MLA-C01 Practice Test 42 — 15 Questions

Question 1

A data engineer is setting up a Glue ETL job to process a large dataset stored in Amazon S3. The job needs to read data in Parquet format, apply a filter, and write the results back to S3 in Parquet. The engineer wants to minimize the cost and runtime. Which optimization technique is MOST effective?

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

Accepted Answer

Use column pruning to read only the columns needed in the transformation.. Column pruning is the most effective optimization because AWS Glue with the Spark engine can push column selection down to the Parquet reader, so only the required columns are read from S3 and processed. Parquet is a columnar format, meaning each column is stored separately, so skipping unneeded columns dramatically reduces I/O, memory footprint, and shuffle volume. This directly lowers DPU-hour consumption and runtime, which are the two cost drivers for Glue ETL jobs.

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Increase the number of DPUs to the maximum allowed.

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Convert the data to JSON format for faster read performance.

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Use a single large file instead of partitioning.

Question 2

An ML team wants to deploy a model that was trained using XGBoost in SageMaker. They want to use the built-in XGBoost algorithm container for inference. Which inference option requires the least custom code?

Accepted Answer

Deploy to a real-time endpoint using the built-in XGBoost container. The built-in XGBoost container in SageMaker is pre-configured with the XGBoost serving stack, including the necessary inference code and dependencies. Deploying a model trained with XGBoost to a real-time endpoint using this container requires no custom inference script or Docker image, only the model artifact and endpoint configuration. This minimizes custom code to just the SageMaker SDK calls for creating the model and endpoint.

Answer

Create a custom Docker container with XGBoost and deploy to an endpoint

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Attach Elastic Inference to a generic container

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Use SageMaker Python SDK to download the model and run local inference

Question 3

A company needs to deploy a new model version to a SageMaker real-time endpoint. They want to route 5% of traffic to the new version initially to monitor for errors before full rollout. Which deployment strategy should they use?

Accepted Answer

Canary deployment with production variants. A canary deployment with production variants allows you to route a specific percentage of traffic (e.g., 5%) to the new model version by adjusting the `InitialVariantWeight` parameter in the production variant configuration. This enables gradual traffic shifting while monitoring errors, and you can later increase the weight to 100% for full rollout. SageMaker real-time endpoints support this natively by hosting multiple model variants behind the same endpoint.

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Blue/green deployment

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Shadow testing

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Multi-model endpoint

Question 4

A data science team deployed a model on Amazon SageMaker and enabled Model Monitor to detect data drift. After a week, they receive alerts indicating that the distribution of a key feature has shifted significantly. However, the model's accuracy on the recent production data remains high. Which action should the team take next?

Accepted Answer

Investigate the root cause of the drift as it may be benign or may lead to future degradation.. Data drift does not always immediately impact model accuracy; the drift may be benign (e.g., a shift in a non-predictive feature) or may indicate a precursor to future degradation. Amazon SageMaker Model Monitor detects distribution shifts using statistical tests like Kolmogorov-Smirnov or Chi-squared, but the team must investigate the root cause—such as changes in data collection, seasonal patterns, or upstream pipeline issues—before taking corrective action. Disabling alerts or retraining blindly could mask underlying problems or waste resources.

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Disable the data drift alert since accuracy is not affected.

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Increase the sample size for monitoring to reduce false positives.

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Retrain the model immediately because data drift always degrades performance.

Question 5

An ML engineer needs to monitor the operational health of a SageMaker endpoint, specifically the time taken for the container to process an inference request and the overhead added by SageMaker. Which two CloudWatch metrics should they examine?

Accepted Answer

ModelLatency and OverheadLatency. ModelLatency is the time taken by the model to respond, and OverheadLatency is the additional time added by SageMaker infrastructure. Invocations is count, not duration; Latency is total latency (ModelLatency + OverheadLatency).

Answer

ModelLatency and 4XXError

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Latency and 5XXError

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Invocations and Latency

Question 6

A machine learning team needs to deploy a new model version for A/B testing, gradually shifting traffic from the old version to the new version over 24 hours. Which deployment strategy should they use?

