MLA-C01 Amazon Web Services AWS Certified Machine Learning Engineer - Associate Free Practice Exam Questions (2026 Updated)

 Problem Description:

The training dataset has a class imbalance, meaning one class (e.g., fraudulent transactions) has fewer samples compared to the majority class (e.g., non-fraudulent transactions). This imbalance affects the model’s ability to learn patterns from the minority class.

 Why SageMaker Data Wrangler?

SageMaker Data Wrangler provides a built-in operation called "Balance Data," which includes oversampling and undersampling techniques to address class imbalances.

Oversampling the minority class replicates samples of the minority class, ensuring the algorithm receives balanced inputs without significant additional operational overhead.

 Steps to Implement:

Import the dataset into SageMaker Data Wrangler.

Apply the "Balance Data" operation and configure it to oversample the minority class.

Export the balanced dataset for training.

 Advantages:

Ease of Use: Minimal configuration is required.

Integrated Workflow: Works seamlessly with the SageMaker ecosystem for preprocessing and model training.

Time Efficiency: Reduces manual effort compared to external tools or scripts.

AWS provides Amazon SageMaker Pipelines as the native CI/CD solution for ML. Pipelines can automatically trigger retraining when new data arrives in Amazon S3, handle large datasets (such as 10 GB files), and orchestrate preprocessing, training, evaluation, and deployment steps.

For governance and auditing, Amazon SageMaker Model Registry integrates directly with Pipelines to manage model versions, approval status, and metadata such as training metrics. This combination eliminates the need for custom tracking logic and ensures traceability across the ML lifecycle.

Option A lacks native ML lineage and version governance. Option C is unsuitable for large data and complex workflows. Option D is manual and not scalable.

Therefore, using SageMaker Pipelines with the Model Registry is the correct and AWS-recommended solution.

This dataset shows severe class imbalance, with only 2% of records representing patients with the disease. AWS ML best practices recommend correcting imbalance only in the training dataset, while keeping validation and test sets representative of real-world distributions.

Synthetic Minority Oversampling Technique (SMOTE) generates synthetic samples of the minority class by interpolating between existing minority examples. This improves the model’s ability to learn disease-related patterns without discarding data.

PCA is a dimensionality reduction method, not an oversampling technique. Oversampling the majority class worsens imbalance. Altering the test dataset would invalidate evaluation results.

Therefore, applying SMOTE to the training dataset is the correct approach.

In regression problems such as house price prediction, extreme values can significantly distort model learning. In this dataset, the presence of a large mansion and an extremely small apartment represents clear outliers in the Square Meters feature. According to AWS Machine Learning best practices, outliers can disproportionately influence loss functions (such as mean squared error), leading to poor predictions for the majority of typical data points.

Removing these outliers helps the model focus on learning patterns that apply to the majority of houses and apartments, which aligns with the requirement to produce accurate predictions for typical properties. After removing outliers, applying a log transformation to the Square Meters feature further improves model performance by reducing skewness and stabilizing variance. Log transformations are commonly recommended in AWS and general ML documentation when numerical features span multiple orders of magnitude.

Option B is incorrect because normalization alone does not address the undue influence of extreme outliers. Option C and D are incorrect because one-hot encoding is intended for categorical variables, not continuous numerical features such as square meters.

Therefore, removing outliers and applying a log transformation is the most statistically sound preprocessing approach.

Scenario: The company needs to run a batch job for 90 minutes every weekend over the next 6 months. The processing can handle interruptions, and cost-effectiveness is a priority.

Why Spot Instances?

Cost-Effective: Spot Instances provide up to 90% savings compared to On-Demand Instances, making them the most cost-effective option for batch processing.

Interruption Tolerance: Since the processing can tolerate interruptions, Spot Instances are suitable for this workload.

Batch-Friendly: Spot Instances can be requested for specific durations or automatically re-requested in case of interruptions.

Steps to Implement:

Create a Spot Instance Request:

Use the EC2 console or CLI to request Spot Instances with desired instance type and duration.

Use Auto Scaling: Configure Spot Instances with an Auto Scaling group to handle instance interruptions and ensure job completion.

Run the Batch Job: Use tools like AWS Batch or custom scripts to manage the processing.

Comparison with Other Options:

Reserved Instances: Suitable for predictable, continuous workloads, but less cost-effective for a job that runs only once a week.

