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To pinpoint inefficiencies inside a training script, AWS recommends using Amazon SageMaker Profiler. SageMaker Profiler provides fine-grained visibility into CPU, GPU, memory, I/O usage, and framework-level operations during training.

By adding profiler annotations directly to the training script, the ML engineer can identify bottlenecks such as inefficient data loading, synchronization delays in DDP, or idle GPU time between training steps.

CloudWatch metrics provide high-level utilization trends but cannot identify exact code-level inefficiencies. CloudTrail is an auditing service and is irrelevant to performance profiling. Model Monitor focuses on data and model quality, not training resource utilization.

Therefore, SageMaker Profiler is the correct tool.

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.

The requirement has two key constraints: detect class imbalance and balance classes without losing any existing data. AWS provides Amazon SageMaker Clarify as the native tool to detect pre-training bias, including class imbalance (CI). CI measures differences in label distributions between classes, and a CI value greater than 0 indicates imbalance.

Once imbalance is detected, the engineer must rebalance the dataset without discarding data. Random undersampling would remove samples from the majority class, violating the requirement. Instead, oversampling is required. SMOTE (Synthetic Minority Oversampling Technique) creates synthetic samples for the minority class, preserving all original data while improving class balance.

Amazon SageMaker Data Wrangler natively supports SMOTE, making it the correct AWS-managed tool for this preprocessing task.

Options C and D are incorrect because SageMaker JumpStart is used for pretrained models and solutions, not for bias detection reporting. Option A is incorrect because it uses undersampling and misinterprets CI = 0 (which actually indicates no imbalance).

Therefore, detecting imbalance with SageMaker Clarify and correcting it using SMOTE in Data Wrangler is the correct solution.

Amazon SageMaker Serverless Inference is designed for irregular or unpredictable traffic patterns. It automatically provisions and scales compute resources based on request volume and scales down to zero when idle, making it the most cost-effective option.

Serverless inference supports payloads up to 6 MB and request durations up to 60 seconds, which comfortably meets the stated constraints. Customers are billed only for actual compute usage during inference execution, not for idle capacity.

Asynchronous inference is intended for long-running jobs (up to 1 hour) and large payloads (up to 1 GB). Real-time inference requires always-on instances, increasing cost during idle periods. Batch transform is designed for offline processing.

Therefore, serverless inference is the optimal choice.

Using Amazon EventBridge with an event pattern that matches S3 upload events provides an automated, low-effort solution. When new data is uploaded to the S3 bucket, the EventBridge rule triggers the SageMaker pipeline. This approach minimizes operational overhead by eliminating the need for custom scripts or external orchestration tools while seamlessly integrating with the existing S3 and SageMaker setup.

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 Amazon QuickSight anomaly detection, the Direction parameter controls which deviations from the expected baseline are flagged as anomalies. When Direction is set to All, QuickSight detects both higher-than-expected and lower-than-expected values. This results in a larger set of detected anomalies, increasing recall.

When the Direction parameter is changed to Lower than expected, the anomaly detection algorithm filters out all higher-than-expected anomalies and only reports unusually low values. Because the scope of detection is narrowed, the total number of detected anomalies decreases. As a result, fewer true anomalies are identified overall, which reduces recall.

Since fewer data points are evaluated as potential anomalies, the frequency of anomaly identification also decreases. This behavior is consistent with AWS documentation, which explains that directional filtering limits detection to a subset of deviations and therefore reduces overall anomaly counts.

Therefore, changing the Direction from All to Lower than expected leads to decreased anomaly identification frequency and decreased recall.

When predicting continuous variables, such as apartment prices, it's essential to evaluate the model's performance using appropriate regression metrics. The Mean Absolute Error (MAE) is a widely used metric for this purpose.

Understanding Mean Absolute Error (MAE):

MAE measures the average magnitude of errors in a set of predictions, without considering their direction. It calculates the average absolute difference between predicted values and actual values, providing a straightforward interpretation of prediction accuracy.

Advantages of MAE:

Interpretability: MAE is expressed in the same units as the target variable, making it easy to understand.

Robustness to Outliers: Unlike metrics that square the errors (e.g., Mean Squared Error), MAE does not disproportionately penalize larger errors, making it more robust to outliers.

Comparison with Other Metrics:

Accuracy, AUC, F1 Score: These metrics are designed for classification tasks, where the goal is to predict discrete labels. They are not suitable for regression problems involving continuous target variables.

Mean Squared Error (MSE): While MSE also measures prediction errors, it squares the differences, giving more weight to larger errors. This can be useful in certain contexts but may be sensitive to outliers.

Conclusion:

For evaluating the performance of a model predicting apartment prices—a continuous variable—MAE is an appropriate and effective metric. It provides a clear indication of the average prediction error in the same units as the target variable, facilitating straightforward interpretation and comparison.

To minimize communication overhead during distributed training:

1. Same VPC Subnet: Ensures low-latency communication between training instances by keeping the network traffic within a single subnet.

2. Same AWS Region and Availability Zone: Reduces network latency further because cross-AZ communication incurs additional latency and costs.

3. Data in the Same Region and AZ: Ensures that the training data is accessed with minimal latency, improving performance during training.

This configuration optimizes communication efficiency and minimizes overhead.

Patient ID is a unique identifier and does not contain predictive information. Including it can cause the model to overfit by memorizing records rather than learning meaningful patterns.

AWS ML best practices recommend removing identifiers that are not causally related to the target variable. Age, medical conditions, and test results are clinically relevant features and should be retained. The target column must remain for supervised learning.

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