Pass the Amazon Web Services AWS Certified Associate MLA-C01 Questions and answers with CertsForce

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.

The issue is caused by oversized Parquet files that cannot be efficiently read into memory during training. The most effective and scalable solution is to repartition the dataset into smaller Parquet files.

AWS best practices for large-scale ML training recommend optimizing data layout, not simply increasing memory. By using Apache Spark on Amazon EMR, the ML engineer can repartition the Parquet files into smaller chunks that can be streamed and processed efficiently by SageMaker training jobs.

Attaching EBS volumes (Option A) increases storage capacity but does not solve in-memory constraints. Changing to memory-optimized instances (Option C) increases cost and does not address long-term scalability. SMDDP (Option D) distributes gradients and computation, not dataset file sizes.

Therefore, repartitioning the Parquet files is the correct solution.

Dynamic data masking allows you to control how sensitive data is presented to users at query time, without modifying or storing transformed versions of the source data. Amazon Redshift supports dynamic data masking, which can be implemented with minimal effort. This solution ensures that the data scientist can access the required information while sensitive data remains protected, meeting the requirements efficiently and with the least implementation effort.

For asynchronous inference on large datasets, AWS recommends SageMaker batch transform, which is designed for offline and large-scale inference workloads. Batch transform jobs do not require real-time endpoints and can process large volumes of data efficiently.

For monitoring data quality, AWS provides Amazon SageMaker Model Monitor, which supports scheduled monitoring of data quality, schema drift, and feature distribution drift. Model Monitor can publish metrics to Amazon CloudWatch and trigger alerts when thresholds are breached.

The other options lack native ML-specific monitoring capabilities. CloudTrail audits API activity, not data quality. Glue, Batch, and ECS would require extensive custom monitoring logic.

Therefore, SageMaker batch transform combined with Model Monitor is the correct solution.

This is a classic class imbalance problem, where fraudulent transactions (minority class) are severely underrepresented. AWS documentation for SageMaker Data Wrangler recommends SMOTE (Synthetic Minority Oversampling Technique) as an effective approach for improving model performance in such scenarios.

SMOTE generates synthetic minority samples by interpolating between existing minority class examples. This improves the model’s ability to learn decision boundaries without simply duplicating data, which can cause overfitting.

Random undersampling removes valuable majority class data, reducing overall model robustness. Random oversampling duplicates data and increases overfitting risk. Changing algorithms does not address the root cause.

AWS best practices highlight SMOTE as the preferred technique for fraud detection and other highly imbalanced datasets.

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

For near real-time anomaly detection on streaming IoT data, AWS recommends Amazon Managed Service for Apache Flink. Flink is designed for stateful, low-latency stream processing and is well suited for time-series sensor analytics.

A key requirement is application resilience during deployments. Managed Flink supports system rollback, which automatically reverts to the last stable application version if a new deployment fails. This capability ensures uninterrupted processing even if application bugs are introduced, meeting the company’s reliability requirement.

Manual rollback (Option B) introduces operational risk and delays. Amazon Data Firehose (Options C and D) is a delivery service and does not support complex anomaly detection logic or application version rollback.

Therefore, using Managed Flink with system rollback enabled is the correct solution.

Amazon Comprehend provides the ImportModel API operation, which allows you to copy a custom model between AWS accounts. By creating a resource-based IAM policy on the model in Account A, you can grant Account B the necessary permissions to access and import the model. This approach requires minimal development effort and is the AWS-recommended method for sharing custom models across accounts.

The requirement is to quickly determine whether the target variable is predictable, with minimal development effort. Amazon SageMaker Autopilot is specifically designed for this purpose.

SageMaker Autopilot automatically handles data preprocessing, feature engineering, algorithm selection, model training, and evaluation. It generates multiple candidate models and provides detailed performance metrics, allowing teams to quickly assess predictability without writing custom code.

Option B requires significant manual development. Option C is incorrect because Amazon Macie is a data security and classification service, not an ML modeling service. Option D is unsuitable because Amazon Bedrock models are not intended for structured tabular prediction tasks.

Therefore, SageMaker Autopilot provides the fastest and least effort solution.

The task described is unsupervised topic modeling, where topics are unknown in advance. Latent Dirichlet Allocation (LDA) is a probabilistic generative model specifically designed to discover latent topics from a corpus of documents without labeled data. AWS provides LDA as a built-in algorithm in Amazon SageMaker, making it well suited for this requirement.

LDA models documents as mixtures of topics and topics as mixtures of words, enabling interpretable topic discovery at scale. This aligns precisely with the need to organize documents into topics when the topics are not predefined.

BlazingText is optimized for word embeddings and supervised text classification, not topic modeling. Sequence-to-sequence models are used for translation or summarization. Object2Vec creates embeddings but does not itself perform topic discovery without additional clustering steps.

Therefore, LDA is the correct and purpose-built solution.

The requirement is event-driven automation when new data arrives in Amazon S3, followed by training and model registration. Amazon EventBridge natively supports S3 object creation events and can trigger downstream workflows immediately.

By using EventBridge to start an AWS Step Functions workflow that includes a training step and a SageMaker Model Registry registration step, the pipeline runs automatically as soon as new data is uploaded—well within the 24-hour requirement.

Option A introduces unnecessary polling and delay. Option B is time-based and does not ensure alignment with data readiness. Option C is invalid because S3 Lifecycle rules manage object transitions, not workflow execution.

Therefore, EventBridge-triggered Step Functions is the correct solution.