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.
26% of exam · 6 sample questions below
A company wants to build a customer service chatbot that answers questions about their internal policy documents. The documents are updated monthly, and the team cannot afford to retrain a model each time. Which approach is MOST appropriate?
Use a larger foundation model with a longer context window and paste all documents into each prompt
Use Retrieval-Augmented Generation (RAG) with the policy documents indexed in a vector store
RAG retrieves relevant passages from the indexed policy documents at query time and supplies them as context to the model, so monthly updates only require re-indexing the vector store rather than retraining. This satisfies the constraint that retraining is unaffordable.
Fine-tune a base LLM on the policy documents monthly
Train a custom model from scratch on the policy documents each month
A data scientist is using SageMaker built-in XGBoost algorithm for a binary classification task. Which objective metric is MOST appropriate for SageMaker Automatic Model Tuning to maximize?
validation:mae
validation:rmse
validation:ndcg
validation:auc
For binary classification, `validation:auc` maximises the area under the ROC curve, giving a threshold-independent measure of class separation. This satisfies the stem's requirement for the most appropriate tuning objective, since AUC handles imbalanced binary labels better than accuracy and is natively supported by SageMaker Automatic Model Tuning.
A company wants to use SageMaker Autopilot for a regression problem. They require an explainability report that shows feature importance globally. Which Autopilot feature should they enable?
AutoML candidate generation
Ensembling mode
Hyperparameter optimization
Explainability report generation
Enabling explainability report generation makes Autopilot produce a SageMaker Clarify report alongside each candidate model, quantifying global feature importance via SHAP values for the regression task. This directly satisfies the stated requirement for a global feature-importance explainability report, which Autopilot does not generate by default.
A team wants to use a custom PyTorch training script in SageMaker. They need to install additional Python packages not included in the base PyTorch container. Which approach should they take?
Use SageMaker Script Mode with a custom Dockerfile
Build a custom container with Docker
Install packages using a lifecycle configuration
Use the SageMaker PyTorch estimator with a requirements.txt file
The PyTorch estimator accepts a requirements.txt file, which SageMaker installs into the container before training begins. This adds the extra Python packages without building a custom image, satisfying the need for dependencies absent from the base container.
A company needs to perform time-series forecasting on historical sales data. Which SageMaker built-in algorithm is BEST suited for this task?
BlazingText
Linear Learner
XGBoost
DeepAR
DeepAR is a supervised recurrent neural network algorithm purpose-built for time-series forecasting, handling multiple related series and probabilistic predictions. It satisfies the stem's historical sales forecasting requirement, unlike classification, regression, or clustering algorithms that ignore temporal ordering.
A data scientist is training an object detection model using SageMaker built-in Object Detection algorithm. They want to visualize the bounding boxes on validation images after training. Which approach should they use?
Use SageMaker Debugger to capture output tensors
Write a custom inference script that saves images with bounding boxes
The built-in Object Detection algorithm emits bounding box coordinates as JSON output, not annotated images. A custom inference script must parse those predictions and draw boxes onto the validation images, since no built-in visualisation step exists.
Enable SageMaker Model Monitor
Use SageMaker Clarify
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Practice this domain28% of exam · 6 sample questions below
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 built-in imputation transforms that run as scalable Spark processing, avoiding custom code for a large dataset. This satisfies the efficiency constraint by handling missing values across many columns in one visual flow, with results exportable directly to SageMaker training.
Use pandas in a SageMaker notebook to impute missing values with the median.
Remove all rows with missing values from the dataset.
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.
CustomerID is a unique identifier carrying no generalisable signal; each value appears once, so the model would memorise rather than learn. Removing it from the feature set prevents noise and spurious splits, satisfying the requirement to prepare the churn dataset correctly.
Encode the column using one-hot encoding.
A data scientist is using Amazon SageMaker Data Wrangler to prepare a dataset. The dataset contains a column with date strings in the format 'YYYY-MM-DD'. The data scientist wants to extract the year, month, and day as separate features. Which Data Wrangler transform should be used?
Encode categorical transform.
Scale values transform.
Parse date transform.
The Parse date transform interprets the 'YYYY-MM-DD' string as a datetime type, from which Data Wrangler can derive year, month and day components. This satisfies the requirement to extract those three separate features without custom code.
