MLA-C01 Practice Test 27 — 15 Questions

Question 1

A data science team needs to deploy a PyTorch model for real-time inference with low latency. The model requires GPU acceleration. Which SageMaker endpoint configuration should they use?

Accepted Answer

Create a real-time endpoint using an ml.p3.2xlarge instance. Real-time SageMaker endpoints with GPU instances like ml.p3.2xlarge are specifically designed for low-latency, synchronous inference with GPU acceleration. PyTorch models requiring GPU must use instance types that support NVIDIA CUDA, and the ml.p3 family provides the necessary GPU compute for real-time predictions.

Answer

Create a multi-model endpoint using ml.m5.large instances

Answer

Create a serverless endpoint with memory set to 6144 MB

Answer

Create a batch transform job using an ml.c5.xlarge instance

Question 2

A company is using Amazon SageMaker Data Wrangler to prepare a dataset for training. They have created a data flow with multiple transforms. Which TWO actions can they take to operationalize the data preparation pipeline for production? (Choose 2)

Accepted Answer

Export the data flow to a SageMaker Processing job. Option C is correct because SageMaker Data Wrangler lets you export a data flow to a SageMaker Processing job, which packages the transforms into a containerized job that can be run on demand or scheduled as part of a production pipeline. Option E is correct because Data Wrangler can export the flow as a standalone Python script (using the data wrangler library and pandas/Spark code), which can be version-controlled, customized, and run in any Python environment such as a processing job or CI/CD workflow. Option A is not correct because you cannot directly convert a Data Wrangler flow into a scheduled SageMaker Pipeline; you must first export to a Processing job or Python script and then orchestrate it with SageMaker Pipelines. Option B is not correct because Autopilot is an automated model-building service, not a target for exporting data preparation flows. Option D is not correct because Data Wrangler flows are batch data preparation artifacts and cannot be deployed as real-time inference endpoints.

Answer

Create a scheduled SageMaker Pipeline by directly converting the Data Wrangler flow

Answer

Convert the data flow directly to Amazon SageMaker Autopilot

Answer

Deploy the data flow as a real-time inference endpoint

Question 3

A data scientist is using SageMaker to train a custom PyTorch model for image classification. They want to use SageMaker Debugger to detect training issues. Which TWO built-in rules are most relevant for detecting common training problems? (Select TWO.)

Accepted Answer

Overfit. Option B (Overfit) is correct because SageMaker Debugger's built-in Overfit rule monitors the gap between training and validation loss across steps and raises an issue when validation loss stops improving while training loss keeps decreasing, which is the classic signature of overfitting in a PyTorch image-classification job. Option C (ExplodingGradients) is correct because the ExplodingGradients rule inspects the gradients tensor emitted by the framework and flags abnormally large gradient values or spikes, which cause unstable or diverging training and are a common problem in deep networks. Option A (DataDistribution) is not a built-in Debugger rule for detecting training problems; it relates to SageMaker Clarify/Model Monitor data and bias analysis rather than Debugger's training-issue rule set. Option D (ImageQuality) is not a SageMaker Debugger built-in rule; image-quality checks would be a custom preprocessing concern, not a Debugger rule. Option E (ConfusionMatrix) is not a Debugger training-issue rule; confusion matrices are evaluation artifacts computed after training (for example with SageMaker Clarify or custom code), not a built-in Debugger rule for detecting training problems.

Answer

DataDistribution

Answer

ImageQuality

Answer

ConfusionMatrix

Question 4

A company wants to serve 200 different PyTorch models. Each model is small (under 1 GB) and only a fraction are used at any time. To minimize cost and management overhead, which SageMaker inference option should be used?

Accepted Answer

Use a multi-model endpoint. A multi-model endpoint (MME) is the correct choice because it allows you to host multiple small PyTorch models (under 1 GB each) on a single endpoint, sharing the underlying compute instance. This minimizes cost by only paying for the active instances, and reduces management overhead since you don't need to create or manage separate endpoints for each model. SageMaker dynamically loads and unloads models from the container's memory based on invocation patterns, which is ideal for a scenario where only a fraction of the 200 models are used at any time.

Answer

Use batch transform for all models

Answer

Create a separate real-time endpoint for each model

Answer

Use a multi-container endpoint

Question 5

An ML engineer runs the CLI command shown in the exhibit. However, the training job fails immediately with an error: 'Unable to assume role'. What is the most likely cause?

