Google Professional Data Engineer on Google Cloud Platform Exam Dumps and Practice Test Questions Set 13 Q181-195
Visit here for our full Google Professional Data Engineer exam dumps and practice test questions.
Question 181
A global e-commerce company wants to provide personalized recommendations to users in real time. Clickstream events must be ingested continuously, enriched with user profile data, and fed into machine learning pipelines for prediction. Which architecture is most appropriate?
A) Cloud Pub/Sub → Dataflow → BigQuery → Vertex AI
B) Cloud SQL → Cloud Functions → BigQuery ML
C) Cloud Storage → Dataproc → BigQuery
D) Bigtable → App Engine → AutoML Tables
Answer: A
Explanation:
Personalized recommendations require ingesting millions of clickstream events per second, including page views, product clicks, searches, and purchases. Cloud Pub/Sub serves as a globally distributed, fully managed messaging system capable of handling this high-throughput ingestion. It guarantees durability, at-least-once delivery, and decouples producers from downstream consumers, ensuring events are not lost even during high traffic or network disruptions. Pub/Sub allows e-commerce platforms to continue receiving user interactions without delays caused by downstream processing bottlenecks.
Dataflow processes incoming streams in near real time. It normalizes events, enriches them with user profile data such as purchase history, preferences, and browsing patterns, and computes features required for machine learning models. Stateful processing and windowed computations enable rolling metrics, session-based aggregations, and anomaly detection. Dataflow’s managed service provides exactly-once processing semantics, automatic scaling, and high availability, reducing operational complexity while maintaining high data quality for analytics and prediction.
BigQuery serves as the analytical warehouse to store raw and enriched data. Analysts and data scientists can query billions of rows efficiently for cohort analysis, trend detection, and feature engineering. Partitioned and clustered tables allow cost-efficient and fast retrieval, essential for training machine learning models. BigQuery also enables integration with machine learning pipelines for continuous model updates, predictive analysis, and real-time experimentation.
Vertex AI is used to train, evaluate, and deploy predictive models for personalized recommendations. Continuous retraining pipelines allow models to adapt to evolving user behaviors, ensuring relevance and accuracy. Low-latency prediction endpoints allow the recommendations engine to provide real-time suggestions on web and mobile platforms. Vertex AI also supports monitoring, experiment tracking, and drift detection to maintain model reliability and accuracy over time.
Alternative architectures are less suitable. Cloud SQL with Cloud Functions and BigQuery ML cannot handle millions of streaming events per second efficiently and introduces latency that would reduce the effectiveness of real-time recommendations. Cloud Storage with Dataproc and BigQuery is more suited for batch analytics rather than streaming, providing delayed predictions. Bigtable with App Engine and AutoML Tables can store high-volume data but lacks integration with real-time enrichment, analytics, and continuous ML retraining. Therefore, Pub/Sub, Dataflow, BigQuery, and Vertex AI form the most appropriate end-to-end solution for real-time personalized recommendations in global e-commerce environments.
Question 182
A fintech company needs to evaluate global transactions in real time to prevent fraud. The system must remain available under high concurrency and provide instant scoring for each transaction. Which storage solution is most appropriate?
A) Memorystore Redis
B) Cloud SQL
C) BigQuery
D) Cloud Storage
Answer: A
Explanation:
Operational fraud detection requires evaluating transactions in sub-millisecond latency to ensure legitimate transactions proceed seamlessly while fraudulent ones are blocked. Memorystore Redis is a fully managed, in-memory key-value store ideal for this workload. It provides extremely fast read and write operations, making it suitable for storing transactional histories, risk scores, blacklists, and velocity metrics. Its in-memory architecture ensures that each transaction can be assessed instantly, which is critical for high-volume financial operations.
Redis supports extreme concurrency, allowing it to scale globally to accommodate spikes in transaction volume, such as during promotions or peak shopping periods. Its advanced data structures, including sorted sets, hashes, and bitmaps, enable aggregation, thresholds, and computation of complex fraud detection rules efficiently. Memorystore provides managed replication and high availability, guaranteeing continued operations even if nodes or regions fail. Fully managed operations remove the need for manual scaling, failover configuration, or cluster management.
Alternative solutions are less suitable. Cloud SQL provides transactional consistency but cannot handle sub-millisecond operational lookups at global scale under high concurrency. BigQuery is designed for analytical workloads and is not capable of evaluating transactions in real time. Cloud Storage is object-based, intended for batch storage, and cannot deliver instant operational access to risk scores.
By using Memorystore Redis, fintech companies achieve a low-latency, highly available, globally scalable system for operational fraud detection. Redis allows rapid access to historical data and precomputed risk scores, enabling the evaluation of millions of transactions per second with high reliability and operational simplicity. Its speed and advanced data structures make it the optimal choice for real-time fraud prevention in financial systems.