Handle missing transform.
A data engineer is using AWS Glue to prepare a dataset for machine learning. The dataset has several columns with outliers. The engineer wants to detect and handle outliers in a scalable manner. Which TWO approaches should the engineer consider? (Select TWO.)
Manually remove outliers by inspecting the data in Amazon S3.
Train a neural network to identify anomalies and remove them.
Use pandas in a SageMaker notebook to calculate z-scores and filter outliers.
Use AWS Glue DynamicFrame with Apache Spark to compute interquartile range (IQR) and filter outliers.
Computing IQR per column on a DynamicFrame lets Spark calculate quartiles and filter rows outside the fences in a distributed, scalable manner. This satisfies the outlier-detection constraint across the full dataset without collecting data to a single node.
Use Amazon SageMaker Data Wrangler to apply an outlier detection transform.
Data Wrangler provides built-in outlier detection transforms that run as part of a scalable SageMaker data flow, letting the engineer identify and handle outliers without writing custom Spark code. This satisfies the requirement for scalable outlier handling within AWS Glue-adjacent ML preparation.
A company uses AWS Glue ETL jobs to transform data for machine learning. They have a dataset with a column 'income' that is heavily right-skewed. Which transformation should be applied to make the distribution more Gaussian-like?
Log transformation (natural log)
A natural log transformation compresses the long right tail of a heavily skewed variable, pulling extreme high values closer to the bulk of the data. This reduces skewness and stabilises variance, making the 'income' distribution approximately Gaussian-like, which many ML algorithms assume.
Standardization (z-score)
Min-max scaling to [0,1]
Equal-width binning
A team is using Amazon SageMaker Processing for data preprocessing. They have a Parquet dataset in Amazon S3. Which configuration will provide the most efficient reading of the dataset during processing?
Read the Parquet files as text using SparkContext.textFile
Split the dataset into many small Parquet files (e.g., 1 MB each)
Convert the Parquet files to CSV before processing
Read the Parquet files directly using SparkSession.read.parquet
SparkSession.read.parquet reads Parquet's columnar, compressed format directly, enabling predicate pushdown and column pruning so SageMaker Processing scans only needed columns and row groups from Amazon S3, avoiding full-dataset deserialisation and delivering the most efficient read.
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Practice this domain22% of exam · 6 sample questions below
A data scientist needs to deploy a single ML model that will serve real-time predictions with low latency (under 10 ms) for a high-traffic web application. The model fits in memory and requires GPU acceleration. Which SageMaker inference option is MOST suitable?
Real-time endpoint on ml.m5 instances
Batch Transform
Real-time endpoint on ml.g4dn instances
A real-time endpoint on ml.g4dn instances provides GPU acceleration with persistent, low-latency inference, satisfying the sub-10 ms requirement for a high-traffic web application. Serverless inference lacks GPU support and cold starts, while batch transform cannot serve real-time requests.
Serverless Inference
A team has 200 small ML models that need to be served via HTTPS endpoints. Each model is used infrequently, and the team wants to minimize hosting costs. Which SageMaker deployment approach is MOST cost-effective?
Use SageMaker Serverless Inference for each model
Deploy each model on a separate real-time endpoint
Use Batch Transform for all models
Use a single multi-model endpoint (MME)
A multi-model endpoint loads multiple models behind one HTTPS endpoint, sharing the underlying instance and loading models on demand. With 200 infrequently used models, this avoids provisioning 200 always-on endpoints, directly satisfying the stem's cost-minimisation constraint.
An ML team uses SageMaker Pipelines to automate model retraining. They want to skip redundant training steps when input data has not changed. Which feature should they enable?
Pipeline caching
Pipeline caching reuses a step's outputs when its inputs, code and parameters are unchanged, so the training step is skipped entirely rather than re-executed. This directly satisfies the stem's requirement to avoid redundant training when input data has not changed, saving compute time and cost.
Pipeline variable expressions
Model registry approval
Step parallelism
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?
Blue/green deployment
Shadow testing
Canary deployment with production variants
Canary deployment with production variants lets SageMaker split endpoint traffic, sending 5% to the new model variant while the old version serves the rest. This directly satisfies the requirement to monitor errors before full rollout.
