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.

For real-time video content moderation with minimal operational overhead, AWS documentation recommends using fully managed, purpose-built AI services. Amazon Rekognition provides real-time video analysis capabilities, including content moderation, unsafe content detection, and label recognition for live video streams.

By integrating Rekognition with AWS Lambda, the company can automatically process video frames, extract moderation metadata, and take immediate action (such as flagging or stopping a stream) without managing servers, models, or infrastructure. This serverless architecture scales automatically and minimizes operational complexity.

Option B introduces unnecessary complexity. While Amazon Bedrock LLMs are powerful, they are not required for image-based moderation tasks that Rekognition already handles natively.

Option C is incorrect because using Amazon SageMaker would require model training, endpoint management, and scaling, significantly increasing operational overhead.

Option D is incorrect because Amazon Transcribe and Amazon Comprehend are designed for audio and text analysis, not image or video frame moderation.

Therefore, Amazon Rekognition with AWS Lambda is the most efficient, scalable, and low-maintenance solution for real-time video moderation during live streaming events.

Amazon Bedrock is a fully managed AWS service and does not reside inside customer VPCs. To allow private connectivity from EC2 instances in a private subnet without using the public internet, AWS documentation recommends AWS PrivateLink.

By creating an interface VPC endpoint for Amazon Bedrock, traffic between the EC2 instances and Bedrock remains entirely within the AWS network. This approach preserves the private subnet configuration and meets strict security requirements.

Security groups alone cannot establish connectivity to a managed service endpoint. Amazon Bedrock cannot be deployed into a customer subnet, making Option C invalid. AWS Direct Connect is designed for on-premises connectivity, not for accessing AWS managed services from within a VPC.

AWS explicitly documents that PrivateLink is the correct mechanism for private access to Amazon Bedrock.

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

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.

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.

Managing permissions for multiple Amazon SageMaker notebook instances can become complex when handled individually. To centralize and streamline permission management, AWS recommends creating a single IAM role with the necessary permissions and attaching this role to each notebook instance used by the data science team.

Steps to Implement the Solution:

Create a Single IAM Role with Necessary Permissions:

Define an IAM role that encompasses all permissions required by the data scientists for their tasks. This includes permissions for SageMaker operations and any other AWS services they interact with.

AWS provides managed policies like AmazonSageMakerFullAccess that can be attached to the role to grant comprehensive SageMaker permissions.(IAM Policies for SageMaker)

Attach the IAM Role to Each Notebook Instance:

When creating or updating a SageMaker notebook instance, specify the IAM role created in the previous step. This ensures that all notebook instances operate under a consistent set of permissions.

In the SageMaker console, during the notebook instance setup, you can choose an existing IAM role to associate with the instance.(Creating SageMaker Workspaces)

Benefits of This Approach:

Centralized Permission Management:By using a single IAM role, you simplify the process of updating permissions. Changes to the role ' s policies automatically propagate to all associated notebook instances, ensuring consistent access control.

Adherence to Best Practices:AWS recommends using IAM roles to manage permissions for applications running on services like SageMaker. This approach avoids the need to manage individual user permissions separately.(IAM Best Practices for SageMaker)

Alternative Options and Their Drawbacks:

Option B: Creating a single IAM group and adding data scientists to it does not directly associate the group with notebook instances. IAM groups are used to manage user permissions, not to assign roles to AWS resources like notebook instances.

Option C: Using a single IAM user with the AdministratorAccess policy is not recommended due to security risks associated with granting broad permissions and the challenges in managing shared user credentials.

Option D: Associating an IAM group with a role and then with notebook instances is not a valid approach, as IAM groups cannot be directly associated with AWS resources.

Conclusion: Option A is the most effective solution to centralize and manage permissions for SageMaker notebook instances, aligning with AWS best practices for IAM role management.

In regression problems such as house price prediction, extreme values can significantly distort model learning. In this dataset, the presence of a large mansion and an extremely small apartment represents clear outliers in the Square Meters feature. According to AWS Machine Learning best practices, outliers can disproportionately influence loss functions (such as mean squared error), leading to poor predictions for the majority of typical data points.

Removing these outliers helps the model focus on learning patterns that apply to the majority of houses and apartments, which aligns with the requirement to produce accurate predictions for typical properties. After removing outliers, applying a log transformation to the Square Meters feature further improves model performance by reducing skewness and stabilizing variance. Log transformations are commonly recommended in AWS and general ML documentation when numerical features span multiple orders of magnitude.

Option B is incorrect because normalization alone does not address the undue influence of extreme outliers. Option C and D are incorrect because one-hot encoding is intended for categorical variables, not continuous numerical features such as square meters.

Therefore, removing outliers and applying a log transformation is the most statistically sound preprocessing approach.

Explaining how a model makes predictions is the domain of model interpretability and explainability. Amazon SageMaker Clarify is designed specifically to provide explanations for ML predictions using techniques such as SHAP (SHapley Additive exPlanations).

SageMaker Clarify can analyze deployed endpoints to show feature importance, explain individual predictions, and quantify how each input feature contributes to the model’s output. This makes it ideal for communicating model behavior to non-technical stakeholders and meeting transparency requirements.

Model Monitor focuses on data and performance drift, not explanations. A/B testing and shadow endpoints compare performance but do not explain predictions.

Therefore, SageMaker Clarify is the correct solution for explaining model predictions.

This scenario clearly describes an overfitting problem. The neural network performs well on training data but fails to generalize to evaluation data. According to AWS Machine Learning and deep learning best practices, overfitting occurs when a model learns noise or overly specific patterns in the training data instead of generalizable relationships.

L1 and L2 regularization are well-established techniques to combat overfitting. They work by penalizing large weight values during optimization, effectively constraining the model’s complexity. L1 regularization encourages sparsity, while L2 regularization (weight decay) smooths the weight distribution. AWS documentation recommends regularization as a primary method for improving generalization when a model shows a large gap between training and evaluation performance.

Option B is incorrect because removing dropout layers would increase overfitting, not reduce it. Dropout is itself a regularization technique.

Option C would likely worsen overfitting, as training for more epochs allows the model to memorize the training data even further.

Option D increases model complexity, which again exacerbates overfitting rather than resolving it.

Therefore, penalizing large weights using L1 or L2 regularization is the correct solution.

Option B is correct because the bank needs a SageMaker-supported algorithm and the resulting model must be explainable for regulators. AWS documentation states that the Amazon SageMaker AI linear learner algorithm is a built-in algorithm that provides a solution for classification and regression problems . Since determining whether customers qualify for a new product is a classification-style decision problem, linear learner fits the business requirement directly.

The explainability requirement strongly favors a simpler and more interpretable model family. AWS Prescriptive Guidance on model interpretability states that simple models are considered more interpretable because the effects of input features on the output are easier to understand. It specifically gives linear regression as an example where the magnitudes of coefficients provide a clear global feature-importance signal. While the question does not explicitly mention SageMaker Clarify, AWS documentation also says SageMaker offers explainability features to help interpret predictions, which complements inherently interpretable model types such as linear models.

The other options are weaker fits. Object2Vec is mainly for learning embeddings and similarity relationships rather than a straightforward explainable eligibility model. A neural network may work technically, but it is generally less interpretable for regulatory review. k-means is an unsupervised clustering algorithm and is not suitable for predicting whether a customer qualifies for a product. Because the bank wants a directly supported SageMaker algorithm and a model that is easier to explain to regulators, the best AWS-aligned answer is B, linear learner .