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 document chunks at query time, ensuring the chatbot always answers from the latest uploaded documents without any model retraining.
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
AUC is a common binary classification metric and is available in XGBoost.
A team is training a large language model using SageMaker with multiple GPUs. They need to reduce training time by splitting the model across devices due to memory constraints. Which distributed training strategy should they use?
SageMaker Distributed Data Parallel (SMDDP)
Data parallelism
SageMaker Distributed Model Parallel (SMDMP)
Model parallelism
Model parallelism splits the model across devices, reducing memory per device.
A machine learning engineer is using SageMaker Debugger to monitor training jobs. They want to capture tensors every 100 steps but only for the first 500 steps. Which configuration should they set in the Debugger hook?
collection_configs with save_interval=500 and end_step=100
collection_configs with start_step=100 and end_step=500
collection_configs with save_interval=100 and end_step=500
This configures saving every 100 steps and stopping after step 500.
Use SageMaker Debugger rules to filter steps
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
Autopilot can generate explainability reports including global feature importance.
A team is fine-tuning a foundation model using LoRA in SageMaker. They want to reduce memory usage during training. Which instance type is optimized for cost-effective fine-tuning with LoRA?
ml.g5.2xlarge
g5 instances offer a good balance of performance and cost for fine-tuning with LoRA.
ml.p3.2xlarge
ml.c5.2xlarge
ml.trn1.2xlarge
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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 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 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
ml.g4dn instances offer GPU acceleration and are designed for low-latency, real-time inference.
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)
MME dynamically loads models from Amazon S3 onto shared instances, minimizing cost for many infrequently used models.
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
Caching compares step hash and skips execution if unchanged.
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
Production variants allow traffic splitting; setting initial weight to 5% on the new variant achieves a canary.
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
Neo optimizes models for target hardware, including ARM CPUs, using its compiler.
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
Condition step allows branching based on a Boolean condition, such as metric threshold.
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 compares the distributions of input features against a baseline to detect statistical and schema drift.
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 captures infrastructure overhead; an increase here explains the total latency rise when ModelLatency is unchanged.
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
Lineage Tracking explicitly models artifacts, actions, and contexts to provide end-to-end reproducibility.
SageMaker Feature Store
A financial services company must deploy a SageMaker endpoint that processes sensitive customer data. They require that all traffic between the endpoint and the model containers be encrypted, and that the endpoint cannot be accessed from outside a specific VPC. Which combination of settings should they use?
Use a private VPC and enable data encryption at rest using KMS
Enable inter-container traffic encryption and configure the endpoint with VPC-only mode
VPC-only mode makes the endpoint only accessible from the VPC, and inter-container traffic encryption encrypts data between containers.
Enable network isolation mode and inter-container traffic encryption
Deploy the endpoint in a private subnet and use a VPC endpoint for SageMaker API
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 automatically maintains a target metric value, such as average CPU utilization.
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 can be configured to run bias monitoring jobs that detect drift in fairness metrics after deployment.
SageMaker Model Monitor – Data Quality
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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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