MLA-C01 Practice Test 32 — 15 Questions

Question 1

An ML engineer is designing a SageMaker Pipeline for model training and registration. They need to ensure that the pipeline can be re-run with different datasets without manual intervention, and that the steps are only re-executed if inputs have changed. Which THREE features should they configure? (Select THREE.)

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

Accepted Answer

Enable step caching to reuse outputs when inputs are unchanged. Option B is correct because SageMaker Pipelines step caching stores each step's output keyed by a hash of the step's inputs (code, arguments, and input artifacts), so when a pipeline re-runs and a step's inputs are unchanged, the cached output is reused instead of re-executing the step. Option D is correct because parameterized execution lets the pipeline be invoked with different parameter values at runtime (for example via StartPipelineExecution with PipelineParameters), enabling re-runs on new datasets without editing or manually rebuilding the pipeline definition. Option E is correct because defining pipeline parameters for dataset location and hyperparameters is the mechanism that makes the pipeline dynamic and reusable, so the same pipeline definition can be pointed at different data and training configurations. Option A is not appropriate because a Condition step performs branching logic on property values during execution; it does not detect data changes or prevent re-execution, and it would require manual logic rather than automatic cache-based skipping. Option C is not appropriate because lineage tracking records the provenance of artifacts (data, models, jobs) for audit and traceability; it does not control whether steps re-execute or enable parameterized re-runs.

Answer

Add a Condition step to manually check for data changes

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Configure lineage tracking to record the origin of models

Question 2

A company is using Amazon SageMaker to deploy a model for real-time inference. The model requires access to a private S3 bucket that contains reference data. The company wants to ensure that the endpoint can access the S3 bucket without using a public internet connection. Which TWO actions should they take? (Select TWO.)

Accepted Answer

Attach the endpoint to a VPC that has a VPC endpoint for S3.. Option B is correct because attaching the SageMaker endpoint to a VPC that has an S3 gateway VPC endpoint (or interface endpoint) allows traffic to reach the private S3 bucket through the AWS private network instead of the public internet, satisfying the no-public-internet requirement. Option C is correct because the SageMaker execution role must have an IAM policy granting s3:GetObject on the bucket, otherwise the endpoint cannot read the reference data regardless of network path. Option A is not correct because security groups cannot whitelist an S3 bucket's IP range reliably (S3 uses dynamic AWS IP ranges and gateway endpoints don't use those IPs), and network access alone doesn't grant authorization. Option D is not correct because routing S3 traffic through an internet gateway uses the public internet, violating the requirement. Option E is not correct because a NAT gateway also routes traffic over the public internet, so it does not meet the private-access requirement.

Answer

Configure the endpoint's security group to allow outbound traffic to the S3 bucket's IP range.

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Attach the endpoint to a VPC with an internet gateway and route the S3 traffic through the internet gateway.

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Attach the endpoint to a VPC with a NAT gateway to route traffic to S3.

Question 3

A data scientist is trying to create a SageMaker endpoint configuration with 6 instances of ml.c5.large for a production variant. The creation fails with the error shown in the exhibit. Which action should the data scientist take to resolve this issue?

Accepted Answer

Request a service quota increase for ml.c5.large for real-time endpoints from the AWS Service Quotas console.. The error indicates that the requested number of instances exceeds the service quota for ml.c5.large for real-time endpoints. AWS enforces default limits on instance counts per instance type per region. Requesting a quota increase via the Service Quotas console is the correct action to raise the limit and allow the deployment of 6 instances.

Answer

Create two separate endpoint configurations, each with 3 instances, and distribute traffic between them.

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Use a different instance type, such as ml.m5.large, which has a higher limit.

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Delete unused endpoints to free up resources.

Question 4

A company wants to use SageMaker Clarify to analyze bias in their training data and model predictions. Which TWO types of bias can Clarify detect? (Choose TWO.)