Question 183
A healthcare organization wants to predict patient readmission risk using structured electronic health records and continuous telemetry from wearable devices. Models must update continuously as new data arrives. Which architecture is most appropriate?
A) Pub/Sub → Dataflow → BigQuery → Vertex AI
B) Cloud Storage → App Engine → BigQuery ML
C) Dataproc → Cloud Storage → Cloud Functions
D) Bigtable → Cloud Run → AutoML Tables
Answer: A
Explanation:
Predictive analytics in healthcare combines structured electronic health records with high-frequency telemetry from wearable devices. Telemetry data includes metrics such as heart rate, blood pressure, oxygen saturation, activity levels, and sleep patterns. The ingestion layer must handle high-frequency events reliably and at scale. Pub/Sub provides a managed, globally distributed messaging system capable of ingesting millions of events per second. It guarantees at-least-once delivery, durability, and decouples data producers from downstream consumers, enabling continuous, uninterrupted ingestion of telemetry and EHR updates.
Dataflow serves as the processing layer, transforming incoming streams in near real time. It normalizes telemetry data, joins it with historical EHR records, aggregates metrics, and computes features for predictive modeling. Windowed and stateful processing allow calculation of rolling averages, detection of anomalies, and capture of temporal trends in patient health. Dataflow’s managed service ensures exactly-once processing, high availability, and automatic scaling, reducing operational overhead while maintaining data integrity and accuracy.
BigQuery acts as the analytical warehouse, storing raw and processed data for large-scale querying. Analysts and data scientists can perform cohort analysis, feature engineering, and historical trend evaluation. Partitioned and clustered tables allow efficient time-based queries, controlling costs while providing rapid access to billions of rows of data. BigQuery also integrates with machine learning workflows, supporting model training, evaluation, and deployment.
Vertex AI is used to train, evaluate, and deploy predictive models for readmission risk. Continuous retraining pipelines ensure models reflect the most recent data, maintaining accuracy over time. Low-latency prediction endpoints allow clinical decision support systems to access predictions instantly. Vertex AI also supports experiment tracking, model monitoring, and drift detection. Alternative architectures are less suitable. Cloud Storage with App Engine and BigQuery ML cannot process continuous streams or automate retraining effectively. Dataproc with Cloud Storage and Cloud Functions introduces operational complexity and lacks tight integration for streaming-to-ML pipelines. Bigtable with Cloud Run and AutoML Tables may store telemetry but is less suited for integrating structured EHR data with continuous predictive modeling.
Therefore, Pub/Sub, Dataflow, BigQuery, and Vertex AI provide the optimal end-to-end architecture for predictive healthcare analytics, enabling accurate, continuous readmission risk prediction while maintaining operational efficiency and scalability.
Question 184
A global ride-sharing company wants to optimize driver allocation and dynamic pricing in real time. Millions of location and trip events must be ingested per second and fed into analytics and machine learning pipelines. Which architecture is most appropriate?
A) Cloud Pub/Sub → Dataflow → BigQuery → Vertex AI
B) Cloud SQL → Cloud Functions → BigQuery ML
C) Cloud Storage → Dataproc → BigQuery
D) Bigtable → App Engine → AutoML Tables
Answer: A
Explanation:
Global ride-sharing operations require continuous ingestion of millions of events, including driver locations, trip requests, completed rides, and cancellations. This data must be processed in real time to optimize driver allocation and dynamic pricing. Cloud Pub/Sub provides a fully managed, globally distributed messaging system capable of handling high-throughput ingestion. It ensures at-least-once delivery, guarantees message durability, and decouples producers from consumers, allowing drivers’ apps to continuously send data without being blocked by downstream processing.
Dataflow processes this real-time data stream. It enriches events with driver availability, historical trip patterns, and surge pricing rules while performing normalization, filtering, and aggregation. Stateful processing and windowed computations enable calculation of rolling metrics, such as average ride demand per region or real-time surge multipliers. Dataflow’s managed service automatically scales to accommodate fluctuating traffic and ensures exactly-once processing, preventing duplication while maintaining data quality and reliability.
BigQuery serves as the analytics warehouse, storing both raw and processed event data. Analysts can perform cohort analysis, trend detection, and feature engineering to inform predictive models. Partitioned and clustered tables allow efficient, cost-effective queries over billions of rows, enabling large-scale analytics and historical evaluation. BigQuery’s integration with machine learning pipelines enables continuous model training and evaluation, essential for accurate dynamic pricing predictions.
