| Exam Name | MLA-C01 Practice Exam – AWS Certified Machine Learning Engineer Associate (2026 Updated) |
|---|---|
| Exam Provider | Amazon Web Services (AWS) |
| Certification Type | Associate-Level Certification (Machine Learning Engineering, Data Processing & Model Deployment on AWS) |
| Total Practice Questions | 90 Advanced MCQs (Scenario-Based + Data Prep + Modeling + Evaluation + Deployment) |
| Exam Domains Covered | • Data Preparation (cleaning, feature engineering, encoding, scaling) • Modeling (classification, regression, clustering, time-series) • Evaluation (metrics, validation, bias-variance trade-off) • Deployment (real-time endpoints, batch inference, CI/CD) • Monitoring & Optimization (drift detection, retraining, performance tuning) • AWS ML Services (SageMaker, Feature Store, Model Monitor, pipelines) |
| Questions in Real Exam | • Total: ~65 Questions • Scenario-based with practical ML workflows • Focus on data handling, model selection, and evaluation strategies |
| Exam Duration | • Total Time: 130 Minutes • Mix of conceptual and applied ML questions • Requires hands-on AWS ML experience |
| Passing Score | • Scaled Score: 720 / 1000 • Requires solid understanding of ML fundamentals and AWS services • Emphasis on real-world problem-solving |
| Question Format | • Multiple Choice & Multiple Response • Scenario-Based ML Problem Solving • Data Preprocessing & Feature Engineering Questions • Model Evaluation & Optimization Cases • Deployment & Monitoring Scenarios |
| Difficulty Level | Intermediate to Advanced (Hands-On ML + Real Exam Scenarios) |
| Key Knowledge Areas | • Data preprocessing (handling missing values, encoding, scaling) • Feature engineering and selection techniques • Model types (classification, regression, clustering, time-series) • Evaluation metrics (accuracy, precision, recall, F1, RMSE) • Bias-variance trade-off and overfitting prevention • Deployment strategies (SageMaker endpoints, batch transform) • Monitoring (model drift, feature drift, performance tracking) • AWS ML services (SageMaker, pipelines, Feature Store) |
| Common Exam Traps | • Data leakage between training and test datasets • Using wrong evaluation metrics (e.g., accuracy for imbalanced data) • Ignoring feature scaling for certain algorithms • Overfitting due to high model complexity • Misinterpreting precision vs recall trade-offs • Not considering drift detection and retraining strategies • Choosing incorrect deployment method (batch vs real-time) |
| Skills Developed | • Building end-to-end machine learning pipelines • Designing scalable data preprocessing workflows • Selecting and tuning models effectively • Evaluating models using appropriate metrics • Deploying ML models in production environments • Monitoring and maintaining ML systems over time |
| Study Strategy | • Focus on real-world ML scenarios and workflows • Practice data preprocessing and feature engineering techniques • Understand evaluation metrics and when to use them • Learn SageMaker services and deployment options • Study model tuning, regularization, and cross-validation • Take timed mock exams and review explanations • Identify common exam traps and avoid them |
| Best For | • Machine learning engineers and data scientists • Software developers working with ML applications • Cloud engineers implementing ML solutions on AWS • Professionals transitioning into ML engineering roles |
| Career Benefits | • Validates practical machine learning engineering skills • Opens roles in ML engineering, data science, and AI development • Enhances expertise in AWS-based ML workflows • Increases earning potential in data-driven industries • Builds foundation for advanced ML and AI certifications |
| Updated | 2026 Latest Version – Based on AWS MLA-C01 Exam Guide & Real Exam Patterns |
1.
A dataset contains missing values. What is the BEST first step?
A. Train model
B. Handle missing values
C. Deploy model
D. Ignore
Answer: B
Rationale: Missing values can distort model performance and lead to errors. Handling them through imputation or removal ensures data quality and improves model reliability.
2.
A developer wants scalable data storage for ML. What is BEST?
A. EC2
B. S3
C. RDS
D. DynamoDB
Answer: B
Rationale: S3 provides highly durable, scalable storage ideal for large datasets used in machine learning workflows.
3.
A developer wants to train models without managing infrastructure. What is BEST?
