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NEW QUESTION # 201
A company needs to extract entities from a PDF document to build a classifier model.
Which solution will extract and store the entities in the LEAST amount of time?
Answer: B
NEW QUESTION # 202
A company wants to build an anomaly detection ML model. The model will use large-scale tabular data that is stored in an Amazon S3 bucket. The company does not have expertise in Python, Spark, or other languages for ML.
An ML engineer needs to transform and prepare the data for ML model training.
Which solution will meet these requirements?
Answer: A
Explanation:
The company lacks programming expertise, so a no-code/low-code solution is required. Amazon SageMaker Canvas includes Data Wrangler's visual interface, which enables users to import, transform, and prepare data using a graphical workflow.
Data Wrangler in Canvas supports common preprocessing tasks-such as handling missing values, normalization, outlier detection, and feature engineering-without writing code. It integrates directly with Amazon S3 and is suitable for large tabular datasets.
Options A, C, and D require coding skills (Spark, SQL, or Python), which violates the stated constraint.
Therefore, using the visual interface of SageMaker Data Wrangler in Canvas is the correct solution.
NEW QUESTION # 203
A healthcare company wants to detect irregularities in patient vital signs that could indicate early signs of a medical condition. The company has an unlabeled dataset that includes patient health records, medication history, and lifestyle changes.
Which algorithm and hyperparameter should the company use to meet this requirement?
Answer: C
Explanation:
Option D is correct because Amazon SageMaker AI Random Cut Forest (RCF) is documented by AWS as an unsupervised algorithm for detecting anomalous data points within a data set . The question describes a healthcare use case where the company wants to detect irregularities in patient vital signs and only has an unlabeled dataset . That is a direct match for anomaly detection with an unsupervised method rather than a supervised classification or regression algorithm. AWS documentation also notes that anomalies can appear as unexpected spikes, breaks in patterns, or otherwise unusual observations.
RCF is a strong fit because it does not require labeled target outcomes to begin identifying unusual observations. AWS explains that RCF assigns an anomaly score to each data point, where higher scores indicate potentially anomalous records. This is especially suitable for early warning scenarios in healthcare, where the goal is to surface unusual combinations of vitals, medication history, and lifestyle changes for further review.
The hyperparameter in the answer, num_trees , is a valid RCF hyperparameter and controls the number of trees used in the forest. In AWS examples and documentation, this is one of the key parameters used when configuring the algorithm. By contrast, XGBoost is generally used for supervised tasks, k-means is for clustering rather than anomaly scoring, and DeepAR is for forecasting time series rather than general anomaly detection across an unlabeled multidimensional dataset.
Therefore, the fully verified AWS-docs answer is D , because Random Cut Forest is the AWS-native unsupervised anomaly detection algorithm best aligned to this healthcare irregularity-detection requirement.
NEW QUESTION # 204
A company stores training data as a .csv file in an Amazon S3 bucket. The company must encrypt the data and must control which applications have access to the encryption key.
Which solution will meet these requirements?
Answer: B
Explanation:
AWS Key Management Service (AWS KMS) is the recommended service for encryption and key access control. By creating a customer-managed KMS key, the company can define granular IAM policies that control which applications and roles can use the key.
The AWS Encryption CLI integrates directly with KMS and enables client-side encryption of files before storing them in Amazon S3. This approach ensures data is encrypted at rest and that only authorized principals can decrypt it.
SSH keys and API keys are not designed for data encryption. IAM roles alone do not create or manage encryption keys-they only grant permissions.
AWS documentation explicitly states that KMS customer-managed keys provide centralized key management, auditing, and access control.
Therefore, Option D is the correct and AWS-aligned solution.
NEW QUESTION # 205
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: D
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 # 206
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