Amazon Web Services · Free Practice Questions · Last reviewed May 2026
24real exam-style questions organised by domain, each with the correct answer highlighted and a plain-English explanation of why it's right — and why the others are wrong.
A data scientist is preparing a large dataset for training a machine learning model. The dataset contains missing values in several columns. Which approach is the MOST efficient for handling missing values in a large dataset using AWS services?
Use AWS Glue ETL to write a custom Python script that imputes missing values with the mean.
Use Amazon SageMaker Data Wrangler to impute missing values using built-in transforms.
Data Wrangler provides efficient, scalable, and visual data preparation without custom code.
Use pandas in a SageMaker notebook to impute missing values with the median.
Remove all rows with missing values from the dataset.
A company is using AWS Glue to prepare data for a machine learning pipeline. The source data is in an Amazon S3 bucket in CSV format. The data scientist wants to convert the data to Parquet format and partition it by date. Which AWS Glue feature should be used to optimize the data for query performance and reduce storage costs?
Use Amazon Athena to convert the data to JSON format and store it in S3.
Use AWS Glue DynamicFrame to repartition the data and write it as Parquet.
DynamicFrame supports efficient partitioning and columnar format conversion.
Use AWS Glue to convert the data to Apache Hive format.
Use Apache Spark DataFrame to write the data as CSV with Snappy compression.
A machine learning engineer is preparing a dataset for a binary classification model. The dataset has a severe class imbalance (95% class A, 5% class B). The engineer wants to use Amazon SageMaker to train the model. Which data preparation technique should the engineer apply to the training dataset to address the imbalance and improve model performance?
Apply data augmentation to the majority class by adding noise.
Apply Synthetic Minority Over-sampling Technique (SMOTE) to generate synthetic samples for the minority class.
SMOTE creates synthetic samples, balancing the dataset without losing data.
Use a weighted loss function during training to penalize misclassifications of the minority class.
Apply random under-sampling to reduce the majority class to match the minority class size.
A data scientist is preparing a dataset for a machine learning model that predicts customer churn. The dataset contains a column 'CustomerID' that is a unique identifier. What should the data scientist do with this column before training the model?
Keep the column as a feature because it uniquely identifies each customer.
Use the column as the target variable.
Remove the column from the feature set.
Removing unique identifiers prevents overfitting and is standard practice.
Encode the column using one-hot encoding.
A company uses AWS Glue to run ETL jobs that prepare data for machine learning. The data is stored in Amazon S3 in Parquet format. A data engineer notices that the Glue job is running slowly and consuming a lot of resources. What is the MOST cost-effective way to improve the performance of the Glue job?
Use the G.1X worker type, which provides more memory per worker compared to the Standard worker type.
G.1X offers more memory, reducing memory-related bottlenecks without increasing DPU count.
Use partition pruning on the source data to reduce the amount of data processed.
Switch the output format from Parquet to CSV to reduce processing overhead.
Use a larger instance type for the Glue job by increasing the number of DPUs.
A machine learning team is building a model using a dataset that contains a mix of numerical and categorical features. The categorical features have high cardinality (e.g., zip code with thousands of unique values). The team wants to use Amazon SageMaker for training. Which technique should the team use to encode the high-cardinality categorical features effectively?
Apply hash encoding to map categories to a fixed number of buckets.
Apply target encoding (mean encoding) to the high-cardinality features.
Target encoding reduces dimensionality and captures target-related information.
Apply one-hot encoding to all categorical features.
Apply label encoding to assign integer values to each category.
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Practice this domainA data scientist is training a binary classification model using imbalanced data where the positive class is only 1% of the dataset. The scientist wants to maximize the recall for the positive class while maintaining reasonable precision. Which evaluation metric is most appropriate to tune during model selection?
Log loss
Area under the ROC curve (AUC)
F1 score
F1 score combines precision and recall, making it suitable for imbalanced classes when both matter.
Accuracy
A machine learning engineer is training a deep learning model on SageMaker and notices that the training loss decreases rapidly in the first few epochs but then plateaus. The validation loss starts increasing after 10 epochs. Which action should the engineer take to improve generalization?
Add more layers to the model
Use early stopping with validation loss monitoring
Early stopping halts training when validation loss stops decreasing, reducing overfitting.
Increase the learning rate
Decrease the batch size
A team is deploying a machine learning model for real-time fraud detection. The model must have inference latency under 10 ms and handle up to 1000 requests per second. The model is a gradient boosting model using XGBoost. Which SageMaker hosting configuration is MOST cost-effective while meeting the requirements?
Use SageMaker Batch Transform with multiple instances
Use a SageMaker Multi-Model Endpoint (MME) on an ml.c5.4xlarge instance with auto scaling
MME allows multiple models to share a container, reducing cost while scaling to meet demand.
