Data science cat

In the context of real-time healthcare systems, which of the following strategies most effectively addresses the simultaneous challenges of latency, scalability, and fault tolerance?

• Batch processing combined with data encryption

• Implementation of edge computing and real-time monitoring with Apache Kafka

• Storing data in centralized data lakes for post-processing

• Using machine learning models exclusively for offline anomaly detection

A financial institution uses real-time data streams for fraud detection. The system faces occasional delays due to network congestion. Which architectural improvement would most effectively reduce latency while maintaining high fault tolerance?

• Shifting to a batch-processing system

• Implementing in-memory processing and replicating data across nodes

• Using static data tables for stream-table joins

• Relying solely on manual anomaly detection methods

A social media platform uses session windows to analyze user activity. Which of the following scenarios would require an enhancement in their windowing strategy to improve real-time content recommendations?

• Detecting trends only during fixed time intervals

• Analyzing overlapping time frames for broader trends

• Responding dynamically to irregular user interaction patterns

• Aggregating data in non-overlapping time windows

During a critical system failure, a healthcare monitoring system experienced data loss. Which advanced processing strategy would best prevent such issues in future implementations?

• Using at-most-once delivery for faster processing

• Leveraging exactly-once processing with checkpointing and failover mechanisms

• Increasing hardware storage capacity

• Implementing static fault recovery protocols

A global IoT-based logistics company streams real-time sensor data for vehicle tracking. Which technique is most appropriate for detecting and responding to anomalies in the streaming data?

• Using static dashboards for daily updates

• Employing machine learning-based anomaly detection models integrated into a streaming pipeline

• Analyzing data through monthly batch reports

• Aggregating data into large windows for long-term trends

What is one of the key features of a hybrid cloud infrastructure in the retail company's case?

• Exclusive use by a single organization.

• Complete reliance on public cloud storage.

• Seamless movement of data between private and public clouds.

• Eliminates the need for sensitive data storage.

In a fault-tolerant hybrid cloud architecture, which tool is commonly used for dynamically adjusting resources based on workload demands?

• Apache Hadoop

• Kubernetes

• Tableau

• Google BigQuery

When integrating legacy systems into a hybrid cloud, what should the retail company prioritize to ensure operational continuity?

• Migrating all systems simultaneously during business hours.

• Backing up critical data and ensuring data consistency during migration.

• Retaining all legacy systems permanently for redundancy.

• Eliminating testing phases to speed up migration.

Which approach would best address the company's challenge of scaling operations during peak shopping seasons?

• Moving all operations to a private cloud.

• Implementing auto-scaling with Kubernetes in the public cloud.

• Reducing data processing during peak periods.

• Relying exclusively on static resources in the public cloud.

What is a key limitation of using hybrid cloud models for big data, as described in the case?

• Inability to support legacy systems.

• High initial investment without any scalability benefits.

• Complexity in managing security across environments.

• Complete reliance on public cloud infrastructure.