Accepted Answer

Canary deployment. Canary deployment is the correct strategy because it allows gradual traffic shifting from the old model version to the new one over a specified time period (e.g., 24 hours) while monitoring for errors or performance degradation. This approach minimizes risk by exposing only a small percentage of users to the new version initially, then incrementally increasing traffic as confidence grows, which aligns perfectly with the A/B testing requirement.

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Blue/green deployment

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Shadow testing

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Direct deployment with immediate full traffic

Question 7

A company needs to perform time-series forecasting on historical sales data. Which SageMaker built-in algorithm is BEST suited for this task?

Accepted Answer

DeepAR. DeepAR is a SageMaker built-in algorithm specifically designed for time-series forecasting using recurrent neural networks (RNNs). It is well-suited for predicting future values in a sequence, such as sales data, and can handle multiple related time series. The other algorithms are for classification, regression, or text, not time-series forecasting.

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BlazingText

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Linear Learner

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XGBoost

Question 8

A machine learning engineer is deploying a model to a SageMaker endpoint for real-time inference. The model must return predictions within 100 milliseconds for 95% of requests. The engineer wants to monitor the endpoint's latency and automatically roll back if latency exceeds the threshold. Which combination of SageMaker features should be used?

Accepted Answer

SageMaker deployment guardrails with a blue/green deployment and CloudWatch alarms for latency.. SageMaker deployment guardrails are designed to safely update endpoints with automatic rollback. By associating a CloudWatch alarm that monitors model latency, the guardrail can trigger a rollback if the alarm state changes to ALARM. This provides the required automatic protection. Other features like Model Monitor, Inference Recommender, or Clarify do not offer this latency-based rollback capability out of the box.

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SageMaker Model Monitor with a custom monitoring schedule and AWS Lambda for rollback.

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SageMaker Clarify for bias detection and AWS Step Functions for rollback orchestration.

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SageMaker Inference Recommender to select the optimal instance type and automatic scaling.

Question 9

A company is training a deep learning model using SageMaker and wants to reduce the time spent on data loading from Amazon S3 during training. The training dataset consists of many small files. Which approach is MOST effective to accelerate data loading?

Accepted Answer

Package the small files into a few large files (e.g., TFRecord or RecordIO) and use File mode.. Many small files cause high S3 request overhead. Consolidating them into a few large files in a format like TFRecord or RecordIO reduces the number of requests and allows efficient sequential reads. File mode then downloads these large files quickly to local storage, significantly speeding up data loading compared to streaming many small files or adding instances.

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Use SageMaker Pipe mode to stream data directly from S3.

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Increase the number of training instances in the cluster.

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Enable SageMaker Debugger to monitor data loading metrics.

Question 10

A machine learning engineer is preparing a training dataset stored in Amazon S3 for a SageMaker training job. The data is in CSV format, and the engineer wants to ensure that the training job can access the data efficiently and securely. The S3 bucket is in the same AWS Region as the SageMaker training job. The engineer needs to provide the training job with the necessary permissions to read the data. Which of the following is the MOST secure and appropriate way to grant the training job access to the S3 bucket?

Accepted Answer

Create an IAM role with a policy that allows s3:GetObject on the specific S3 bucket and attach it to the SageMaker training job.. The correct approach is to create an IAM role with a policy that grants s3:GetObject permission on the specific S3 bucket and attach it to the SageMaker training job. This follows the principle of least privilege and allows the training job to access the data securely without embedding credentials. It is the standard method for granting SageMaker training jobs access to S3 data.

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Store AWS credentials (access key and secret key) in the training script and use them to access S3 directly.

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Make the S3 bucket public and allow the training job to read the data without authentication.

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Generate a presigned URL for each object in the S3 bucket and pass the URLs as hyperparameters to the training job.

Question 11

A machine learning engineer is preparing a dataset for training a SageMaker built-in Linear Learner model for binary classification. The dataset contains a highly imbalanced target with only 2% positive examples. They want to improve the model's ability to detect positives without collecting more data. Which SageMaker Linear Learner hyperparameter should they adjust to assign more weight to the positive class?