A. EC2
B. SageMaker
C. S3
D. RDS
Answer: B
Rationale: SageMaker provides managed ML training, removing infrastructure overhead.
4.
A classification model predicts categories. What is BEST metric?
A. RMSE
B. Accuracy
C. MAE
D. MSE
Answer: B
Rationale: Accuracy measures correct predictions and is suitable for classification tasks.
5.
A regression model predicts continuous values. What is BEST metric?
A. Accuracy
B. RMSE
C. Precision
D. Recall
Answer: B
Rationale: RMSE measures prediction error magnitude in regression.
6.
A developer wants real-time predictions. What is BEST?
A. Batch
B. SageMaker endpoint
C. S3
D. EC2
Answer: B
Rationale: Endpoints provide real-time inference.
7.
A developer wants batch predictions. What is BEST?
A. Endpoint
B. Batch transform
C. EC2
D. S3
Answer: B
Rationale: Batch transform processes large datasets efficiently.
8.
A model overfits training data. What is BEST?
A. Increase complexity
B. Regularization
C. Ignore
D. EC2
Answer: B
Rationale: Regularization reduces overfitting.
9.
A developer wants feature scaling. What is BEST?
A. Ignore
B. Normalize data
C. EC2
D. S3
Answer: B
Rationale: Scaling improves model performance.
10.
A developer wants data visualization. What is BEST?
A. CloudWatch
B. SageMaker notebooks
C. Config
D. Lambda
Answer: B
Rationale: Notebooks enable analysis.
11.
A developer wants hyperparameter tuning. What is BEST?
A. Manual
B. SageMaker tuning jobs
C. EC2
D. S3
Answer: B
Rationale: Automated tuning improves performance.
12.
A developer wants model versioning. What is BEST?
A. Hardcode
B. SageMaker model registry
C. EC2
D. S3
Answer: B
Rationale: Registry tracks versions.
13.
A developer wants pipeline automation. What is BEST?
A. Manual
B. SageMaker pipelines
C. EC2
D. S3
Answer: B
Rationale: Pipelines automate workflows.
14.
A developer wants feature storage. What is BEST?
A. S3
B. SageMaker Feature Store
C. EC2
D. RDS
Answer: B
Rationale: Feature Store manages features.
15.
A developer wants monitoring. What is BEST?
A. CloudWatch
B. CloudTrail
C. Config
D. Lambda
Answer: A
Rationale: CloudWatch monitors metrics.
16.
A developer wants logging. What is BEST?
A. CloudWatch Logs
B. CloudTrail
C. Config
D. Lambda
Answer: A
Rationale: Logs enable debugging.
17.
A developer wants secure data. What is BEST?
A. IAM
B. KMS
C. CloudWatch
D. Lambda
Answer: B
Rationale: KMS encrypts data.
18.
A developer wants authentication. What is BEST?
A. IAM
B. Cognito
C. S3
D. EC2
Answer: B
Rationale: Cognito manages users.
19.
A developer wants scalable APIs. What is BEST?
A. API Gateway + Lambda
B. EC2
C. S3
D. RDS
Answer: A
Rationale: Serverless APIs scale automatically.
20.
A developer wants CI/CD. What is BEST?
A. CodePipeline
B. EC2
C. S3
D. RDS
Answer: A
Rationale: CodePipeline automates deployment.
21.
A developer wants anomaly detection. What is BEST?
A. Classification
B. Unsupervised learning
C. EC2
D. S3
Answer: B
Rationale: Unsupervised models detect anomalies.
22.
A developer wants clustering. What is BEST?
A. Regression
B. K-means
C. EC2
D. S3
Answer: B
Rationale: K-means clusters data.
23.
A developer wants text classification. What is BEST?
A. Regression
B. NLP model
C. EC2
D. S3
Answer: B
Rationale: NLP handles text tasks.
24.
A developer wants image recognition. What is BEST?
A. Regression
B. CNN
C. EC2
D. S3
Answer: B
Rationale: CNNs process images.
25.
A developer wants feature engineering. What is BEST?
A. Ignore
B. Transform features
C. EC2
D. S3
Answer: B
Rationale: Feature engineering improves performance.
26.
A developer wants model evaluation. What is BEST?