Accepted Answer

Pre-training bias. SageMaker Clarify organizes its bias metrics into two categories that map directly to the two marked options: pre-training bias and post-training bias. Option B (Pre-training bias) is correct because Clarify analyzes the training dataset itself before model training, computing metrics such as Class Imbalance (CI), Difference in Proportions of Labels (DPL), and Kolmogorov-Smirnov (KS) to detect imbalances or label skew in the input data. Option E (Post-training bias) is correct because Clarify evaluates model predictions after training, using metrics like Disparate Impact (DI), Difference in Conditional Acceptance (DCA), and Accuracy Difference (AD) to detect bias in predicted outcomes across groups. The remaining options are not Clarify bias categories: algorithmic bias (A) is a general concept rather than a Clarify metric group, while inference bias (C) and deployment bias (D) are not terms Clarify uses to classify the bias it detects.

Answer

Algorithmic bias

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Inference bias

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Deployment bias

Question 5

A team wants to monitor the number of requests and latency of their SageMaker endpoint using a unified dashboard. Which AWS service should they use to create a custom dashboard with these metrics?

Accepted Answer

Amazon CloudWatch Dashboards. Amazon CloudWatch Dashboards is the native AWS service for building custom, unified dashboards from CloudWatch metrics. SageMaker automatically publishes endpoint metrics such as Invocations, ModelLatency, OverheadLatency, and Invocation4XXErrors to CloudWatch, so a CloudWatch dashboard can display requests and latency side by side.

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AWS CloudTrail

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AWS Config

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SageMaker Studio

Question 6

An MLOps engineer is setting up a SageMaker endpoint for a model that performs inference on large images. The model is containerized and expects input in a specific format. The team wants to preprocess the images (resize and normalize) before passing them to the model. What is the most efficient way to implement this?

Accepted Answer

Configure SageMaker to use a preprocessing container as the first step of an inference pipeline, followed by the model container.. SageMaker Inference Pipelines allow you to chain multiple containers in a serial fashion, where the output of one container becomes the input of the next. By placing a preprocessing container as the first step, you can resize and normalize large images before passing them to the model container, which keeps the model container focused on inference and avoids unnecessary data transfer or custom code. This is the most efficient and natively supported approach within SageMaker for multi-step inference workflows.

Answer

Use Amazon API Gateway to perform request transformation before forwarding to the endpoint.

Answer

Package the preprocessing logic into the same Docker container as the model.

Answer

Use a Lambda function as a proxy to preprocess requests before calling the SageMaker endpoint.

Question 7

A team uses SageMaker real-time endpoints for inference. They want to deploy a new model version and compare its performance with the current version under live traffic without affecting user experience. Which method should they use?

Accepted Answer

Shadow testing with SageMaker. Shadow testing with SageMaker deploys the new model alongside the production model and mirrors live traffic to it without returning its responses to users. This lets the team compare performance under real traffic with zero impact on user experience.

Answer

A/B testing with production variant traffic splitting

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Batch transform on a holdout test set

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Blue/green deployment

Question 8

A data scientist needs to split a dataset into training, validation, and test sets. The dataset has a categorical target variable with imbalanced class distribution. Which splitting technique ensures that each subset has a similar proportion of each class?

Accepted Answer

Stratified split. Stratified splitting preserves the original class proportions in each subset (training, validation, test) by sampling each class independently. This is critical for imbalanced datasets to avoid skewed distributions that could bias model evaluation or training.

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K-fold cross-validation split

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Chronological split

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Random split

Question 9

A team is building a time-series forecasting model for daily sales data. They want to evaluate model performance using cross-validation while respecting the temporal order of the data. Which data splitting strategy should they use?

Accepted Answer

Walk-forward validation (time-series split). Walk-forward validation (time-series split) respects the temporal order by training on past data and validating on future data, expanding the training window forward in time. This mimics real forecasting conditions and prevents data leakage from future observations into the training set. It is the correct strategy for time-series cross-validation.

Answer

Holdout with a random 80/20 split

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Random k-fold cross-validation

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Stratified sampling

Question 10

A data engineer is preparing a large dataset of 10 TB for ML training on Amazon SageMaker. The data is stored in Amazon S3 as CSV files. To reduce training time and cost, the engineer wants to use a columnar format that is optimized for analytical queries. Which format should the engineer convert the data to?