Vertex AI is used for training, evaluating, and deploying predictive models for ride demand forecasting, surge pricing, and driver allocation. Continuous retraining pipelines ensure models remain accurate as traffic patterns change. Low-latency endpoints provide real-time predictions to the allocation system. Vertex AI supports monitoring, drift detection, and experiment tracking to maintain model accuracy and reliability.
Alternative architectures are less effective. Cloud SQL with Cloud Functions and BigQuery ML cannot handle millions of streaming events per second and introduces latency. Cloud Storage with Dataproc and BigQuery is suitable only for batch processing and delayed predictions. Bigtable with App Engine and AutoML Tables may store large datasets, but lacks native integration for real-time streaming enrichment, analytics, and continuous ML retraining. Therefore, Pub/Sub, Dataflow, BigQuery, and Vertex AI form the optimal end-to-end architecture for real-time ride-sharing optimization at a global scale.
Question 185
A fintech company wants to prevent fraudulent transactions in real time. Each transaction must be evaluated within milliseconds, and the system must remain highly available globally. Which storage solution is most appropriate?
A) Memorystore Redis
B) Cloud SQL
C) BigQuery
D) Cloud Storage
Answer: A
Explanation:
Operational fraud detection requires that each financial transaction is assessed almost instantaneously. Sub-millisecond latency is critical to approve legitimate transactions without interruption while preventing fraudulent activity. Memorystore Redis, a fully managed, in-memory key-value store, provides the speed required for operational data access. It stores transaction histories, velocity metrics, precomputed risk scores, and other operational data necessary for real-time decision-making.
Redis supports extremely high concurrency and scales horizontally to accommodate global traffic peaks, such as during promotional events or financial holidays. Advanced data structures like sorted sets, hashes, and bitmaps allow aggregation, thresholds, and execution of complex fraud rules efficiently. Memorystore provides managed replication and high availability, ensuring system continuity even if nodes or entire regions fail. Its fully managed nature eliminates the operational burden of manual scaling, failover management, and cluster administration.
Alternative solutions are less suitable. Cloud SQL provides relational ACID transactions but cannot meet sub-millisecond latency under global high-concurrency workloads. BigQuery is optimized for analytical queries and cannot support real-time operational scoring of individual transactions. Cloud Storage is object-based, intended for batch processing or archival, and is not designed for instantaneous retrieval of operational risk data.
By using Memorystore Redis, fintech companies achieve a globally available, low-latency, and highly scalable operational system for fraud prevention. Redis enables rapid access to account histories and precomputed risk scores, allowing evaluation of millions of transactions per second while maintaining performance, reliability, and operational simplicity. Its speed, reliability, and advanced data structures make it the preferred choice for operational fraud detection in financial systems.
Question 186
A healthcare organization wants to predict patient readmission risk using structured EHR data and continuous wearable telemetry. Predictive models must update frequently as new data arrives. Which architecture is most appropriate?
A) Pub/Sub → Dataflow → BigQuery → Vertex AI
B) Cloud Storage → App Engine → BigQuery ML
C) Dataproc → Cloud Storage → Cloud Functions
D) Bigtable → Cloud Run → AutoML Tables
Answer: A
Explanation:
Predictive healthcare analytics integrates structured electronic health records with high-frequency telemetry from wearable devices, which may include heart rate, blood pressure, oxygen saturation, activity, and sleep patterns. The ingestion system must handle high-frequency events reliably and at scale. Pub/Sub provides a fully managed, globally distributed messaging system capable of ingesting millions of events per second. It ensures at-least-once delivery, durability, and decouples producers from downstream consumers, enabling uninterrupted, continuous ingestion of telemetry and EHR data from thousands of patients globally.
Dataflow processes the incoming streams in near real time. It normalizes telemetry, merges it with historical EHR data, aggregates metrics, and computes derived features for predictive modeling. Windowed and stateful processing allow rolling averages, anomaly detection, and time-series analysis. Dataflow’s managed service ensures exactly-once processing, high availability, and automatic scaling, reducing operational overhead while maintaining data accuracy and integrity.
BigQuery serves as the analytical warehouse, storing raw and processed data for large-scale querying. Analysts and data scientists perform cohort analysis, feature engineering, and historical trend exploration. Partitioned and clustered tables enable cost-efficient queries over billions of rows. BigQuery also integrates seamlessly with machine learning pipelines for training, evaluation, and deployment.
Vertex AI allows creation, training, and deployment of predictive models for readmission risk. Continuous retraining pipelines ensure models are up to date as new telemetry and EHR data arrive. Low-latency prediction endpoints allow clinical decision support systems to access risk scores instantly. Vertex AI supports monitoring, drift detection, and experiment tracking. Alternative architectures are less suitable. Cloud Storage with App Engine and BigQuery ML cannot process continuous streams or automate retraining efficiently. Dataproc with Cloud Storage and Cloud Functions adds operational overhead and lacks streaming-to-ML integration. Bigtable with Cloud Run and AutoML Tables can store telemetry but is less suited for integrating structured EHR with continuous predictive modeling.