A. Ignore
B. Validation dataset
C. EC2
D. S3
Answer: B
Rationale: Validation ensures generalization.
27.
A developer wants deployment automation. What is BEST?
A. Manual
B. CI/CD pipeline
C. EC2
D. S3
Answer: B
Rationale: Automation improves reliability.
28.
A developer wants cost optimization. What is BEST?
A. Use EC2
B. Use managed services
C. S3
D. RDS
Answer: B
Rationale: Managed services reduce overhead.
29.
A developer wants high availability. What is BEST?
A. Single AZ
B. Multi-AZ
C. EC2
D. S3
Answer: B
Rationale: Multi-AZ ensures redundancy.
30.
A developer wants production ML system. What is BEST?
A. Single tool
B. End-to-end pipeline (data → train → deploy → monitor)
C. EC2
D. S3
Answer: B
Rationale: A full pipeline ensures scalability, reliability, and maintainability of ML systems.
31.
A model performs well on training data but poorly on validation data. What is the issue?
A. Underfitting
B. Overfitting
C. Data scaling
D. EC2
Answer: B
Rationale: Overfitting occurs when a model learns noise and patterns specific to training data, resulting in poor generalization on unseen validation data.
32.
A dataset contains categorical features. What is BEST preprocessing?
A. Ignore
B. One-hot encoding
C. EC2
D. S3
Answer: B
Rationale: One-hot encoding converts categorical variables into numerical format suitable for ML algorithms.
33.
A developer wants to prevent data leakage. What is BEST?
A. Use all data
B. Separate training and test sets
C. EC2
D. S3
Answer: B
Rationale: Data leakage occurs when test data influences training. Proper separation ensures unbiased evaluation.
34.
A model has high bias. What is BEST solution?
A. Increase complexity
B. Reduce features
C. EC2
D. S3
Answer: A
Rationale: High bias indicates underfitting. Increasing model complexity allows capturing more patterns.
35.
A model has high variance. What is BEST solution?
A. Increase complexity
B. Regularization
C. EC2
D. S3
Answer: B
Rationale: Regularization reduces overfitting by penalizing complexity.
36.
A dataset is imbalanced. What is BEST approach?
A. Ignore
B. Resampling or class weighting
C. EC2
D. S3
Answer: B
Rationale: Imbalanced datasets require techniques like oversampling or weighting to avoid biased predictions.
37.
A developer wants cross-validation. What is BEST?
A. Single split
B. K-fold cross-validation
C. EC2
D. S3
Answer: B
Rationale: K-fold cross-validation provides more robust evaluation.
38.
A developer wants feature importance. What is BEST?
A. Ignore
B. Model-based importance metrics
C. EC2
D. S3
Answer: B
Rationale: Feature importance helps understand model behavior.
39.
A developer wants dimensionality reduction. What is BEST?
A. Increase features
B. PCA
C. EC2
D. S3
Answer: B
Rationale: PCA reduces feature space while preserving variance.
40.
A developer wants anomaly detection. What is BEST?
A. Classification
B. Isolation Forest
C. EC2
D. S3
Answer: B
Rationale: Isolation Forest is effective for anomaly detection.
41.
A developer wants time-series forecasting. What is BEST?
A. Classification
B. ARIMA or forecasting model
C. EC2
D. S3
Answer: B
Rationale: Time-series models capture temporal patterns.
42.
A developer wants to avoid overfitting. What is BEST?
A. Increase epochs
B. Early stopping
C. EC2
D. S3
Answer: B
Rationale: Early stopping halts training when validation performance declines.
43.
A developer wants model interpretability. What is BEST?
A. Ignore
B. SHAP values
C. EC2
D. S3
Answer: B
Rationale: SHAP explains predictions.
44.
A developer wants real-time inference scaling. What is BEST?
A. EC2 manual
B. SageMaker autoscaling endpoint
C. S3
D. RDS
Answer: B
Rationale: Autoscaling endpoints handle traffic dynamically.
45.
A developer wants batch inference optimization. What is BEST?
A. Endpoint
B. Batch transform job
C. EC2
D. S3
Answer: B
Rationale: Batch transform is efficient for offline predictions.
46.
A developer wants hyperparameter tuning at scale. What is BEST?
