MLA-C01 Practice Test 18 — 15 Questions

Question 1

A company wants to serve a large ensemble of models using NVIDIA Triton Inference Server on SageMaker for high throughput GPU inference. Which SageMaker inference option supports this?

Accepted Answer

Real-time endpoint with a custom container running Triton. NVIDIA Triton Inference Server is a custom inference server that supports multiple frameworks, model ensembles, and dynamic batching. To use it on SageMaker, you deploy a real-time endpoint with a custom container that runs Triton, which supports GPU inference and high throughput. This is the only option that explicitly supports Triton.

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Asynchronous Inference

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Multi-model endpoint

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Serverless Inference

Question 2

A data scientist is building a binary classification model on a highly imbalanced dataset where the positive class represents only 1% of the data. The scientist needs to train the model using Amazon SageMaker's built-in XGBoost algorithm. Which strategy should be used to address the class imbalance?

Accepted Answer

Set the `scale_pos_weight` hyperparameter to `sum(negative cases) / sum(positive cases)`. XGBoost's `scale_pos_weight` hyperparameter is specifically designed to handle class imbalance by adjusting the weight of the positive class during training. Setting it to `sum(negative cases) / sum(positive cases)` (i.e., 99/1 = 99) tells the algorithm to penalize misclassifications of the minority class more heavily, effectively balancing the gradient updates. This is the recommended approach for built-in XGBoost in SageMaker, as it directly modifies the loss function without altering the dataset.

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Undersample the majority class until the dataset is balanced

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Use the `max_delta_step` hyperparameter to increase the learning rate for the majority class

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Use SMOTE to oversample the minority class before passing the data to XGBoost

Question 3

A company uses SageMaker Inference Recommender to select the optimal endpoint configuration. After running the recommender, they receive a recommendation for a specific instance type and initial instance count. What should they do next to optimize costs over time?

Accepted Answer

Set up auto-scaling with a target tracking policy based on the recommended metric. Inference Recommender provides an initial right-sized instance type and count based on load testing, but traffic varies over time. Setting up auto-scaling with a target tracking policy (e.g., on InvocationsPerInstance or CPU utilization) lets the endpoint scale in during low traffic and out during peaks, optimizing cost continuously rather than paying for a static over-provisioned fleet.

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Use the recommended configuration without changes, as it is already optimal

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Purchase a Savings Plan for the recommended instance type to reduce hourly cost

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Manually adjust the instance count daily based on observed traffic

Question 4

A team is monitoring a SageMaker endpoint and notices that the average latency (ModelLatency) is increasing over time, but the number of invocations is steady. They suspect that the model's inference code is becoming slower due to memory leaks. Which metric should they also examine to confirm this hypothesis?

Accepted Answer

MemoryUtilization metric (if custom). MemoryUtilization is the correct metric to examine because memory leaks in the inference code cause gradual memory consumption, which can lead to increased latency due to swapping or garbage collection overhead. Since the number of invocations is steady, the rising latency is likely due to internal resource exhaustion rather than increased load. Monitoring MemoryUtilization (if custom) directly confirms whether memory is growing over time, supporting the hypothesis.

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Invocations metric

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OverheadLatency metric

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5XXError metric

Question 5

A team is training a large deep learning model on SageMaker using a single ml.p3.16xlarge instance. Training is taking too long. They want to reduce time by distributing across multiple GPUs but are constrained by model size that does not fit in a single GPU memory. Which distributed training strategy should they use?

Accepted Answer

Model parallelism using SageMaker distributed model parallelism. Model parallelism splits the model across multiple GPUs, which is needed when the model does not fit in a single GPU. Data parallelism replicates the model on each GPU and splits data, which requires the model to fit in each GPU's memory.

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Data parallelism using SageMaker distributed data parallelism

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Switch to a smaller instance type and use horizontal scaling

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Use multiple training jobs with hyperparameter tuning

Question 6

A company wants to deploy a model using a serverless inference endpoint that can automatically scale to zero when not in use and has a configurable maximum concurrency. Which SageMaker inference option meets these requirements?

