Google  Professional Data Engineer on Google Cloud Platform Exam  Dumps and Practice Test Questions Set 8 Q141- 160 

Visit here for our full Google Professional Data Engineer exam dumps and practice test questions.

Q141

A company wants to build a real-time analytics pipeline for social media engagement data. The system must ingest millions of events per second, guarantee exactly-once processing, perform session-based and windowed aggregations, and support low-latency dashboards for marketing and content teams. Which architecture is most suitable?

A) Cloud Pub/Sub → Dataflow → BigQuery

B) Cloud Storage → Dataproc → BigQuery

C) Cloud SQL → Cloud Functions → BigQuery

D) Bigtable → Cloud Run → BigQuery

Answer

A) Cloud Pub/Sub → Dataflow → BigQuery

Explanation

A) Cloud Pub/Sub, Dataflow, and BigQuery together provide a fully managed, serverless architecture suitable for ingesting and processing millions of social media events per second, such as likes, shares, comments, and impressions. Cloud Pub/Sub automatically scales to handle large spikes in engagement during campaigns, trending content, or viral posts, while ensuring durable message delivery with acknowledgments.

Dataflow provides exactly-once processing, which guarantees that events are counted accurately, avoiding duplicates that could misrepresent engagement metrics. Its stateful and windowed processing capabilities allow session-based aggregations, such as calculating average engagement per user session or per content type. Sliding or tumbling window aggregations support near real-time analytics for dashboards, enabling marketing teams to react to trends quickly and optimize content strategies.

BigQuery serves as the analytics layer, storing both raw and aggregated engagement data. Its serverless, columnar architecture scales seamlessly to petabyte-level datasets and supports low-latency queries for dashboards, reporting, and historical analysis. Integration with BI tools like Looker or Data Studio enables real-time visualization of engagement metrics, campaign effectiveness, and user behavior insights.

B) Cloud Storage → Dataproc → BigQuery is batch-oriented. Storing events and processing them in batches introduces latency unsuitable for real-time analytics. Dataproc clusters require manual management, and exactly-once processing is not guaranteed, which could affect the accuracy of session-based aggregations.

C) Cloud SQL → Cloud Functions → BigQuery cannot scale to millions of events per second. Cloud SQL is optimized for transactional workloads, and Cloud Functions have memory and execution limits. Implementing exactly-once processing and windowed aggregations at this scale would require complex orchestration.

D) Bigtable → Cloud Run → BigQuery can store raw data efficiently but lacks stream processing and exactly-once semantics. Windowed and session-based aggregations would require additional orchestration, increasing complexity and operational risk.

Q142

A company wants to store IoT telemetry data in BigQuery for analytics. Queries frequently filter by timestamp, device type, and region. The dataset is expected to grow to petabytes. Which table design is most suitable for performance and cost efficiency?

A) Partition by ingestion timestamp and cluster by device type and region

B) Partition by device type and cluster by timestamp

C) Use a single unpartitioned table

D) Create multiple tables per device type and region

Answer

A) Partition by ingestion timestamp and cluster by device type and region

Explanation

A) Partitioning by ingestion timestamp ensures queries filtered by time only scan relevant partitions, reducing the amount of data processed and lowering costs. Clustering by device type and region physically organizes rows within each partition, improving query performance for filtering and aggregation. This approach scales efficiently for multi-petabyte datasets and maintains consistent performance as the dataset grows.

A single partitioned and clustered table reduces operational overhead, removing the need to manage multiple tables or datasets. Partitioned and clustered tables support both streaming and batch ingestion, enabling real-time anomaly detection, trend analysis, and predictive maintenance. BigQuery manages partition metadata, optimizes query execution, and scales compute and storage automatically. This design is ideal for IoT telemetry, where queries often involve multiple dimensions such as device type, region, and timestamp.

B) Partitioning by device type creates many small partitions, increasing metadata overhead and reducing query efficiency. Clustering by timestamp alone does not optimize common queries filtered by region.

C) A single unpartitioned table is inefficient at petabyte scale. Queries filtered by timestamp, device type, or region would scan the entire table, increasing costs and reducing performance.

D) Multiple tables per device type and region increase operational complexity. Schema evolution, cross-device queries, and table maintenance become cumbersome. Queries spanning multiple tables require unions or joins, reducing efficiency and increasing risk of errors.

Q143

A company wants to implement a GDPR-compliant analytics pipeline in BigQuery. Customer PII must be automatically detected, masked, or anonymized while still allowing analytics. Which GCP service is most suitable?

A) Cloud Data Loss Prevention (DLP)

B) Cloud KMS

C) Cloud Identity-Aware Proxy (IAP)

D) Cloud Functions

Answer

A) Cloud Data Loss Prevention (DLP)

Explanation

A) Cloud Data Loss Prevention (DLP) is designed to detect, classify, and transform sensitive data such as PII. It integrates with BigQuery, Cloud Storage, and Pub/Sub, allowing secure large-scale analytics. DLP provides pre-built detectors for common PII types including names, emails, phone numbers, social security numbers, and financial data. It supports masking, tokenization, redaction, and format-preserving encryption, enabling analytics on anonymized datasets while protecting sensitive information.