A. Manual
B. SageMaker tuning job
C. EC2
D. S3
Answer: B
Rationale: Automated tuning explores parameter space efficiently.
47.
A developer wants experiment tracking. What is BEST?
A. Manual
B. SageMaker Experiments
C. EC2
D. S3
Answer: B
Rationale: Experiments track runs and results.
48.
A developer wants pipeline automation. What is BEST?
A. Manual
B. SageMaker pipelines
C. EC2
D. S3
Answer: B
Rationale: Pipelines automate workflows.
49.
A developer wants feature consistency. What is BEST?
A. Manual
B. Feature Store
C. EC2
D. S3
Answer: B
Rationale: Feature Store ensures consistency between training and inference.
50.
A developer wants monitoring for drift. What is BEST?
A. CloudTrail
B. SageMaker Model Monitor
C. Config
D. Lambda
Answer: B
Rationale: Model Monitor detects drift.
51.
A developer wants secure ML pipelines. What is BEST?
A. Public
B. IAM + encryption
C. EC2
D. S3
Answer: B
Rationale: Security best practices protect pipelines.
52.
A developer wants logging. What is BEST?
A. CloudWatch Logs
B. CloudTrail
C. Config
D. Lambda
Answer: A
Rationale: Logs enable debugging.
53.
A developer wants monitoring alerts. What is BEST?
A. CloudWatch alarms
B. CloudTrail
C. Config
D. Lambda
Answer: A
Rationale: Alerts notify issues.
54.
A developer wants CI/CD for ML. What is BEST?
A. CodePipeline
B. EC2
C. S3
D. RDS
Answer: A
Rationale: CodePipeline automates ML deployment.
55.
A developer wants scalable storage. What is BEST?
A. S3
B. EC2
C. RDS
D. DynamoDB
Answer: A
Rationale: S3 scales automatically.
56.
A developer wants authentication. What is BEST?
A. IAM
B. Cognito
C. S3
D. EC2
Answer: B
Rationale: Cognito manages users.
57.
A developer wants encryption. What is BEST?
A. IAM
B. KMS
C. CloudWatch
D. Lambda
Answer: B
Rationale: KMS manages encryption keys.
58.
A developer wants API scaling. What is BEST?
A. API Gateway + Lambda
B. EC2
C. S3
D. RDS
Answer: A
Rationale: Serverless APIs scale.
59.
A developer wants high availability. What is BEST?
A. Single AZ
B. Multi-AZ
C. EC2
D. S3
Answer: B
Rationale: Multi-AZ ensures redundancy.
60.
A developer wants production ML system. What is BEST?
A. Single model
B. End-to-end ML pipeline
C. EC2
D. S3
Answer: B
Rationale: A full pipeline ensures reliability, scalability, and maintainability.
61.
A model uses future data during training for time-series forecasting. What is the issue?
A. Overfitting
B. Data leakage
C. Underfitting
D. EC2
Answer: B
Rationale: Using future data introduces leakage because the model gains information it wouldn’t have in real-world predictions, leading to overly optimistic results.
62.
A dataset has highly correlated features. What is BEST?
A. Keep all
B. Remove redundant features
C. EC2
D. S3
Answer: B
Rationale: Highly correlated features can cause multicollinearity, which negatively impacts model stability and interpretability.
63.
A developer wants to improve recall in a classification model. What is BEST?
A. Increase threshold
B. Decrease threshold
C. EC2
D. S3
Answer: B
Rationale: Lowering the classification threshold increases recall by capturing more positives, though it may reduce precision.
64.
A model performs poorly due to skewed feature distribution. What is BEST?
A. Ignore
B. Log transformation
C. EC2
D. S3
Answer: B
Rationale: Transforming skewed data improves model learning and stability.
65.
A developer wants to evaluate ranking models. What is BEST?
A. Accuracy
B. NDCG
C. RMSE
D. MAE
Answer: B
Rationale: NDCG evaluates ranking quality based on position and relevance.
66.
A developer wants to detect concept drift. What is BEST?
A. Ignore
B. Monitor input/output distributions
C. EC2
D. S3
Answer: B
Rationale: Drift detection compares distributions over time.
67.
A developer wants to improve precision. What is BEST?