Accepted Answer

Serverless inference. SageMaker Serverless Inference is the correct choice because it automatically scales to zero when the endpoint is idle, eliminating costs during periods of no traffic, and it allows you to configure a maximum concurrency limit per endpoint to control throughput. This fully managed, pay-per-invoke option is designed for workloads with intermittent or unpredictable traffic patterns, meeting both requirements precisely.

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Real-time endpoint with auto-scaling

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Batch transform

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Asynchronous inference

Question 7

An ML engineer is fine-tuning a large language model using LoRA on SageMaker. The training is converging slowly, and GPU utilization is low. The engineer suspects the bottleneck is data loading. Which action should the engineer take to improve GPU utilization?

Accepted Answer

Use SageMaker Pipe mode to stream data from S3 directly to the training instances. SageMaker Pipe mode streams training data directly from Amazon S3 to the training container over a high-throughput channel, bypassing the local disk and the download-then-read pattern of File mode. This removes the I/O bottleneck that starves the GPU when the dataset is large or when many small files cause slow reads. With faster data delivery, the GPU spends less time idle waiting for batches, raising utilization and speeding convergence.

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Increase the batch size to maximize GPU memory usage

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Enable checkpointing and use spot instances

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Reduce model parallelism to decrease communication overhead

Question 8

A company wants to implement a retraining pipeline that automatically triggers when SageMaker Model Monitor detects data drift. The retraining job should use the latest approved pipeline version in SageMaker Pipelines. Which approach meets these requirements?

Accepted Answer

Create an EventBridge rule that listens for SageMaker Model Monitor violation events and triggers a Lambda function that starts the pipeline. It uses an EventBridge rule to listen for SageMaker Model Monitor violation events (e.g., `aws.sagemaker.model-monitoring-violation`), which then triggers a Lambda function that starts the latest approved pipeline version in SageMaker Pipelines. This creates an automated, event-driven retraining pipeline without manual intervention or scheduled polling.

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Use a scheduled EventBridge rule to run the pipeline every day

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Use SageMaker Model Monitor to update the model registry and trigger a deployment

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Configure SageMaker Model Monitor to directly invoke a Lambda function on violation

Question 9

A machine learning engineer needs to prepare a dataset containing customer transactions for training a fraud detection model. The dataset includes features such as transaction amount, timestamp, merchant category, and customer ID. The engineer wants to create a feature representing the average transaction amount per customer over the last 7 days. Which approach should be used in Amazon SageMaker Data Wrangler?

Accepted Answer

Write a custom PySpark SQL query in a SQL transform that uses the `AVG` window function partitioned by customer ID and ordered by timestamp with a range between 7 days preceding and current row. A rolling 7-day average per customer requires a windowed aggregation over time, which in SageMaker Data Wrangler is best expressed via a SQL transform using the AVG window function with PARTITION BY customer_id and an ORDER BY timestamp with a RANGE frame of '7 days PRECEDING'. This computes, for each transaction, the average amount across that customer's transactions in the trailing 7-day window — exactly the time-aware feature required.

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Export the data to Amazon SageMaker Feature Store and use point-in-time queries with a 7-day lookback

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Use the built-in 'Aggregate' transform with a group-by on customer ID and average of transaction amount

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Use the 'Handle Missing' transform to fill missing values with the mean transaction amount

Question 10

A machine learning engineer needs to ingest streaming data from thousands of IoT devices into Amazon S3 for batch training. The data should be available in S3 within minutes of arrival. Which combination of services should the engineer use?

Accepted Answer

Amazon Kinesis Data Streams and Amazon Kinesis Data Firehose. Amazon Kinesis Data Streams ingests and stores streaming data from thousands of IoT devices durably, while Amazon Kinesis Data Firehose automatically delivers that data to Amazon S3 with near-real-time latency (typically 60–90 seconds). This combination provides the required buffering, scaling, and direct S3 integration without custom code, meeting the 'within minutes' requirement for batch training data.

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AWS IoT Core and Amazon DynamoDB Streams

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Amazon SQS and AWS Lambda

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Amazon Kinesis Data Analytics and AWS Glue ETL

Question 11

A company has deployed a SageMaker real-time endpoint for a model that predicts customer churn. The endpoint uses a single ml.m5.large instance. After deployment, the team notices that during peak hours, the endpoint returns 5xx errors for about 20% of requests. The endpoint has not been configured with any scaling policy. The team needs to resolve this issue with minimal cost increase. Which solution should the team implement?