Therefore, Pub/Sub, Dataflow, BigQuery, and Vertex AI provide the optimal architecture for predictive healthcare analytics, enabling accurate, continuous readmission risk prediction while maintaining operational efficiency, scalability, and reliability.
Question 187
A global logistics company wants to monitor shipment conditions such as temperature, humidity, and location in real time. Alerts must be triggered if conditions deviate from thresholds, and analytics must be available for operational improvement. Which architecture is most appropriate?
A) Cloud Pub/Sub → Dataflow → BigQuery → Looker
B) Cloud SQL → Cloud Functions → Dataproc → Looker
C) Cloud Storage → Cloud Run → BigQuery ML
D) Bigtable → App Engine → Data Studio
Answer: A
Explanation:
Real-time monitoring of global shipments requires ingestion of continuous streams of telemetry data from trucks, containers, warehouses, and IoT sensors. These streams include temperature, humidity, GPS coordinates, and vibration data, all of which need to be captured reliably and processed immediately to detect anomalies or trigger alerts. Cloud Pub/Sub provides a fully managed, globally distributed messaging service capable of handling millions of events per second. It guarantees message durability, at-least-once delivery, and decouples data producers from consumers, ensuring uninterrupted ingestion even during network interruptions or system scaling events.
Dataflow serves as the processing engine for this streaming data. It normalizes, enriches, and transforms incoming telemetry, merging it with contextual metadata such as shipment contents, route information, or expected delivery schedules. Windowed computations allow the aggregation of metrics over defined time intervals, while stateful processing enables tracking trends and detecting persistent anomalies. Dataflow’s managed service provides exactly-once processing, high availability, and automatic scaling, allowing it to handle fluctuations in shipment volume without manual intervention.
BigQuery acts as the analytical warehouse, storing both raw and processed telemetry. Analysts can query billions of rows efficiently, perform historical trend analysis, evaluate operational performance, and generate features for predictive maintenance or risk modeling. Partitioned and clustered tables optimize query performance and cost, allowing fast access to large datasets without excessive overhead. BigQuery’s integration with visualization and analytics tools enables continuous insight generation.
Looker provides dashboards, alerts, and operational insights. Users can monitor shipment conditions in real time, configure automated alerts for threshold breaches, and analyze trends for performance improvement. Alternative architectures are less effective. Cloud SQL with Cloud Functions and Dataproc introduces latency and is difficult to scale for high-throughput telemetry. Cloud Storage with Cloud Run and BigQuery ML is suitable for batch analytics but cannot support real-time streaming or alerting. Bigtable with App Engine and Data Studio can store large-scale time-series data but lacks integrated streaming analytics and alerting functionality. Therefore, Pub/Sub, Dataflow, BigQuery, and Looker provide the most appropriate end-to-end architecture for real-time shipment monitoring and operational analytics.
Question 188
A fintech company must evaluate transactions globally in real time to prevent fraud. Each transaction must be scored within milliseconds, and the system must maintain high availability under traffic spikes. Which storage solution is most appropriate?
A) Memorystore Redis
B) Cloud SQL
C) BigQuery
D) Cloud Storage
Answer: A
Explanation:
Real-time fraud detection requires evaluating transactions almost instantaneously to prevent fraudulent activity while allowing legitimate transactions to proceed without delay. Memorystore Redis, a fully managed in-memory key-value store, provides sub-millisecond read and write operations, making it ideal for storing transactional histories, precomputed risk scores, user velocity metrics, and blacklists. Its in-memory architecture ensures extremely fast access to operational data needed for decision-making.
Redis supports high concurrency and can scale horizontally to handle global traffic peaks, such as promotional events, holiday seasons, or financial market surges. Its advanced data structures, including sorted sets, hashes, and bitmaps, allow efficient aggregation of multiple risk factors, evaluation of thresholds, and execution of complex fraud detection rules. Memorystore provides managed replication and high availability, ensuring continuous operation even in the event of node or regional failures. Fully managed operations reduce operational overhead, eliminating the need to manually configure scaling, failover, or clustering.
Alternative storage solutions are less effective. Cloud SQL provides relational storage and ACID compliance, but cannot reliably deliver sub-millisecond latency at a global scale under high concurrency. BigQuery is optimized for analytical workloads rather than operational real-time lookups. Cloud Storage is object-based and designed for batch storage or archival; it cannot provide instantaneous retrieval of transactional or risk data.