Accepted Answer

The IAM role's trust policy does not grant SageMaker permission to assume the role.. The 'Unable to assume role' error indicates that SageMaker cannot assume the IAM role specified in the CLI command. This is a trust policy issue: the role's trust policy must include SageMaker as a trusted service (i.e., `"Service": "sagemaker.amazonaws.com"`). Without this, SageMaker is not authorized to assume the role, regardless of the role's permissions.

Answer

The IAM role 'SageMakerExecutionRole' does not have permission to create the training job.

Answer

The training image in ECR does not exist.

Answer

The S3 bucket 'my-bucket' does not exist.

Question 6

A team is using SageMaker to run a large-scale distributed training job for a language model. They are using SageMaker's Pipe mode to stream data from S3 to reduce IO. They observe that the training throughput is lower than expected, and the CPU utilization is high while GPU utilization is low. The training script uses PyTorch's DataLoader with num_workers=0. The data preprocessing is minimal. Which change is most likely to improve GPU utilization?

Accepted Answer

Increase the number of data loading workers (num_workers).. With num_workers=0, PyTorch's DataLoader loads data in the main training process, creating a CPU bottleneck that keeps GPUs idle. Increasing num_workers parallelizes data loading across multiple subprocesses, which reduces CPU strain and feeds data faster to GPUs, improving throughput. Adding more GPUs (Option C) or vCPUs (Option B) does not address the root cause, and switching to File mode (Option D) would increase I/O overhead, worsening performance.

Answer

Use a larger instance with more vCPUs.

Answer

Increase the number of GPUs per instance.

Answer

Switch from Pipe mode to File mode.

Question 7

A machine learning engineer needs to optimize a trained TensorFlow model for deployment on edge devices with limited compute. Which SageMaker feature should they use to compile the model for target hardware?

Accepted Answer

SageMaker Neo. SageMaker Neo is the correct choice because it is specifically designed to compile trained machine learning models into an optimized format for target hardware architectures, such as ARM, Intel, or NVIDIA, enabling efficient inference on edge devices with limited compute resources. It uses a compiler to apply hardware-specific optimizations like operator fusion and memory layout tuning, reducing latency and memory footprint without requiring manual code changes.

Answer

SageMaker Model Monitor

Answer

SageMaker Debugger

Answer

SageMaker Elastic Inference

Question 8

A data scientist is training a deep learning model using SageMaker and wants to use distributed training across multiple GPUs to reduce training time. Which TWO actions should the scientist take to configure distributed training? (Select TWO.)

Accepted Answer

Use the SageMaker distributed data parallelism library. Option B is correct because the SageMaker distributed data parallelism library (smdistributed.dataparallel) is purpose-built to shard data and synchronize gradients across multiple GPUs, reducing training time for deep learning jobs. Option D is correct because distributed training is enabled by passing a distribution parameter (e.g., {'smdistributed': {'dataparallel': {'enabled': True}}}) to the SageMaker estimator, which tells SageMaker how to launch the distributed framework. Option A is wrong because reducing epochs is unrelated to distributed training and would only degrade model convergence. Option C is wrong because SageMaker's data parallelism library handles data sharding automatically, so manual sharding and S3 uploads are unnecessary. Option E is wrong because setting instance count to 1 with a multi-GPU instance does not configure distributed training across multiple instances and is not the required configuration action.

Answer

Reduce the number of epochs to match the number of GPUs

Answer

Manually split the training data into shards and upload to S3

Answer

Set the instance count to 1 with a multi-GPU instance

Question 9

A data engineer needs to assess the quality of a dataset containing customer information. The dataset has missing values, outliers, and duplicate records. Which TWO AWS services can be used to perform data quality assessment? (Select TWO.)

Accepted Answer

AWS Glue DataBrew. AWS Glue DataBrew (B) is correct because it is a visual data preparation service that provides over 250 built-in transformations and profiling features, including data quality checks such as detecting missing values, outliers, and duplicate records via its profile view and anomaly detection. Amazon SageMaker Data Wrangler (E) is correct because it offers data quality and insights reports that automatically surface missing values, outliers, duplicate rows, and target leakage, allowing a data engineer to assess dataset quality before ML workflows. Amazon Athena (A) is a serverless query service for analyzing data in S3, not a dedicated data quality assessment tool. AWS Glue ETL (C) is used for building and running extract-transform-load jobs rather than profiling data quality. Amazon QuickSight (D) is a business intelligence visualization service and does not provide built-in data quality assessment capabilities.