Multi-model endpoint
An ML engineer needs to compile a trained TensorFlow model to run efficiently on a target edge device with an ARM CPU. Which AWS service should they use?
SageMaker Debugger
AWS Inferentia
SageMaker Neo
SageMaker Neo compiles trained models for specific target hardware, including ARM CPUs, producing optimised executables that run efficiently on edge devices. It directly satisfies the stem's requirement to compile a TensorFlow model for an ARM-based edge target.
Amazon Elastic Inference
A data science team uses SageMaker Pipelines for automated training. They need to conditionally register a model only if evaluation metrics exceed a threshold. Which pipeline step type should they use after the evaluation step?
Condition step
A condition step evaluates a JSON condition against the evaluation step's output and branches execution accordingly, so registration only proceeds when metrics exceed the threshold. This satisfies the requirement for conditional model registration within SageMaker Pipelines, unlike a processing or callback step.
Processing step
Transform step
RegisterModel step
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Practice this domain24% of exam · 6 sample questions below
A machine learning engineer is monitoring a deployed model for data drift. The input features are a mix of categorical and numerical columns. The baseline is from the training data. Which SageMaker Model Monitor feature should they enable to detect changes in the distribution of each feature over time?
Bias drift monitoring
Data quality monitoring
Data quality monitoring computes distribution metrics per feature against the training baseline, handling numerical and categorical columns separately, and emits violations when distributions shift. This satisfies the requirement to detect per-feature distribution changes over time.
Model quality monitoring
Feature attribution drift monitoring
A team receives alerts that their SageMaker endpoint latency has increased significantly. They check CloudWatch metrics and see Invocations rising, but ModelLatency remains stable. Which metric should they investigate to find the source of the increased latency?
OverheadLatency
OverheadLatency measures time spent outside the model, covering request routing, queueing and response handling. Since Invocations rose while ModelLatency stayed flat, the added delay sits in this overhead, not in model execution, pinpointing the source.
ModelLatency
5XXError
4XXError
A data scientist wants to track the lineage of models, datasets, and training jobs in SageMaker. Which SageMaker feature should they use to capture these relationships as artifacts and actions?
SageMaker Model Registry
SageMaker Experiments
SageMaker ML Lineage Tracking
SageMaker ML Lineage Tracking automatically records relationships between datasets, training jobs and model artifacts as lineage entities, capturing both artifacts and actions. This directly satisfies the stem's requirement to track provenance across the machine learning workflow, which generic logging or experiment tracking alone would not provide.
SageMaker Feature Store
A team has deployed a real-time inference endpoint and wants to automatically scale based on CPU utilization. Which scaling policy type should they use with Application Auto Scaling for SageMaker endpoints?
Target tracking scaling
Target tracking scaling adjusts capacity automatically to hold a chosen metric, such as CPU utilisation, at a specified target value. For SageMaker real-time endpoints, Application Auto Scaling creates the required CloudWatch alarms and scales instances in or out, directly satisfying the stem's requirement to scale on CPU utilisation without manually defining thresholds.
Step scaling
Predictive scaling
Simple scaling
A company deploys a model for fraud detection. They need to monitor for bias after deployment, specifically whether the model's false positive rate changes across demographic groups over time. Which SageMaker feature should they use?
SageMaker Model Monitor – Model Quality
SageMaker Model Monitor – Feature Attribution Drift
SageMaker Clarify (post-deployment bias monitoring)
SageMaker Clarify's post-deployment bias monitoring continuously evaluates live endpoint traffic, computing metrics such as false positive rate disparity across demographic groups over time. This directly satisfies the requirement to detect bias drift after deployment, which static pre-training analysis cannot address.
SageMaker Model Monitor – Data Quality
A company wants to reduce costs for a production SageMaker endpoint that has predictable traffic patterns. They have purchased a Savings Plan. What additional step can they take to further optimize costs while maintaining performance?
Use SageMaker Inference Recommender to right-size the endpoint
Inference Recommender profiles the model against candidate instance types and configurations, identifying the cheapest instance that still meets latency and throughput targets. This right-sizes the endpoint, so the Savings Plan discount applies to a smaller, better-matched instance, compounding the cost reduction.
Reduce the number of instances to one, regardless of load
Switch from real-time to batch inference
Disable auto-scaling
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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: ML Model Development, Data Preparation for Machine Learning, 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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