Using Memorystore Redis, fintech companies achieve a globally available, low-latency, and highly scalable operational database for real-time fraud prevention. Redis ensures rapid access to historical data and precomputed risk scores, enabling evaluation of millions of transactions per second while maintaining reliability, speed, and operational simplicity. Its performance, scalability, and advanced data structures make it the ideal solution for operational fraud detection in global financial systems.
Question 189
A healthcare organization wants to predict patient readmission risk using structured EHR data and continuous telemetry from wearable devices. Models must update continuously as new data arrives. Which architecture is most appropriate?
A) Pub/Sub → Dataflow → BigQuery → Vertex AI
B) Cloud Storage → App Engine → BigQuery ML
C) Dataproc → Cloud Storage → Cloud Functions
D) Bigtable → Cloud Run → AutoML Tables
Answer: A
Explanation:
Predictive healthcare analytics integrates structured EHR data with continuous telemetry streams from wearable devices. These streams include heart rate, blood pressure, activity levels, sleep patterns, and other physiological metrics. The ingestion layer must handle high-frequency, large-volume data reliably and at scale. Pub/Sub provides a fully managed, globally distributed messaging system capable of ingesting millions of events per second. It guarantees at least once delivery, ensures durability, and decouples data producers from downstream consumers, allowing uninterrupted continuous ingestion from thousands of patients globally.
Dataflow processes these streams in near real time. It normalizes telemetry, merges it with historical EHR records, aggregates metrics, and computes derived features needed for predictive modeling. Stateful processing and windowed computations allow calculation of rolling averages, detection of anomalies, and identification of temporal trends in patient health. Dataflow’s managed service provides exactly-once processing semantics, high availability, and automatic scaling, minimizing operational complexity while ensuring accuracy and reliability.
BigQuery acts as the analytical warehouse, storing both raw and processed data. Analysts and data scientists can perform cohort analysis, feature engineering, historical trend evaluation, and large-scale predictive analytics. Partitioned and clustered tables optimize query performance and cost for billions of rows of patient data. BigQuery integrates seamlessly with machine learning workflows, providing a platform for model training, evaluation, and deployment.
Vertex AI enables the creation, training, and deployment of predictive models for readmission risk. Continuous retraining pipelines ensure models remain accurate as new telemetry and EHR data arrive. Low-latency prediction endpoints allow clinical decision support systems to access risk scores instantly. Vertex AI supports monitoring, drift detection, and experiment tracking. Alternative architectures are less suitable. Cloud Storage with App Engine and BigQuery ML cannot efficiently handle continuous streaming or automated retraining. Dataproc with Cloud Storage and Cloud Functions introduces operational complexity and lacks integrated streaming-to-ML pipelines. Bigtable with Cloud Run and AutoML Tables may store telemetry data, but is less effective for integrating structured EHR data with continuous predictive modeling.
Therefore, Pub/Sub, Dataflow, BigQuery, and Vertex AI provide the optimal end-to-end architecture for predictive healthcare analytics, enabling accurate, continuous readmission risk prediction while maintaining scalability, reliability, and operational efficiency.
Question 190
A global retail company wants to provide personalized product recommendations to users in real time. Clickstream data must be ingested continuously, enriched with customer profile data, and used for real-time predictions. Which architecture is most appropriate?
A) Cloud Pub/Sub → Dataflow → BigQuery → Vertex AI
B) Cloud SQL → Cloud Functions → BigQuery ML
C) Cloud Storage → Dataproc → BigQuery
D) Bigtable → App Engine → AutoML Tables
Answer: A
Explanation:
Providing personalized recommendations requires processing high-volume, real-time clickstream data. Events such as product views, searches, clicks, and purchases must be captured and delivered to downstream analytics and machine learning pipelines without delay. Cloud Pub/Sub is a fully managed, globally distributed messaging system that ingests millions of events per second. It guarantees at least once delivery, durability, and decouples producers from consumers, allowing continuous data ingestion without dependency on downstream processing capacity.
Dataflow processes the event streams in real time. It normalizes and enriches clickstream events with customer profiles, such as purchase history, browsing patterns, and demographic data. Dataflow supports windowed and stateful computations, enabling rolling metrics, session-based aggregations, and anomaly detection. Its managed service ensures exactly-once processing, high availability, and automatic scaling, which is essential for processing millions of events per second and providing timely data for real-time prediction.
BigQuery serves as the analytical warehouse, storing both raw and enriched data. Analysts and data scientists can perform cohort analysis, feature engineering, and historical trend detection. Partitioned and clustered tables optimize cost and query performance, allowing fast access to large-scale datasets. BigQuery also integrates with machine learning pipelines, enabling continuous model training and evaluation, which is essential for keeping recommendations relevant and accurate.