On-Demand Instances: More expensive and unnecessary given the tolerance for interruptions.

Dedicated Instances: Best for isolation and compliance but significantly more costly.

AWS recommends shadow testing to evaluate a new model against a production model with minimal operational overhead. Using production variants on a single SageMaker endpoint allows traffic to be routed to multiple models without managing additional endpoints.

With a shadow variant, the new model receives a copy of live traffic but does not affect production responses. Performance metrics such as latency, accuracy, and error rates can be compared directly against the current model using Amazon CloudWatch metrics. This approach is natively supported by Amazon SageMaker Endpoints.

Options A, B, and D introduce unnecessary complexity by requiring additional endpoints, traffic routing infrastructure, or custom code.

Therefore, deploying the new model as a shadow variant on the same endpoint is the most efficient solution.

AWS Glue is a serverless data integration service that is well-suited for creating data ingestion pipelines, especially when raw data is stored in Amazon S3. It can clean, transform, and catalog data, making it accessible for downstream ML tasks.

Amazon SageMaker Studio Classic provides a comprehensive environment for building, training, and deploying ML models. It includes built-in tools and capabilities to create efficient model deployment pipelines with minimal setup.

This combination ensures seamless integration of data ingestion and ML model deployment with minimal operational overhead.

Apache Parquet is a columnar storage file format optimized for complex and large datasets. It provides efficient reading and processing by accessing only the required columns, which reduces I/O and speeds up data handling. This makes it ideal for use with Amazon SageMaker Canvas, where minimizing processing time is important for training ML models. Parquet is also compatible with S3 and widely supported in data analytics and ML workflows.

Network ACLs (Access Control Lists) operate at the subnet level and allow for rules to explicitly deny traffic from specific IP addresses. By creating an inbound rule in the network ACL to deny traffic from the suspicious IP address, the company can block traffic to the Amazon SageMaker domain from that IP. This approach works because network ACLs are evaluated before traffic reaches the security groups, making them effective for blocking traffic at the subnet level.

Amazon SageMaker batch transform is ideal for obtaining inferences from large datasets in an asynchronous manner, as it processes data in batches rather than requiring real-time inputs.

SageMaker Model Monitor allows scheduled monitoring of data quality, detecting shifts in input data characteristics, and generating alerts when changes in data quality occur.

This solution provides a fully managed, efficient way to handle both asynchronous inference and data quality monitoring with minimal operational overhead.

Amazon SageMaker supports direct file system access for training jobs through Amazon FSx. AWS documentation states that FSx for NetApp ONTAP file systems can be mounted directly to SageMaker training instances when they are located in the same VPC.

Mounting the FSx file system allows SageMaker to stream large datasets efficiently without copying data into Amazon S3. This is ideal for very large datasets such as 6 TB of image data and avoids unnecessary storage duplication.

Options involving SageMaker Data Wrangler are intended for data preparation and exploration, not large-scale training data access. Mountpoint for Amazon S3 is not required and introduces additional complexity.

Therefore, Option A is the correct and AWS-aligned solution.

The scenario describes a classic overfitting problem: the XGBoost model performs well on the training dataset but poorly on the test hold-out dataset. According to AWS Machine Learning and XGBoost documentation, overfitting occurs when a model is too complex and learns noise and patterns specific to the training data rather than generalizable relationships.

One effective way to address overfitting is to reduce model complexity. Option B, reducing the number of features, simplifies the hypothesis space and lowers the risk of fitting spurious correlations. Feature reduction is a recommended best practice when the model shows a large generalization gap between training and test error.

Another effective method is to increase regularization. Option D, increasing the L2 regularization parameter (lambda in XGBoost), penalizes large weights and discourages overly complex trees. AWS documentation explicitly notes that L2 regularization helps improve generalization by smoothing model parameters and reducing variance.

Option A would worsen overfitting by increasing complexity. Option C is incorrect because reducing the number of training samples generally increases overfitting risk. Option E would decrease regularization strength and further degrade test performance.

Therefore, reducing feature complexity and increasing L2 regularization are the correct changes.

Using custom tags allows you to organize and categorize models in the SageMaker Model Registry without altering their existing groupings or affecting the integrity of the model artifacts. Tags are a lightweight and scalable way to improve model discoverability at scale, enabling the data scientists to filter and identify models by category (e.g., computer vision, NLP, speech recognition). This approach meets the requirements efficiently without introducing structural changes to the existing model registry setup.