DLP transformations can occur inline during ingestion or query execution, minimizing operational overhead and ensuring GDPR compliance. Audit logs provide traceability, documenting how PII was detected and transformed. DLP scales to petabyte-level datasets, ensuring automated protection for large-scale analytics pipelines. This reduces operational risk, enforces consistent data protection, and allows actionable analytics while remaining compliant.

B) Cloud KMS secures encryption keys but does not detect or transform PII, so it cannot ensure GDPR-compliant analytics.

C) Cloud Identity-Aware Proxy secures access to applications but does not provide automated detection or anonymization, making it unsuitable for GDPR analytics.

D) Cloud Functions can implement custom PII detection and masking, but it requires development effort, is less scalable, and lacks the automation and reliability of DLP.

Q144

A company wants to monitor BigQuery query costs and trigger near real-time alerts for expensive queries. The solution must be serverless, scalable, and low-maintenance. Which approach is most effective?

A) Query INFORMATION_SCHEMA tables with Cloud Functions and Pub/Sub alerts

B) Query BigQuery from Cloud SQL manually

C) Export logs to Cloud Storage and process offline

D) Store query metadata in Bigtable and poll for alerts

Answer

A) Query INFORMATION_SCHEMA tables with Cloud Functions and Pub/Sub alerts

Explanation

A) INFORMATION_SCHEMA tables provide metadata about BigQuery jobs, including execution time, bytes processed, and costs. Cloud Functions can query these tables periodically to identify expensive queries and trigger alerts via Pub/Sub, email, or other notification channels. This fully serverless approach scales automatically and requires minimal operational effort.

Custom thresholds allow monitoring of specific users, workloads, or query patterns. Using Cloud Functions and Pub/Sub ensures near real-time monitoring and proactive cost management. This solution is cost-effective, low-maintenance, and follows best practices for monitoring BigQuery query costs at scale.

B) Querying BigQuery from Cloud SQL manually is not scalable. Cloud SQL cannot handle high metadata volumes efficiently, and manual processes introduce latency that prevents near real-time alerts.

C) Exporting logs to Cloud Storage and processing offline introduces delays, making near real-time monitoring impractical.

D) Storing query metadata in Bigtable and polling for alerts adds operational complexity. Polling increases overhead and is less efficient than the serverless Cloud Functions + Pub/Sub approach.

Q145

A company wants to implement a multi-tenant SaaS analytics solution on BigQuery. Each tenant’s data must remain isolated, but occasional cross-tenant queries are required. The dataset will grow to petabytes. Which table design is most suitable?

A) Single table with tenant_id column and clustered by tenant_id

B) Separate BigQuery projects per tenant

C) Store data in Cloud SQL and replicate to BigQuery

D) Multiple unpartitioned tables per tenant

Answer

A) Single table with tenant_id column and clustered by tenant_id

Explanation

A) A single table with a tenant_id column and clustering by tenant_id is the most scalable and operationally efficient design for multi-tenant SaaS analytics. Clustering improves query performance for tenant-specific filtering and cross-tenant aggregations. Partitioning by ingestion timestamp further optimizes queries filtered by time.

This design scales to petabyte-level datasets without the overhead of managing multiple tables or projects. Cross-tenant queries are straightforward, requiring only filtering or grouping by tenant_id. Centralized schema management ensures consistent schema evolution. BigQuery’s serverless architecture handles scaling, metadata management, and query optimization automatically. This approach minimizes operational complexity, reduces cost, maintains high performance, and supports both dashboards and ad hoc analytics efficiently, providing isolation and analytical flexibility.

B) Separate BigQuery projects per tenant increases operational complexity, including billing, IAM management, and schema evolution. Cross-tenant queries are cumbersome and inefficient.

C) Storing data in Cloud SQL and replicating to BigQuery introduces latency and operational complexity, making it unsuitable for petabyte-scale multi-tenant analytics.

D) Multiple unpartitioned tables per tenant increase operational overhead. Queries across multiple tables require unions or joins, reducing efficiency and maintainability.

Q146

A company wants to build a real-time analytics pipeline for connected vehicle telemetry. The system must ingest millions of events per second, guarantee exactly-once processing, perform windowed aggregations, and support low-latency dashboards for fleet monitoring, predictive maintenance, and route optimization. Which architecture is most suitable?

