts and use the SageMaker built-in scikit-learn container for inference. SageMaker provides a pre-built, optimized Docker container for scikit-learn that supports inference. By packaging the model artifacts (e.g., a joblib or pickle file) and deploying them using the built-in container, the team avoids the overhead of custom container creation while ensuring compatibility with SageMaker's hosting infrastructure, including automatic scaling and load balancing.

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Create a Jupyter notebook that loads the model and runs predictions on the SageMaker notebook instance

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Create a custom Docker container with scikit-learn and deploy it on SageMaker

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Launch a SageMaker training job with the model and use the training instance as an endpoint

Question 6

A data scientist is training a binary classifier on a highly imbalanced dataset (1:100 class ratio). The dataset contains 500,000 rows and 30 features. The data is stored in S3 in Parquet format. The data scientist wants to use SageMaker's built-in XGBoost algorithm. Which data preparation technique should the data scientist apply to best address the class imbalance without causing data leakage?

Accepted Answer

Use the scale_pos_weight parameter in XGBoost to assign higher weight to the minority class.. The scale_pos_weight parameter in XGBoost directly adjusts the loss function to penalize misclassifications of the minority class more heavily, effectively handling class imbalance without modifying the dataset. This avoids data leakage because the weighting is applied during training only, not during preprocessing, and does not involve any synthetic data generation or resampling that could inadvertently expose test information.

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Undersample the majority class to create a balanced dataset, then split.

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Oversample the minority class using SMOTE on the entire dataset before splitting into train/validation sets.

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Randomly oversample the minority class by duplicating rows, then perform stratified train/test split.

Question 7

A healthcare company is building a model to predict patient readmission rates. The dataset contains a mix of numeric features (age, blood pressure, lab test results) and categorical features (gender, diagnosis code, hospital department). The dataset has 2 million rows. The data is stored in an Amazon S3 bucket, and they use AWS Glue to catalog and preprocess the data. The data scientist notices that the 'diagnosis_code' column has 10,000 unique codes, and 20% of the rows have missing values for 'blood_pressure'. They plan to use a SageMaker built-in XGBoost model. For optimal model performance, which preprocessing steps should they apply using AWS Glue ETL?

Accepted Answer

Impute missing 'blood_pressure' with median, and apply integer encoding to 'diagnosis_code'.. XGBoost handles missing values natively, so median imputation for 'blood_pressure' is robust to outliers and preserves data distribution, while integer encoding (label encoding) for 'diagnosis_code' with 10,000 unique values is efficient and avoids the dimensionality explosion of one-hot encoding. AWS Glue ETL can apply these transformations using built-in functions like `Imputer` and `StringIndexer` without excessive memory overhead.

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Impute missing 'blood_pressure' with the mean, and apply label encoding to 'diagnosis_code'.

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Replace missing 'blood_pressure' with -1 and apply one-hot encoding to 'diagnosis_code' after grouping rare codes into 'other'.

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Apply one-hot encoding to 'diagnosis_code' and drop rows with missing 'blood_pressure'.

Question 8

A company has deployed a machine learning model on Amazon SageMaker and wants to automatically detect when the distribution of input features deviates significantly from the training data distribution. Which SageMaker feature should they use?

Accepted Answer

SageMaker Model Monitor – Data Quality Monitoring. SageMaker Model Monitor – Data Quality Monitoring is the correct choice because it is specifically designed to detect deviations in the distribution of input features compared to the training data distribution. It continuously monitors incoming inference requests and compares statistical properties (e.g., mean, variance, or histogram) against a baseline computed from the training dataset, alerting when drift is detected.

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SageMaker Clarify

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SageMaker Edge Manager

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SageMaker Model Monitor – Model Quality Monitoring

Question 9

A data engineer is building a data pipeline for a machine learning model that requires both structured and unstructured data. The structured data (customer demographics) is in Amazon RDS, and the unstructured data (customer support chat logs) is in Amazon S3 as JSON files. The engineer needs to combine these datasets into a single training dataset stored in S3 in Parquet format. They must also perform feature engineering such as text vectorization on the chat logs. The pipeline should be serverless and cost-effective. Which approach should they use?

Accepted Answer

Use AWS Glue ETL with a Spark script that reads from RDS (via JDBC) and S3, performs transformations, and writes Parquet.. AWS Glue ETL with a Spark script is the correct choice because it natively supports reading from both Amazon RDS (via JDBC) and Amazon S3 (JSON), performing complex transformations like text vectorization, and writing the output as Parquet. Glue is serverless, cost-effective (pay per DPU-hour), and fully managed, making it ideal for batch ETL pipelines that combine structured and unstructured data for ML training.

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Use a SageMaker Processing job with a custom Python script that reads from both sources and writes to S3.

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Use Amazon Athena to join the data from RDS and S3, then export the results as Parquet.

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Use Amazon Kinesis Data Analytics to read from RDS and S3 and produce a continuous stream of processed data.

Question 10

A data science team uses Amazon SageMaker Model Monitor to detect data drift in production. They notice that the schema of incoming data (number of features) has changed compared to the training baseline. Which type of monitor is BEST suited to detect this issue?

Accepted Answer

Data quality monitor. The Data quality monitor in SageMaker Model Monitor is specifically designed to detect violations in the input data schema, such as changes in the number of features, feature types, or missing values, by comparing incoming data against a baseline computed from the training dataset. Since the issue is a structural change in the schema (number of features), the Data quality monitor is the correct choice.

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Bias drift monitor

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Feature attribution drift monitor

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Model quality monitor