A. Lower threshold
B. Increase threshold
C. EC2
D. S3
Answer: B
Rationale: Increasing threshold reduces false positives.
68.
A developer wants to avoid multicollinearity. What is BEST?
A. Add features
B. Remove correlated features
C. EC2
D. S3
Answer: B
Rationale: Reduces instability.
69.
A developer wants balanced evaluation. What is BEST?
A. Accuracy
B. F1-score
C. RMSE
D. MAE
Answer: B
Rationale: F1 balances precision and recall.
70.
A developer wants feature selection. What is BEST?
A. Random
B. Recursive feature elimination
C. EC2
D. S3
Answer: B
Rationale: RFE selects important features.
71.
A developer wants to reduce training time. What is BEST?
A. Increase features
B. Reduce feature set
C. EC2
D. S3
Answer: B
Rationale: Fewer features reduce computation.
72.
A developer wants to handle outliers. What is BEST?
A. Ignore
B. Remove or cap outliers
C. EC2
D. S3
Answer: B
Rationale: Outliers distort models.
73.
A developer wants better generalization. What is BEST?
A. Overfit
B. Regularization
C. EC2
D. S3
Answer: B
Rationale: Regularization improves generalization.
74.
A developer wants feature scaling for neural networks. What is BEST?
A. Ignore
B. Normalize/standardize
C. EC2
D. S3
Answer: B
Rationale: Scaling improves convergence.
75.
A developer wants hyperparameter tuning efficiency. What is BEST?
A. Grid search
B. Bayesian optimization
C. EC2
D. S3
Answer: B
Rationale: Bayesian optimization is efficient.
76.
A developer wants model explainability. What is BEST?
A. Ignore
B. SHAP
C. EC2
D. S3
Answer: B
Rationale: SHAP explains predictions.
77.
A developer wants feature drift detection. What is BEST?
A. Ignore
B. Compare feature distributions over time
C. EC2
D. S3
Answer: B
Rationale: Detects drift.
78.
A developer wants batch pipeline automation. What is BEST?
A. Manual
B. Step Functions or pipelines
C. EC2
D. S3
Answer: B
Rationale: Automates workflows.
79.
A developer wants model retraining automation. What is BEST?
A. Manual
B. Scheduled pipeline
C. EC2
D. S3
Answer: B
Rationale: Keeps model updated.
80.
A developer wants evaluation dataset. What is BEST?
A. Training data
B. Separate validation/test data
C. EC2
D. S3
Answer: B
Rationale: Ensures unbiased evaluation.
81.
A developer wants scalable inference. What is BEST?
A. EC2
B. SageMaker endpoint autoscaling
C. S3
D. RDS
Answer: B
Rationale: Autoscaling handles load.
82.
A developer wants experiment reproducibility. What is BEST?
A. Manual
B. Track configs and seeds
C. EC2
D. S3
Answer: B
Rationale: Ensures repeatability.
83.
A developer wants secure pipelines. What is BEST?
A. Public
B. IAM + encryption
C. EC2
D. S3
Answer: B
Rationale: Security best practices.
84.
A developer wants logging. What is BEST?
A. CloudWatch Logs
B. CloudTrail
C. Config
D. Lambda
Answer: A
Rationale: Debugging.
85.
A developer wants monitoring alerts. What is BEST?
A. CloudWatch alarms
B. CloudTrail
C. Config
D. Lambda
Answer: A
Rationale: Alerts notify issues.
86.
A developer wants CI/CD. What is BEST?
A. CodePipeline
B. EC2
C. S3
D. RDS
Answer: A
Rationale: Automates deployment.
87.
A developer wants scalable storage. What is BEST?
A. S3
B. EC2
C. RDS
D. DynamoDB
Answer: A
Rationale: S3 scales.
88.
A developer wants authentication. What is BEST?
A. IAM
B. Cognito
C. S3
D. EC2
Answer: B
Rationale: User management.
89.
A developer wants encryption. What is BEST?
A. IAM
B. KMS
C. CloudWatch
D. Lambda
Answer: B
Rationale: Encryption keys.
90.
A developer wants production ML system. What is BEST?
A. Single model
B. End-to-end ML pipeline with monitoring
C. EC2
D. S3
Answer: B
Rationale: Full pipeline ensures scalability and reliability.