Accepted Answer

Enable Auto Scaling for the endpoint with a target tracking policy based on the average InvocationsPerInstance metric.. Enabling Auto Scaling with a target tracking policy based on the average InvocationsPerInstance metric dynamically adjusts the number of instances in response to traffic spikes, preventing 5xx errors during peak hours without over-provisioning. This approach minimizes cost by scaling only when needed, unlike manual instance upgrades or batch transforms that either increase baseline cost or introduce latency.

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Deploy the model to a multi-model endpoint to reduce resource utilization.

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Increase the instance type to ml.m5.xlarge to handle more concurrent requests.

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Use SageMaker batch transform instead of real-time inference to process peak traffic asynchronously.

Question 12

A data scientist wants to use SageMaker Clarify to analyze bias during training of a binary classification model. Which TWO types of bias metrics can SageMaker Clarify compute? (Select TWO.)

Accepted Answer

Post-training bias metrics (e.g., Difference in Positive Proportions, AD). SageMaker Clarify computes bias metrics in two distinct phases of the ML lifecycle, and both are valid answers here. Option B is correct because post-training bias metrics evaluate the model's predictions after training, using measures such as Difference in Positive Proportions in Predicted Labels (DPPL), Accuracy Difference (AD), and Disparate Impact, which quantify whether outcomes differ across groups. Option D is correct because pre-training bias metrics analyze the training data before any model is built, using measures such as Class Imbalance (CI), Difference in Proportions of Labels (DPL), and Label Imbalance to detect skew in the dataset itself. Option A is not a bias metric category — feature importance (e.g., via SHAP) explains which features drive predictions, not whether outcomes are biased. Option C is likewise an explainability technique, not a bias metric, and Clarify reports SHAP values under its explainability analysis. Option E is a standard model evaluation artifact for classification performance, not a bias metric computed by Clarify.

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Feature importance

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SHAP values

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Confusion matrix

Question 13

A data scientist is working with a dataset containing a categorical feature 'country' with 200 unique values. They plan to use a linear regression model. Which encoding method is most suitable to avoid the dummy variable trap while maintaining interpretability?

Accepted Answer

One-hot encoding with dropping the first category. One-hot encoding with dropping the first category is the most suitable method because it creates binary columns for each category except one, avoiding perfect multicollinearity (the dummy variable trap) while preserving interpretability. Each coefficient directly represents the effect of that category relative to the dropped reference category, which is intuitive for linear regression.

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Binary encoding

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Target encoding

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Label encoding

Question 14

Which SageMaker feature provides AutoML capabilities, including automatic data preprocessing, model selection, and hyperparameter tuning?

Accepted Answer

SageMaker Autopilot. SageMaker Autopilot is the only SageMaker feature that provides end-to-end AutoML: it automatically inspects and preprocesses raw tabular data, explores candidate algorithms, performs hyperparameter tuning, and selects the best model. It also generates explainability reports and notebooks so users can understand and reproduce the pipeline. The other options are narrower tools that address only one part of the ML lifecycle.

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SageMaker Data Wrangler

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SageMaker Automatic Model Tuning

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SageMaker Experiments

Question 15

An ML engineer wants to use MLflow on SageMaker to track experiments and log metrics. They have set up MLflow on an EC2 instance. How can they best integrate MLflow tracking with SageMaker training jobs?

Accepted Answer

Set the MLFLOW_TRACKING_URI environment variable in the training job and use the mlflow library in the training script. The ML engineer can set the `MLFLOW_TRACKING_URI` environment variable in the SageMaker training job definition and use the `mlflow` library inside the training script to log parameters, metrics, and artifacts directly to the MLflow tracking server running on the EC2 instance. This approach allows the training job to communicate with the external MLflow server over HTTP/HTTPS without requiring any additional SageMaker integrations.

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Install MLflow on the SageMaker notebook instance only

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Use the SageMaker Experiments integration with MLflow

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Use SageMaker Processing to run MLflow after training