Accepted Answer

positive_example_weight_mult. SageMaker Linear Learner provides positive_example_weight_mult to scale the contribution of positive examples in the loss. For a binary target with only 2% positives, increasing this multiplier pushes the model to prioritize positive class recall. Other hyperparameters like loss, mini_batch_size, or nonexistent balance_multiplier do not implement class weighting in this algorithm.

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loss

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mini_batch_size

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balance_multiplier

Question 12

A machine learning engineer is training a tabular regression model using the SageMaker built-in XGBoost algorithm. They want to reduce overfitting and improve generalization without changing the algorithm. Which SageMaker hyperparameter should they tune to control the fraction of features randomly sampled per tree?

Accepted Answer

colsample_bytree. The SageMaker built-in XGBoost algorithm exposes colsample_bytree to control column subsampling per tree. Setting it below 1.0 introduces feature-level randomness that combats overfitting and can improve generalization on tabular data. Other hyperparameters such as subsample, eta, and max_depth affect different aspects of training and do not implement the requested feature-sampling behavior.

Answer

subsample

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max_depth

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eta

Question 13

A financial institution is training a fraud detection model using SageMaker. The dataset is highly imbalanced, with only 0.1% fraudulent transactions. The team wants to use SageMaker Automatic Model Tuning to find the best hyperparameters. They notice that the tuning job spends most of its time on configurations that predict all transactions as non-fraudulent. Which hyperparameter should they tune to directly address this issue?

Accepted Answer

scale_pos_weight. The scale_pos_weight hyperparameter in SageMaker's XGBoost algorithm adjusts the weight of the positive class, which is crucial for imbalanced datasets. By increasing this value, the model's loss function penalizes false negatives more, encouraging better detection of fraudulent transactions. This directly tackles the problem of the model predicting all instances as the majority class.

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max_depth

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learning_rate

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subsample

Question 14

A machine learning engineer is preparing a training dataset in Amazon SageMaker for a binary classification model. The dataset is stored as a single CSV file in Amazon S3 and contains 12 categorical features with high cardinality (thousands of unique values each). The engineer wants to avoid the curse of dimensionality and reduce training time while preserving predictive power. Which preprocessing approach should be used with the SageMaker built-in XGBoost algorithm?

Accepted Answer

Use target encoding (mean encoding) for the categorical features, computed within cross-validation folds to prevent leakage.. High-cardinality categorical features require an encoding that avoids exponential feature expansion. Target encoding summarizes each category by its relationship to the target, reducing dimensionality and training time. Performing it within cross-validation folds prevents data leakage, ensuring that validation metrics remain honest. This approach is compatible with the SageMaker built-in XGBoost algorithm, which accepts numerical features and can benefit from the reduced feature space.

Answer

Convert the categorical features to integer codes using LabelEncoder and pass them directly to XGBoost.

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Hash the categorical features into a fixed number of buckets using a hashing trick, then one-hot encode the hashed values.

Answer

Apply one-hot encoding to all categorical features before training.

Question 15

A data scientist is using SageMaker to train a deep learning model with the PyTorch estimator. They want to log custom scalar metrics such as validation accuracy and loss during training so they can monitor the job in SageMaker. Which approach should they use to emit these metrics from the training script?

Accepted Answer

Print the metrics to stdout in a consistent format and define regex patterns in the estimator's metric_definitions parameter.. For SageMaker training jobs, custom metrics are captured by parsing the job's logs. The PyTorch estimator supports metric_definitions, a list of name and regex pairs. Printing metrics to stdout in a consistent format lets SageMaker extract them for monitoring and tuning. Direct API calls or file writes are not the supported mechanism for estimator metric capture.

Answer

Use the sagemaker_metrics API to write metrics to a local file and pass its path to the estimator's metric_definitions.

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Call the SageMaker Metrics API directly from the training container to publish each metric.

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Store metrics in Amazon CloudWatch Logs using the PutMetricData API and then reference them in the estimator.