Deploy on a single ml.c5.xlarge instance with a real-time endpoint
Deploy separate real-time endpoints for each model on ml.m5.large instances
A data scientist is using Amazon SageMaker to train a linear regression model. After training, the scientist notices that the training and validation errors are both low, but the model performs poorly on new test data. What is the MOST likely cause?
There is data leakage from the validation set into the training set
Data leakage artificially inflates performance on validation but fails on true unseen data.
The features are not scaled properly
The model is overfitting the training data
The model has high bias
A company is using SageMaker to train a neural network for image classification. The training job is taking too long. The team wants to reduce training time without sacrificing model accuracy. Which approach should they recommend?
Increase the batch size to the maximum possible
Use a GPU-based instance such as ml.p3.2xlarge
GPUs accelerate matrix operations in neural networks, reducing training time.
Use a learning rate scheduler that reduces the learning rate over time
Add more convolutional layers to the model
A machine learning engineer is using SageMaker Automatic Model Tuning (AMT) to optimize hyperparameters for a random forest model. The engineer notices that the tuning job is taking too long and many hyperparameter combinations are being evaluated but not improving the objective metric. Which action should the engineer take to make the tuning more efficient?
Switch the strategy from Bayesian to random search
Use a smaller instance type for each training job
Increase the maximum number of training jobs
Enable early stopping for the tuning job
Early stops poorly performing trials, reducing wasted computation.
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Practice this domainA data science team has trained a PyTorch model using Amazon SageMaker and wants to deploy it with a custom inference container that includes a pre-processing step. The team needs to minimize latency and ensure the pre-processing runs only once per request. Which SageMaker real-time inference option should they use?
Deploy the model on a multi-model endpoint and include pre-processing in the model code.
Use a batch transform job with a pre-processing script.
Package pre-processing and inference in a single container with a custom entry point.
Create a SageMaker inference pipeline with two containers: one for pre-processing and one for inference.
An inference pipeline chains containers sequentially, allowing pre-processing to run once per request with low latency.
A company is deploying a real-time inference endpoint for a natural language processing model using Amazon SageMaker. The model requires GPU acceleration and must handle variable traffic patterns, including sudden spikes. The team wants to minimize costs while maintaining low latency during spikes. Which endpoint configuration strategy should they use?
Use a single large GPU instance with provisioned concurrency.
Use a serverless endpoint with GPU support.
Use a single GPU instance in multiple Availability Zones with an Application Load Balancer.
Use a multi-model endpoint on a GPU instance with Auto Scaling based on invocation count.
Multi-model endpoints share instances across models, and Auto Scaling adjusts capacity for spikes.
A machine learning engineer is deploying a model using AWS Lambda for inference. The model is a small scikit-learn classifier with a size of 50 MB. The Lambda function is invoked by an API Gateway REST API. The engineer notices that cold starts are causing high latency. Which action would most effectively reduce cold start latency without increasing costs significantly?
Store the model in Amazon EFS and load it at runtime.
Increase the Lambda function memory to the maximum of 10,240 MB.
Configure provisioned concurrency for the Lambda function.
Provisioned concurrency keeps instances initialized and ready to respond immediately.
Package the model in a container image and deploy using Lambda container support.
A company uses Amazon SageMaker to train and deploy machine learning models. The security team requires that all data in transit between the training job and S3 be encrypted, and that no data traverses the public internet. Which configuration should the company use?
Create a VPC with S3 VPC endpoints, attach a VPC-only policy to the SageMaker execution role, and enable KMS encryption for training jobs.
S3 VPC endpoints keep traffic within AWS network, and KMS encrypts data in transit and at rest.
Use an S3 bucket with SSE-S3 encryption and restrict bucket access to a VPC.
Enable default encryption on the S3 bucket and use HTTPS for all SageMaker endpoints.
Create a VPC with a NAT gateway, and configure SageMaker to use the VPC and enforce HTTPS.
A team is deploying a deep learning model on a SageMaker real-time endpoint. The model has high memory requirements, and the team wants to minimize instance cost while ensuring the endpoint can handle up to 10 concurrent requests. They plan to use a single ml.p3.2xlarge instance (8 vCPUs, 61 GB memory). Which SageMaker endpoint configuration will allow the endpoint to handle 10 concurrent requests without errors?
Disable ModelServerWorkers to reduce overhead.
Set the initial instance count to 1 and configure the container to use multiple ModelServerWorkers.
Multiple workers allow the instance to handle multiple requests concurrently, up to the CPU/memory limit.
Set the initial variant weight to 10.
Set the initial instance count to 10 in the production variant.