Vertex AI is used to train, evaluate, and deploy predictive models for personalized recommendations. Continuous retraining pipelines ensure models reflect evolving user behavior, and low-latency prediction endpoints allow real-time suggestions on web and mobile applications. Vertex AI supports monitoring, drift detection, and experiment tracking to maintain model accuracy and reliability.
Alternative architectures are less suitable. Cloud SQL with Cloud Functions and BigQuery ML cannot handle millions of streaming events per second efficiently and introduces latency. Cloud Storage with Dataproc and BigQuery is better suited for batch analytics and delayed predictions. Bigtable with App Engine and AutoML Tables can store high-volume datasets but lacks integration for real-time enrichment, analytics, and continuous ML retraining. Therefore, Pub/Sub, Dataflow, BigQuery, and Vertex AI provide the most effective architecture for real-time personalized recommendations in a global retail environment.
Question 191
A fintech company must evaluate transactions globally in real time to prevent fraud. The system must provide instant scoring for each transaction and maintain high availability under traffic spikes. Which storage solution is most appropriate?
A) Memorystore Redis
B) Cloud SQL
C) BigQuery
D) Cloud Storage
Answer: A
Explanation:
Operational fraud detection demands sub-millisecond latency to allow legitimate transactions to proceed while blocking fraudulent ones. Memorystore Redis is a fully managed in-memory key-value store that can handle high-speed operations, making it suitable for storing transaction histories, precomputed risk scores, velocity metrics, and blacklists. Its in-memory architecture ensures that each transaction is evaluated almost instantaneously, which is critical for high-volume global fintech operations.
Redis supports extreme concurrency, allowing the system to scale globally to accommodate traffic peaks, such as promotional campaigns, holiday shopping, or market surges. Its advanced data structures, including sorted sets, hashes, and bitmaps, enable efficient aggregation, threshold evaluation, and execution of complex fraud detection rules. Memorystore provides managed replication and high availability, ensuring continuous operation even in the event of node or regional failure. Fully managed operations eliminate the operational burden of manually configuring scaling, failover, and cluster management.
Alternative storage solutions are less effective. Cloud SQL provides transactional consistency but cannot deliver sub-millisecond latency at a global scale. BigQuery is optimized for analytical queries and cannot provide real-time operational scoring of individual transactions. Cloud Storage is object-based and suitable for batch storage, but it cannot support operational fraud evaluation.
By using Memorystore Redis, fintech companies achieve a low-latency, highly available, globally scalable system for real-time fraud prevention. Redis ensures rapid access to historical data and precomputed risk scores, enabling the evaluation of millions of transactions per second while maintaining reliability, speed, and operational simplicity. Its advanced capabilities make it the optimal solution for operational fraud detection in modern financial systems.
Question 192
A healthcare organization wants to predict patient readmission risk using structured EHR data and continuous telemetry from wearable devices. Predictive models must update continuously as new data arrives. Which architecture is most appropriate?
A) Pub/Sub → Dataflow → BigQuery → Vertex AI
B) Cloud Storage → App Engine → BigQuery ML
C) Dataproc → Cloud Storage → Cloud Functions
D) Bigtable → Cloud Run → AutoML Tables
Answer: A
Explanation:
Healthcare predictive analytics combines structured electronic health records with high-frequency telemetry from wearable devices, including metrics such as heart rate, blood pressure, oxygen saturation, activity, and sleep patterns. The ingestion system must handle high-frequency, large-volume streams reliably. Pub/Sub is a managed, globally distributed messaging system capable of ingesting millions of events per second. It guarantees at least once delivery, durability, and decouples producers from downstream consumers, ensuring continuous, uninterrupted data ingestion from thousands of patients worldwide.
Dataflow processes these streams in near real time. It normalizes telemetry, merges it with historical EHR data, computes features for predictive modeling, and performs windowed and stateful computations. Rolling averages, anomaly detection, and temporal trend calculations are supported by Dataflow, ensuring that feature engineering and aggregation are accurate and timely. Its managed service guarantees exactly-once processing, high availability, and automatic scaling, reducing operational overhead while maintaining data integrity.
BigQuery serves as the analytical warehouse, storing both raw and processed data. Analysts and data scientists can perform cohort analysis, feature engineering, and historical trend analysis. Partitioned and clustered tables allow cost-efficient queries over billions of rows. BigQuery integrates with machine learning pipelines to train, evaluate, and deploy predictive models efficiently.
