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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Preparation for Machine Learning (ML) | 28% | - Data preprocessing and transformation
|
| Topic 2: Deployment and Orchestration of ML Workflows | 22% | - Model deployment
|
| Topic 3: ML Model Development | 26% | - Model selection and training
|
| Topic 4: ML Solution Monitoring, Maintenance, and Security | 24% | - Monitoring and observability
|
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NEW QUESTION # 18
An ML engineer has trained a neural network by using stochastic gradient descent (SGD). The neural network performs poorly on the test set. The values for training loss and validation loss remain high and show an oscillating pattern. The values decrease for a few epochs and then increase for a few epochs before repeating the same cycle.
What should the ML engineer do to improve the training process?
Answer: A
Explanation:
An oscillating loss pattern during training with stochastic gradient descent (SGD) is a strong indicator that the learning rate is too high. When the learning rate is excessive, the optimizer takes overly large steps during gradient updates, causing the model to repeatedly overshoot the optimal minimum of the loss function. This results in unstable convergence behavior, where training and validation loss decrease briefly and then increase again in a repeating cycle.
AWS Machine Learning documentation and general deep learning best practices recommend reducing the learning rate when training loss and validation loss both remain high and fluctuate rather than steadily decreasing. Lowering the learning rate allows the optimizer to take smaller, more precise steps toward the minimum, leading to smoother convergence and improved generalization on the test dataset.
Option A, early stopping, is used primarily to prevent overfitting when validation loss increases while training loss continues to decrease. In this scenario, both losses remain high and unstable, indicating an optimization issue rather than overfitting.
Option B is incorrect because increasing the test set size does not affect the training dynamics or convergence behavior of the model.
Option C would worsen the problem, as increasing the learning rate would further amplify oscillations and instability.
Therefore, decreasing the learning rate is the correct corrective action to stabilize SGD training and improve model performance.
NEW QUESTION # 19
A company has a large collection of chat recordings from customer interactions after a product release. An ML engineer needs to create an ML model to analyze the chat data. The ML engineer needs to determine the success of the product by reviewing customer sentiments about the product.
Which action should the ML engineer take to complete the evaluation in the LEAST amount of time?
Answer: C
NEW QUESTION # 20
An ML engineer decides to use Amazon SageMaker AI automated model tuning (AMT) for hyperparameter optimization (HPO). The ML engineer requires a tuning strategy that uses regression to slowly and sequentially select the next set of hyperparameters based on previous runs. The strategy must work across small hyperparameter ranges.
Which solution will meet these requirements?
Answer: D
Explanation:
Amazon SageMaker Automated Model Tuning supports several hyperparameter search strategies. Bayesian optimization is explicitly designed to model the relationship between hyperparameters and objective metrics using regression techniques. Based on results from previous training jobs, Bayesian optimization predicts which hyperparameter combinations are most likely to improve model performance and evaluates those next.
AWS documentation highlights Bayesian optimization as the preferred strategy when the hyperparameter search space is small to medium and when training jobs are expensive. Because the algorithm learns from prior runs, it avoids wasting resources on unpromising configurations and converges efficiently.
Grid search exhaustively evaluates all combinations and becomes inefficient even with moderately sized search spaces. Random search does not use information from prior runs and is less efficient. Hyperband focuses on aggressive early stopping and resource allocation, not regression-based sequential selection.
Therefore, Option C is the correct and AWS-verified solution.
NEW QUESTION # 21
An ML company wants to monitor and analyze the API calls that its AWS resources make. The company has created an AWS CloudTrail log file that logs to an Amazon S3 bucket. The company has also created an organization in AWS Organizations to manage permissions across accounts.
The company needs to enable log file validation to ensure the integrity of its log files.
Which solution will meet these requirements?
Answer: B
Explanation:
The correct answer is A. Enable CloudTrail log file integrity validation.
AWS CloudTrail provides the ability to record API calls made to AWS services and delivers log files to an Amazon S3 bucket. For organizations that need to ensure the authenticity and integrity of these log files, AWS recommends enabling log file integrity validation. This feature applies a hash function to each log file and stores the hash separately, allowing you to verify that the logs have not been altered, deleted, or tampered with after delivery.
Enabling log file integrity validation is critical in ML operations when auditing model training pipelines, production inference calls, or system access patterns across accounts. It ensures that security-sensitive API activity is accurately recorded and verifiable. In multi-account environments managed by AWS Organizations, this validation provides an extra layer of trust when logs are consolidated from multiple accounts.
Option B, creating a multi-Region trail, ensures that API activity across regions is logged but does not inherently guarantee the integrity of logs. Option C, creating an organization trail, centralizes logging for all accounts, which is valuable for governance, but again does not automatically provide verification of log integrity. Option D, enabling CloudWatch Logs delivery, allows real-time monitoring and alerting but does not address the verification of historical log files.
By enabling log file integrity validation, organizations can cryptographically verify each log file, detect unauthorized changes, and meet compliance requirements for secure ML monitoring and auditing. This aligns with AWS best practices for ML solution monitoring, maintenance, and security, ensuring reliable tracking of model operations and API usage across distributed ML systems.
NEW QUESTION # 22
An ML engineer is collecting data to train a classification ML model by using Amazon SageMaker AI. The target column can have two possible values: Class A or Class B. The ML engineer wants to ensure that the number of samples for both Class A and Class B are balanced, without losing any existing training data. The ML engineer must test the balance of the training data.
Which solution will meet this requirement?
Answer: D
Explanation:
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.
NEW QUESTION # 23
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