Amazon Q Business provides built-in guardrails to control and constrain model responses. One such control is the ability to define blocked phrases, which explicitly prevents specified terms from appearing in generated responses.

Configuring the competitor’s name as a blocked phrase guarantees that Amazon Q Business will never include that name in responses, fully meeting the requirement. This approach is deterministic and does not rely on ranking or retrieval behavior.

Option B is incorrect because retrievers control what documents are fetched, not how responses are generated. Option C adds unnecessary complexity and does not guarantee exclusion in generated answers. Option D only deprioritizes content and does not prevent it from appearing.

Therefore, blocking the competitor’s name is the correct solution.

This problem describes a recommendation system with millions of records, many categorical variables, and a high-dimensional sparse feature space created by one-hot encoding. AWS documentation explicitly recommends Amazon SageMaker Factorization Machines (FM) for such use cases.

Factorization Machines are designed to handle sparse datasets efficiently and to model interactions between categorical features without explicitly enumerating all feature combinations. This capability makes FM particularly well-suited for recommendation problems such as predicting user-item interactions, including destination recommendations.

With 2,000 possible airport destinations, the target space is large and sparse. One-hot encoding further increases sparsity. Factorization Machines address this challenge by learning latent factors that capture relationships between features, even when many feature combinations are rarely observed.

Option A (CatBoost) is not an Amazon SageMaker built-in algorithm and therefore does not meet the requirement. Option B (DeepAR) is a time-series forecasting algorithm, not intended for recommendation or classification problems. Option D (k-means) is an unsupervised clustering algorithm and cannot directly predict a specific destination label.

AWS documentation explicitly lists recommendation systems and click prediction as primary use cases for the SageMaker Factorization Machines algorithm.

Therefore, Option C is the correct and AWS-verified choice.

Scenario: The ML engineer needs a low-overhead solution to query thousands of existing and new CSV objects stored in Amazon S3 based on a transaction date.

Why Athena?

Serverless: Amazon Athena is a serverless query service that allows direct querying of data stored in S3 using standard SQL, reducing operational overhead.

Ease of Use: By using the CTAS statement, the engineer can create a table with optimized partitions based on the transaction date. Partitioning improves query performance and minimizes costs by scanning only relevant data.

Low Operational Overhead: No need to manage or provision additional infrastructure. Athena integrates seamlessly with S3, and CTAS simplifies table creation and optimization.

Steps to Implement:

Organize Data in S3: Store CSV files in a bucket in a consistent format and directory structure if possible.

Configure Athena: Use the AWS Management Console or Athena CLI to set up Athena to point to the S3 bucket.

Run CTAS Statement:

CREATE TABLE processed_data

WITH (

format = 'PARQUET',

external_location = 's3://processed-bucket/',

partitioned_by = ARRAY['transaction_date']

) AS

SELECT *

FROM input_data;

This creates a new table with data partitioned by transaction date.

Query the Data: Use standard SQL queries to fetch data based on the transaction date.

The ETL process has two critical characteristics: it is long-running (6 hours) and written in R. AWS Lambda is unsuitable because it has a maximum execution time of 15 minutes. AWS Glue primarily supports Spark-based ETL and does not natively support custom R-based workloads.

AWS documentation recommends using Amazon SageMaker Processing Jobs for long-running, custom data processing workloads. Processing jobs allow users to run arbitrary code in custom Docker containers, making them ideal for migrating on-premises ETL jobs written in R.

By building a custom Docker image that includes the R runtime and required libraries and storing it in Amazon ECR, the company can run the ETL job at scale on managed infrastructure without rewriting the code.

SageMaker script mode is intended for training, not ETL. Therefore, SageMaker processing jobs with a custom container are the correct solution.

A serverless inference endpoint in Amazon SageMaker is ideal for use cases where the model is invoked infrequently, such as running one time each night. It eliminates the cost of idle resources when the model is not in use. Setting the MaxConcurrency parameter to 1 ensures cost-efficiency while supporting the required single nightly invocation. This solution minimizes costs and matches the requirement to process a small amount of data quickly.

The target_precision hyperparameter in the Amazon SageMaker linear learner controls the trade-off between precision and recall for the model. Increasing the target_precision prioritizes minimizing false positives by making the model more cautious in its predictions. This approach is effective for use cases where false positives have higher consequences than false negatives.