Vertex AI is used to create, train, and deploy predictive models for readmission risk. Continuous retraining pipelines ensure models remain accurate as new telemetry and EHR data arrive. Low-latency endpoints provide predictions to clinical decision support systems instantly. Vertex AI supports monitoring, drift detection, and experiment tracking. Alternative architectures are less effective. Cloud Storage with App Engine and BigQuery ML cannot process continuous streams efficiently or provide automated retraining. Dataproc with Cloud Storage and Cloud Functions introduces operational complexity and lacks streaming-to-ML integration. Bigtable with Cloud Run and AutoML Tables can store telemetry, but is less suited for integrating structured EHR with continuous predictive modeling.
Therefore, Pub/Sub, Dataflow, BigQuery, and Vertex AI provide the optimal end-to-end solution for predictive healthcare analytics, enabling accurate, continuous readmission risk prediction while maintaining scalability, reliability, and operational efficiency.
Question 193
A global e-commerce company wants to optimize supply chain operations by monitoring inventory levels and shipment status in real time. Alerts must be generated for low stock, delays, and potential disruptions. Which architecture is most appropriate?
A) Cloud Pub/Sub → Dataflow → BigQuery → Looker
B) Cloud SQL → Cloud Functions → Dataproc → Looker
C) Cloud Storage → Cloud Run → BigQuery ML
D) Bigtable → App Engine → Data Studio
Answer: A
Explanation:
Real-time supply chain monitoring involves ingesting telemetry and transactional events from warehouses, distribution centers, and shipping partners. These events include inventory updates, shipment tracking, environmental conditions, and order fulfillment statuses. Cloud Pub/Sub provides a fully managed, globally distributed messaging system capable of handling millions of messages per second. It ensures at-least-once delivery, decouples producers from consumers, and guarantees durability, allowing continuous ingestion even during network disruptions or scaling events.
Dataflow processes these real-time streams. It normalizes and enriches data, merges inventory and shipment events with contextual metadata such as product category and warehouse location, and computes operational metrics. Stateful and windowed computations allow for rolling averages, anomaly detection, and trend analysis, enabling timely detection of low-stock situations or potential shipment delays. Dataflow’s managed service ensures exactly-once processing, high availability, and automatic scaling, minimizing operational overhead while maintaining accurate and reliable data processing.
BigQuery serves as the analytical warehouse, storing both raw and processed streams. Analysts and operations teams can perform cohort analysis, historical trend detection, predictive modeling for demand forecasting, and feature engineering for machine learning models. Partitioned and clustered tables allow efficient time-based queries over large datasets, optimizing query performance and cost. BigQuery’s integration with visualization tools enables near real-time dashboards for monitoring inventory, shipments, and operational performance.
Looker provides dashboards, automated alerts, and analytical insights. Operational teams can track inventory levels, identify delays, and receive threshold-based alerts to proactively manage supply chain risks. Alternative architectures are less suitable. Cloud SQL with Cloud Functions and Dataproc introduces latency and is difficult to scale for high-throughput streaming events. Cloud Storage with Cloud Run and BigQuery ML is suitable for batch processing, but cannot provide real-time monitoring or alerting. Bigtable with App Engine and Data Studio can store time-series data, but lacks integrated streaming analytics and automated alerting. Therefore, Pub/Sub, Dataflow, BigQuery, and Looker form the most appropriate architecture for real-time supply chain optimization in a global e-commerce environment.
Question 194
A fintech company wants to evaluate transactions in real time to prevent fraud. The system must provide instant scoring and remain available globally under heavy traffic. Which storage solution is most appropriate?
A) Memorystore Redis
B) Cloud SQL
C) BigQuery
D) Cloud Storage
Answer: A
Explanation:
Operational fraud detection requires evaluating transactions with sub-millisecond latency. This ensures legitimate transactions are processed without delay while preventing fraudulent activity. Memorystore Redis is a fully managed in-memory key-value store optimized for rapid operational data access. It stores transaction histories, precomputed risk scores, velocity metrics, and blacklists, enabling instant evaluation of each transaction.
Redis supports extreme concurrency, allowing the system to scale horizontally and handle global traffic spikes such as holiday promotions, market surges, or flash sales. Advanced data structures, including sorted sets, hashes, and bitmaps, enable efficient aggregation, threshold evaluation, and computation of complex fraud detection rules. Memorystore provides managed replication and high availability, ensuring continuous operation even in the event of node or regional failures. Fully managed operations eliminate the operational burden of scaling, failover management, and cluster maintenance.
Alternative solutions are less effective. Cloud SQL provides transactional consistency but cannot reliably deliver sub-millisecond latency at a global scale under high concurrency. BigQuery is designed for analytical workloads and cannot support operational real-time transaction scoring. Cloud Storage is object-based, intended for batch storage, and unsuitable for instant transactional access.