Answer

Amazon Athena

Answer

AWS Glue ETL

Answer

Amazon QuickSight

Question 10

A company deploys a SageMaker model using AWS KMS for encryption at rest. They have a compliance requirement to rotate the KMS key every year without causing downtime for the inference endpoint. Which approach should they take?

Accepted Answer

Enable automatic key rotation on the existing KMS key. AWS KMS supports automatic key rotation, which creates new backing keys annually while retaining the same key ID and metadata. This ensures that the SageMaker endpoint continues to use the same KMS key alias and configuration, so no endpoint update or downtime is required. Automatic rotation satisfies the compliance requirement without any manual intervention or endpoint recreation.

Answer

Use AWS Certificate Manager (ACM) for encryption

Answer

Create a new KMS key and update the endpoint configuration

Answer

Manually rotate the key by recreating the endpoint

Question 11

A company uses SageMaker Clarify to detect bias in their training data. They find that the model has a high disparate impact for a protected attribute. What should they do to mitigate this bias during training?

Accepted Answer

Preprocess the data using techniques like reweighing or resampling to reduce bias. SageMaker Clarify detects bias but does not automatically mitigate it during training. To reduce disparate impact, the correct approach is to preprocess the training data using bias mitigation techniques such as reweighing (assigning weights to instances to balance outcomes across groups) or resampling (oversampling underrepresented groups or undersampling overrepresented ones). These methods adjust the data distribution before training, directly addressing the source of bias and reducing disparate impact.

Answer

Use SageMaker Clarify’s built-in bias mitigation algorithm during training

Answer

Remove the protected attribute from the dataset

Answer

Increase the model complexity to capture more patterns

Question 12

A company is using SageMaker Automatic Model Tuning to optimize a regression model. They want to minimize the root mean squared error (RMSE). The tuner has completed 20 jobs, and the RMSE has plateaued. Which action should the data scientist take to potentially improve the results?

Accepted Answer

Decrease the range of hyperparameters to focus on promising areas. When RMSE has plateaued, narrowing the hyperparameter ranges to focus on promising areas can help the tuner explore more finely around good values. This is a common technique in Bayesian optimization to refine the search. Increasing jobs or parallelism may not help if the search space is too broad.

Answer

Increase the maximum number of training jobs

Answer

Increase the number of parallel training jobs

Answer

Switch the objective metric to mean absolute error (MAE)

Question 13

A data engineer needs to catalog metadata from multiple data sources across the organization for use in ML workflows. Which AWS Glue component should be used to store and manage this metadata?

Accepted Answer

AWS Glue Data Catalog. The AWS Glue Data Catalog is a persistent, centralized metadata repository that stores table definitions, schemas, and partition information for data sources across the organization. It integrates natively with Athena, Redshift Spectrum, EMR, and Glue ETL jobs, making it the correct component for cataloging metadata used in ML workflows. Crawlers populate the Data Catalog, but the Catalog itself is the storage and management layer.

Answer

AWS Glue Crawler

Answer

AWS Glue Studio

Answer

AWS Glue ETL

Question 14

A machine learning practitioner is building a binary classifier with severe class imbalance (1:1000). They want to use SMOTE for oversampling. What is a potential drawback of applying SMOTE on the entire dataset before splitting into training and test sets?

Accepted Answer

It causes data leakage, making validation metrics overly optimistic. When SMOTE is applied before the train/test split, synthetic minority samples are generated using information from the entire dataset, including the observations that will later become the test set. This means the test set is no longer independent of the training data, so the model has effectively 'seen' information from the test distribution. The result is data leakage that inflates validation metrics (precision, recall, F1) and produces a model that appears far better than it will perform in production.

Answer

SMOTE increases the risk of overfitting to the minority class

Answer

SMOTE cannot be applied to categorical features

Answer

SMOTE generates synthetic samples that may not be realistic

Question 15

Refer to the exhibit. A data scientist reviews the output of a SageMaker training job. The model has 95% training accuracy and 92% validation accuracy. Which statement is true?

Accepted Answer

The model has acceptable performance with a small generalization gap. Training accuracy of 95% and validation accuracy of 92% indicate the model performs well on both seen and unseen data, with only a 3-point generalization gap. This is a healthy, acceptable result — the model is neither severely overfitting nor underfitting. The small gap suggests good generalization.

Answer

The model is underfitting because the validation accuracy is too low

Answer

The model needs more epochs to improve validation accuracy

Answer

The model is overfitting because the training accuracy is higher than validation accuracy