A company wants to deploy a machine learning model that was trained on-premises using TensorFlow. The model is a TensorFlow SavedModel. The company uses AWS and wants to minimize operational overhead. Which deployment option meets these requirements?
Deploy the model on Amazon ECS using a custom Docker image.
Deploy the model as an AWS Lambda function with the TensorFlow runtime.
Deploy the model using Amazon SageMaker Studio.
Deploy the model using Amazon SageMaker with a TensorFlow inference container.
SageMaker provides pre-built TensorFlow containers and manages the endpoint, reducing operational overhead.
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Practice this domainA machine learning engineer at a retail company is monitoring a production model that predicts inventory demand. The model's prediction accuracy has dropped significantly over the past week. The engineer checks the model's input data and notices a new product category was introduced with a different distribution. Which concept is most likely causing the performance degradation?
Concept drift
Covariate shift
Covariate shift occurs when the distribution of input features changes over time.
Data leakage
Model decay
A data science team is using Amazon SageMaker to train and deploy a binary classification model. They want to continuously monitor the model for data drift in production. Which combination of AWS services and SageMaker features should they use to implement automated drift detection with minimal operational overhead?
SageMaker Debugger and Amazon SNS
SageMaker Pipelines and AWS Lambda
SageMaker Clarify and AWS Config
SageMaker Model Monitor and Amazon CloudWatch
SageMaker Model Monitor detects drift and sends metrics to CloudWatch for alerting.
A financial services company uses a custom container on Amazon SageMaker to serve a fraud detection model. The model's inference latency has recently increased, causing timeouts for some requests. The team reviews the SageMaker logs and finds that the container is consuming more memory than allocated. What should the team do to maintain service quality while ensuring cost-effectiveness?
Decrease the model's batch size to reduce memory usage
Increase the number of instances in the endpoint to distribute the load
Implement an auto-scaling policy based on memory utilization
Change the instance type to a memory-optimized instance, such as r5.large
Switching to a memory-optimized instance provides more memory per instance, resolving the issue cost-effectively.
A machine learning team is building a CI/CD pipeline for model deployment using Amazon SageMaker. They need to ensure that all model artifacts are encrypted at rest and in transit, and that access to the models is controlled via IAM. Which TWO actions should the team take to meet these requirements? (Choose TWO.)
Set the SageMaker model's 'EnableNetworkIsolation' parameter to true
Enable default encryption on the S3 bucket that stores model artifacts
Enable AWS CloudTrail to log all API calls to SageMaker
Configure the SageMaker notebook instance to use a KMS key for encryption
KMS encrypts data at rest in SageMaker.
Use HTTPS endpoints for invoking the SageMaker model
HTTPS encrypts data in transit.
A healthcare company deploys a model to predict patient readmission risk. The model was trained on historical data and is now showing signs of concept drift. The team needs to implement a monitoring solution that can detect drift and automatically retrain the model when drift is detected. Which THREE steps should the team take to build this solution? (Choose THREE.)
Deploy SageMaker Model Monitor to track prediction quality over time
Model Monitor can detect drift using ground truth.
Disable the existing endpoint to prevent stale predictions during retraining
Set up a process to collect ground truth labels from patient outcomes
Ground truth is required to detect concept drift.
Manually compare the model's predictions against a holdout validation set each week
Use AWS Lambda to invoke a SageMaker training job when drift is detected
Lambda can automate the retraining trigger.
A company is using Amazon SageMaker to host a real-time inference endpoint. They want to restrict access to the endpoint to only a specific VPC and require authentication using AWS IAM. Which TWO configuration steps should they take to achieve this? (Choose TWO.)
Configure the endpoint to be deployed in a private subnet within the VPC
Private subnet restricts traffic to within the VPC.
Enable IAM-based authentication for the endpoint
IAM auth ensures only authorized users can invoke the endpoint.
Attach a resource-based policy to the endpoint that denies all traffic except from the VPC
Place the endpoint behind Amazon CloudFront to act as a proxy
Use a public subnet and configure a security group to allow only the company's IP range
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Practice this domainThe MLA-C01 exam has 50 questions and must be completed in 130 minutes. The passing score is 700/1000.
Scenario-based questions covering exam objectives with detailed answer explanations.
The exam covers 4 domains: Data Preparation for Machine Learning, ML Model Development, Deployment and Orchestration of ML Workflows, ML Solution Monitoring, Maintenance and Security. Questions are weighted by domain — higher-weight domains appear more on your actual exam.
No. These are original exam-style practice questions written against the official Amazon Web Services MLA-C01 exam objectives. They are not copied from the real exam. Courseiva focuses on genuine understanding, not memorisation of braindumps.
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