Using Memorystore Redis, fintech companies achieve a globally available, low-latency, and highly scalable system for real-time fraud detection. Redis ensures rapid access to historical transaction data and precomputed risk scores, enabling evaluation of millions of transactions per second while maintaining reliability, performance, and operational simplicity. Its speed and advanced capabilities make it the optimal solution for operational fraud prevention in high-volume financial environments.
Question 195
A healthcare organization wants to predict patient readmission risk using structured EHR data and continuous telemetry from wearable devices. Predictive models must update frequently as new data arrives. Which architecture is most appropriate?
A) Pub/Sub → Dataflow → BigQuery → Vertex AI
B) Cloud Storage → App Engine → BigQuery ML
C) Dataproc → Cloud Storage → Cloud Functions
D) Bigtable → Cloud Run → AutoML Tables
Answer: A
Explanation:
Healthcare predictive analytics integrates structured electronic health record data with continuous telemetry from wearable devices, including heart rate, blood pressure, oxygen saturation, activity levels, and sleep patterns. The ingestion system must handle high-frequency streams reliably at scale. Pub/Sub provides a fully managed, globally distributed messaging system capable of ingesting millions of events per second. It ensures at least once delivery, durability, and decouples data producers from downstream consumers, allowing uninterrupted, continuous ingestion from thousands of patients globally.
Dataflow processes these streams in near real time. It normalizes telemetry, merges it with historical EHR records, aggregates metrics, and computes derived features for predictive modeling. Windowed and stateful processing enable rolling averages, anomaly detection, and temporal trend analysis, ensuring accurate and timely feature extraction. Dataflow’s managed service ensures exactly-once processing, high availability, and automatic scaling, reducing operational overhead while maintaining data accuracy and integrity.
BigQuery is a fully managed, serverless data warehouse designed to handle large-scale analytical workloads efficiently, making it an ideal choice for organizations that need to store and analyze massive datasets. In the context of healthcare analytics, BigQuery can serve as a centralized repository for both raw and processed patient data. Raw data includes unprocessed records from electronic health systems, monitoring devices, and lab results, preserving the full detail of clinical events. Processed data, on the other hand, consists of curated datasets where transformations, cleaning, and feature engineering have been applied to prepare it for analysis or predictive modeling. By combining both types of data in a single platform, analysts and data scientists can perform exploratory analyses, cohort studies, and historical trend evaluations, enabling insights into patient populations, treatment outcomes, and readmission risks.
One of BigQuery’s key advantages is its ability to efficiently handle extremely large datasets. Partitioned tables allow data to be segmented based on time, patient ID, or other relevant dimensions, so queries scan only the relevant partitions, improving performance and reducing costs. Clustered tables further optimize query execution by physically organizing data according to frequently queried columns, such as diagnosis codes or patient demographics. These features are particularly useful when analyzing billions of rows of patient records, as they allow healthcare organizations to execute complex queries with low latency and minimal resource consumption.
BigQuery also integrates seamlessly with machine learning workflows, providing a robust environment for predictive analytics. With BigQuery ML, data scientists can train, evaluate, and deploy models directly on the data stored in BigQuery without the need to move it to an external system. This enables end-to-end workflows where features extracted from patient records can be immediately used for training models that predict outcomes such as readmission risk, disease progression, or treatment effectiveness. Once trained, these models can be evaluated, iterated upon, and deployed as low-latency endpoints to support clinical decision-making in near real time.
BigQuery provides a unified platform for both storage and analysis of healthcare data. Its support for raw and processed datasets, coupled with partitioning and clustering for cost-efficient performance, ensures that large-scale queries on patient populations can be executed efficiently. The integration with machine learning pipelines further enhances its value, enabling predictive modeling and real-time clinical insights. By combining analytical power, scalability, and seamless ML integration, BigQuery empowers healthcare organizations to derive actionable insights, improve patient outcomes, and support data-driven decision-making across the continuum of care.
Vertex AI enables training, evaluation, and deployment of predictive models for readmission risk. Continuous retraining pipelines ensure models remain accurate as new telemetry and EHR data arrive. Low-latency prediction endpoints allow clinical decision support systems to access risk scores instantly. Vertex AI also supports monitoring, drift detection, and experiment tracking. Alternative architectures are less suitable. Cloud Storage with App Engine and BigQuery ML cannot process continuous streams efficiently or provide automated retraining. Dataproc with Cloud Storage and Cloud Functions introduces operational complexity and lacks streaming-to-ML integration. Bigtable with Cloud Run and AutoML Tables may store telemetry, but is less effective for integrating structured EHR with continuous predictive modeling.
Therefore, Pub/Sub, Dataflow, BigQuery, and Vertex AI provide the optimal architecture for predictive healthcare analytics, enabling accurate, continuous readmission risk prediction while maintaining scalability, reliability, and operational efficiency.