Accepted Answer

Parquet. Parquet is a columnar storage format that is highly optimized for analytical queries and is natively supported by Amazon SageMaker for efficient data loading. By converting the 10 TB of CSV data to Parquet, the data engineer can reduce I/O and storage costs because columnar formats allow SageMaker to read only the columns needed for training, rather than scanning entire rows. This directly addresses the goal of reducing training time and cost for ML workloads.

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XML

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ORC

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JSON Lines

Question 11

A data scientist deploys a model and wants to monitor the endpoint's invocation latency. They notice that the CloudWatch metric 'ModelLatency' is high, but 'OverheadLatency' is low. Which statement correctly interprets these metrics?

Accepted Answer

The model inference time is the bottleneck; consider optimizing the model or using a faster instance type. The 'ModelLatency' metric measures the time taken by the SageMaker model container to process a single request, including inference and any preprocessing/postprocessing within the container. 'OverheadLatency' measures the time spent on SageMaker infrastructure (e.g., network I/O, request queuing, and response handling). When ModelLatency is high and OverheadLatency is low, the bottleneck is clearly the model inference time itself, not the infrastructure overhead. Therefore, optimizing the model (e.g., quantization, pruning) or upgrading to a faster instance type (e.g., GPU vs. CPU) is the correct remediation.

Answer

The SageMaker overhead is causing the delay; check endpoint configuration

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The endpoint is overloaded; increase the number of instances

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The network latency is high; move the endpoint closer to clients

Question 12

A team is fine-tuning a Hugging Face transformer model on SageMaker. They need to use a custom training script with the Hugging Face Estimator. Which SageMaker feature does this represent?

Accepted Answer

Script mode. Using a custom training script with the Hugging Face Estimator in SageMaker represents Script Mode. Script Mode allows you to bring your own training script and run it within a pre-built framework container, such as Hugging Face, PyTorch, or TensorFlow. This provides flexibility to customize training logic while leveraging SageMaker's managed infrastructure.

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Built-in algorithm

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SageMaker Autopilot

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SageMaker Debugger

Question 13

A data scientist needs to annotate a large dataset of images for an object detection model. The team wants to minimize manual labeling effort and cost. Which Amazon SageMaker feature should they use?

Accepted Answer

SageMaker Ground Truth. SageMaker Ground Truth provides labeling workflows with built-in active learning, which automatically selects the most informative images for human review and uses machine learning to label the rest, reducing manual effort and cost.

Answer

SageMaker Feature Store

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SageMaker Studio Classic

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SageMaker Data Wrangler

Question 14

A data scientist is using SageMaker to train a linear regression model. After training, they evaluate the model on the test set and get an R² of 0.95. However, when they deploy the model to a SageMaker endpoint and run predictions on new data, the predictions are far off. What is the most likely cause?

Accepted Answer

The test set is not representative of the production data distribution.. A high R² of 0.95 on the test set indicates the model fits the test data well, but if the test set was drawn from the same distribution as the training data and does not reflect the real-world production data, the model will fail to generalize. In SageMaker, the endpoint serves predictions on live data that may have different statistical properties, leading to poor performance despite high test-set metrics. This is a classic case of dataset shift, not a model training or deployment configuration issue.

Answer

The endpoint is using a different inference script.

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The model was trained with a wrong algorithm.

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The model is overfitting the training data.

Question 15

A company is using SageMaker to train a model with a custom container. The training script requires a specific version of a Python library that is not included in the default SageMaker containers. How should they provide this library?

Accepted Answer

Extend a SageMaker framework container and install the library using a Dockerfile. Using a custom container (BYOC) allows bundling all dependencies, including specific library versions, into a Docker image that SageMaker can run.

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Use SageMaker Script Mode and specify the library in a requirements.txt

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Use SageMaker's lifecycle configuration to install the library on the training instance

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Use pip install in the training script before model training