In this case, a collaborative filtering approach is most appropriate. It recommends products based on the behavior and preferences of users who are similar to the target user. Content-based and item-based filtering consider product characteristics, while reinforcement learning is used for sequential decision-making.

Transfer Learning is the technique that involves leveraging a pre-trained model on a new, but related task. It allows you to take advantage of knowledge acquired from one domain and apply it to a different but related problem, which can save time and resources in training deep learning models.

The company should ethically address this concern by "Improving data anonymization and transparency." This approach allows the company to provide personalized recommendations while safeguarding customer privacy. By being transparent about data usage and ensuring that data is properly anonymized, the company can strike a balance between personalization and privacy.

Data Engineers play a pivotal role in designing and maintaining big data infrastructure, such as Hadoop or Spark clusters. They are responsible for ensuring that the infrastructure is efficient, scalable, and suitable for data processing and analysis needs.

L2 Regularization, also known as weight decay, penalizes the squared magnitude of the coefficients in deep learning models. It adds a term to the loss function that discourages large weight values, helping to prevent overfitting. By penalizing the magnitude of weights, L2 regularization encourages the model to distribute its learning across many features, resulting in smoother weight values and reducing the risk of overfitting.

Seaborn is a popular data visualization library in Python that provides a heatmap function, commonly used to visualize correlation matrices. Heatmaps are effective for displaying the correlation between variables, making it easier to identify relationships in complex datasets.

To ensure that both 'Age' and 'Blood Pressure' contribute equally to the distance metric in a k-NN algorithm, you should standardize both features. Standardization scales the features to have a mean of 0 and a standard deviation of 1, preventing one from dominating the distance calculation. Normalization may not achieve this balance, and changing the distance metric or giving more weight to one feature can bias the results.

The ROC (Receiver Operating Characteristic) Curve plots the True Positive Rate (Sensitivity) against the False Positive Rate for different threshold values of a classification model. It is used to evaluate the model's performance in distinguishing between classes at various thresholds.

The primary ethical concern in this use case is "Data ownership and consent." When using data from various sources, including social media, it's essential to consider data ownership, consent, and privacy rights. Proper consent and data protection measures are critical to ensure ethical practices in healthcare data analysis.

To standardize data, you subtract the mean from the value and then divide by the standard deviation. In this case, the standardized value for a data point with a value of 11 is (11 - 5) / 2 = 3.5. (Option C)

Data preprocessing (Option D) is a crucial concern when deploying machine learning models to production. It involves preparing and transforming data to ensure it's compatible with the model, which is a common concern during deployment.

The YARN (Yet Another Resource Negotiator) framework in Hadoop is responsible for managing resources and enabling the distributed processing of large datasets across clusters. It allows efficient resource utilization and job scheduling, making it a critical component in Hadoop's ecosystem.