Amazon Web Services AWS Certified Machine Learning Engineer - Associate MLA-C01 Exam Dumps: Updated Questions & Answers (March 2026)

The key requirements are real-time processing, high throughput, and minimal operational overhead. Amazon Kinesis Data Streams is designed for ingesting thousands of events per second with low latency.

For anomaly detection on streaming data, Amazon Managed Service for Apache Flink provides a built-in Random Cut Forest (RCF) function. RCF is an unsupervised anomaly detection algorithm that works well on numerical streaming data and does not require labeled training data.

This fully managed combination eliminates the need to deploy or maintain SageMaker endpoints, EC2 instances, or custom ML pipelines. Options B and C introduce unnecessary infrastructure and model management overhead. Option D is batch-oriented and unsuitable for real-time anomaly detection.

Therefore, using Kinesis Data Streams with Flink’s built-in Random Cut Forest is the most scalable and low-overhead solution.

AWS provides Amazon SageMaker Model Monitor as a native solution for detecting data drift and model quality issues in production ML pipelines. Model Monitor continuously analyzes incoming inference data and compares it with baseline training data to identify schema drift, feature distribution drift, and data quality anomalies.

When drift thresholds are violated, Model Monitor generates CloudWatch metrics and alerts. These alerts can directly trigger an AWS Lambda function, which can then programmatically initiate a SageMaker retraining job or start a SageMaker Pipeline execution. This design is explicitly documented by AWS as the recommended architecture for automated retraining workflows.

Option A is incorrect because AWS Glue is a data integration service and does not provide ML-specific drift detection capabilities.

Option B is incorrect because Apache Flink is designed for stream processing, not ML data drift detection.

Option D is incorrect because Amazon QuickSight anomaly detection is intended for business intelligence metrics, not ML feature drift.

Therefore, using SageMaker Model Monitor with AWS Lambda automation is the correct, AWS-native solution for drift-driven retraining.

SageMaker Pipelines provides a directed acyclic graph (DAG) view for managing and visualizing ML workflows with fine-grained control. It integrates seamlessly with SageMaker Studio, offering an intuitive interface for workflow orchestration.

SageMaker ML Lineage Tracking keeps a running history of experiments and tracks the lineage of datasets, models, and training jobs. This feature supports model governance, auditing, and compliance verification requirements.

This issue occurs because target tracking auto scaling allows the endpoint to scale down to zero, and scaling up only happens after traffic arrives. At the start of the business day, no instances are running, so the first requests experience cold-start delays.

AWS documentation for Amazon SageMaker recommends using scheduled or step scaling policies when predictable traffic patterns exist. In this case, business hours are predictable, so the best practice is to proactively scale the endpoint before traffic arrives.

Option D correctly uses Amazon CloudWatch alarms with step scaling to increase the minimum instance count from zero to one at the start of the business day. This ensures at least one warm instance is ready to handle requests immediately, eliminating startup latency.

Options A, B, and C do not guarantee instance availability before traffic begins. Cooldown tuning and metric changes only react after load is detected.

Therefore, scheduled step scaling using CloudWatch alarms is the correct solution.

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.

Large Parquet files can cause out-of-memory (OOM) issues during training if individual files exceed the memory capacity of the training instances. AWS documentation recommends repartitioning large datasets into smaller files to enable efficient streaming and parallel loading.

By using Apache Spark on Amazon EMR to repartition the Parquet files, the ML engineer can split the dataset into multiple smaller files that can be read incrementally during training. This approach avoids loading a single large file into memory and improves data parallelism.

Attaching larger EBS volumes increases storage capacity but does not solve memory constraints. Switching to memory-optimized instances increases cost and is not necessary when the dataset can be restructured. SageMaker distributed data parallelism focuses on model parameter synchronization across GPUs, not dataset file size.

AWS best practices explicitly recommend partitioning large Parquet datasets to improve memory efficiency during training.

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

Amazon SageMaker automatically tracks the Invocations metric, which represents the number of API calls made to the endpoint, in Amazon CloudWatch. By adding this metric to a CloudWatch dashboard, you can monitor the endpoint's activity in real-time. Setting up a CloudWatch alarm allows the system to send notifications whenever the API call events exceed the defined threshold, meeting both the monitoring and notification requirements efficiently.

The Amazon SageMaker Model Registry is designed to manage and catalog ML models, including those hosted in Amazon ECR. By creating a model group for each model in the SageMaker Model Registry and setting up cross-account resource policies, the company can establish a central catalog in a new AWS account. This allows all models from the initial accounts to be accessible in a unified, centralized manner for better organization, management, and governance. This solution leverages existing AWS services and ensures scalability and minimal operational overhead.

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.

The problem is a time series forecasting task with historical data and categorical features. Amazon SageMaker DeepAR is purpose-built for this use case. DeepAR uses recurrent neural networks to learn temporal patterns across multiple related time series and supports categorical covariates.

The context length hyperparameter controls how much historical data the model uses as input, while the prediction length specifies how far into the future the model forecasts. Correctly setting these hyperparameters is critical for capturing trends and seasonality.

XGBoost is a general-purpose tabular algorithm and does not model temporal dependencies natively. k-means is a clustering algorithm. Random Cut Forest is used for anomaly detection, not forecasting.

Therefore, DeepAR with appropriate context and prediction lengths is the correct and AWS-recommended solution.