MLA-C01 Flashcards — Free AWS Certified Machine Learning Engineer Associate MLA-C01 Study Cards

Use Retrieval-Augmented Generation (RAG) with the policy documents indexed in a vector store

RAG allows the LLM to retrieve relevant document sections at inference time, so knowledge stays current without retraining.

validation:auc

Model parallelism

collection_configs with save_interval=100 and end_step=500

Use Amazon SageMaker Data Wrangler to impute missing values using built-in transforms.

Amazon SageMaker Data Wrangler provides a visual interface and built-in transforms for handling missing values efficiently at scale, without writing custom code. Glue ETL is more code-heavy, and imputation with pandas is not scalable for large datasets. Removing all rows with missing values is not always optimal and may not be efficient.

Use AWS Glue DynamicFrame to repartition the data and write it as Parquet.

AWS Glue DynamicFrames provide built-in optimizations for writing data in columnar formats like Parquet, which improves query performance through predicate pushdown and compression, and reduces storage costs by using efficient encoding. The DynamicFrame's `repartition()` method allows you to control the number of output files, and writing as Parquet directly from Glue avoids intermediate conversions, making it the most efficient choice for this task.

Apply Synthetic Minority Over-sampling Technique (SMOTE) to generate synthetic samples for the minority class.

SMOTE generates synthetic samples for the minority class by interpolating between existing minority instances, which directly addresses the severe class imbalance (95% class A, 5% class B) by creating a more balanced training dataset. This technique is particularly effective for tabular data in Amazon SageMaker, as it increases the representation of the minority class without simply duplicating existing samples, thereby reducing overfitting and improving the model's ability to learn decision boundaries for the minority class.

Remove the column from the feature set.

'CustomerID' is a unique identifier with no predictive power for churn. Including it as a feature would cause the model to memorize individual customers rather than learn generalizable patterns, leading to overfitting and poor performance on unseen data. In machine learning, such columns should be removed during data preparation to ensure the model learns from meaningful features.

Use the G.1X worker type, which provides more memory per worker compared to the Standard worker type.

Increasing the number of DPUs (Data Processing Units) in AWS Glue can improve parallelism and reduce job runtime, but it increases cost. Using G.1X worker type with more memory per worker can improve performance without increasing DPU count, offering better resource utilization. Switching to CSV may degrade performance. Using partition pruning on the source data can reduce data scanned but may not address resource consumption.

Real-time endpoint on ml.g4dn instances

Real-time endpoints on GPU instances (ml.g4dn) provide low latency and GPU acceleration, ideal for high-traffic, latency-sensitive workloads.

Use a single multi-model endpoint (MME)

Multi-model endpoints (MME) allow hosting multiple models on a single endpoint, sharing instances and reducing costs, especially for infrequently used models.

Pipeline caching

SageMaker Pipelines caching stores step outputs; if the step configuration and inputs are identical, the pipeline reuses the cached output, skipping execution.

Data quality monitoring

Data quality monitoring in SageMaker Model Monitor detects schema and statistical drift (including distribution changes) for input features. Model quality monitors predictions vs. ground truth, not input features.

OverheadLatency

OverheadLatency measures the time taken by the SageMaker infrastructure to handle requests before and after model inference, including request routing, authentication, and response processing. Since ModelLatency is stable but total endpoint latency has increased, the extra time must be in the overhead component, making OverheadLatency the correct metric to investigate.

SageMaker ML Lineage Tracking

SageMaker ML Lineage Tracking creates a graph of artifacts (datasets, models) and actions (training jobs, endpoints) to track the provenance of ML workflows.

Enable inter-container traffic encryption and configure the endpoint with VPC-only mode

Inter-container traffic encryption ensures that data between the SageMaker endpoint and the model containers is encrypted in transit, typically using TLS. Configuring the endpoint with VPC-only mode restricts all inference traffic to the specified VPC, preventing any access from outside that VPC. This combination directly addresses the requirements for encrypted inter-container traffic and VPC-restricted access.