A) Cloud Pub/Sub → Dataflow → BigQuery

B) Cloud Storage → Dataproc → BigQuery

C) Cloud SQL → Cloud Functions → BigQuery

D) Bigtable → Cloud Run → BigQuery

Answer

A) Cloud Pub/Sub → Dataflow → BigQuery

Explanation

A) Cloud Pub/Sub, Dataflow, and BigQuery together provide a fully managed, serverless architecture suitable for ingesting and processing millions of connected vehicle telemetry events per second, such as GPS location, speed, fuel levels, tire pressure, and engine diagnostics. Cloud Pub/Sub auto-scales to accommodate sudden spikes in traffic from thousands of vehicles reporting simultaneously, ensuring reliable and durable event delivery with acknowledgment-based guarantees.

Dataflow ensures exactly-once processing, which is critical for metrics like cumulative miles driven, maintenance prediction, and real-time alerts for engine anomalies. Its support for stateful and windowed processing allows session-based aggregations, such as total distance covered per trip, average speed per hour, or anomaly detection over sliding time windows. Windowed aggregations help fleet managers monitor performance trends, predict vehicle maintenance needs, and optimize routes efficiently.

BigQuery serves as the analytics layer, storing both raw telemetry data and aggregated metrics. Its serverless, columnar architecture scales seamlessly to petabyte-level datasets and supports low-latency queries for dashboards, reporting, and historical analysis. Integration with BI tools like Looker or Data Studio allows fleet managers to monitor vehicle health, utilization, and route efficiency in near real time.

B) Cloud Storage → Dataproc → BigQuery is batch-oriented, requiring events to be stored first and processed later. This introduces latency that makes real-time monitoring impractical. Dataproc requires cluster management, and exactly-once processing is not guaranteed, risking inaccurate vehicle metrics.

C) Cloud SQL → Cloud Functions → BigQuery cannot handle millions of events per second. Cloud SQL is optimized for transactional workloads, and Cloud Functions have memory and execution limits. Implementing exactly-once processing and windowed aggregations at this scale would require complex orchestration.

D) Bigtable → Cloud Run → BigQuery can store raw telemetry efficiently but lacks stream processing and exactly-once semantics. Windowed and session-based aggregations would require additional orchestration, increasing latency, complexity, and operational risk.

Q147

A company wants to store IoT sensor data in BigQuery for analytics. Queries frequently filter by timestamp, device type, and geographic region. The dataset is expected to grow to petabytes. Which table design is most suitable for performance and cost efficiency?

A) Partition by ingestion timestamp and cluster by device type and region

B) Partition by device type and cluster by timestamp

C) Use a single unpartitioned table

D) Create multiple tables per device type and region

Answer

A) Partition by ingestion timestamp and cluster by device type and region

Explanation

A) Partitioning by ingestion timestamp ensures that queries filtered by time scan only relevant partitions, reducing the amount of data processed and lowering query costs. Clustering by device type and region physically organizes rows within each partition, improving query performance for filtering and aggregation. This design scales efficiently for multi-petabyte datasets and maintains consistent query performance as the dataset grows.

A single partitioned and clustered table reduces operational complexity, eliminating the need to manage multiple tables or datasets. Partitioned and clustered tables support both streaming and batch ingestion, enabling real-time anomaly detection, trend analysis, and predictive maintenance. BigQuery automatically manages partition metadata, optimizes query execution, and scales compute and storage seamlessly. This is ideal for IoT telemetry where queries often involve multiple dimensions such as device type, region, and timestamp.

B) Partitioning by device type creates many small partitions, increasing metadata overhead and reducing query efficiency. Clustering by timestamp alone does not optimize common queries filtered by region.

C) A single unpartitioned table is inefficient for petabyte-scale datasets. Queries filtered by timestamp, device type, or region would scan the entire table, leading to higher costs and slower performance.

D) Multiple tables per device type and region increase operational complexity. Schema evolution, cross-device queries, and table maintenance become cumbersome. Queries across multiple tables require unions or joins, reducing efficiency and increasing the risk of errors.

Q148

A company wants to implement a GDPR-compliant analytics pipeline in BigQuery. Customer PII must be automatically detected, masked, or anonymized while still allowing analytics. Which GCP service is most suitable?

A) Cloud Data Loss Prevention (DLP)

B) Cloud KMS

C) Cloud Identity-Aware Proxy (IAP)

D) Cloud Functions

Answer

A) Cloud Data Loss Prevention (DLP)

Explanation

A) Cloud Data Loss Prevention (DLP) is designed to detect, classify, and transform sensitive data such as PII. It integrates with BigQuery, Cloud Storage, and Pub/Sub, enabling large-scale analytics pipelines to be GDPR-compliant. DLP provides pre-built detectors for common PII types, including names, email addresses, phone numbers, social security numbers, and financial data. Transformations include masking, tokenization, redaction, and format-preserving encryption, which allow analytics on anonymized datasets while protecting sensitive information.

DLP can perform transformations inline during ingestion or query execution, reducing operational overhead and ensuring compliance. Audit logs document how PII was detected and transformed. DLP scales to petabyte-level datasets, automating the protection of sensitive information and reducing the risk of non-compliance. This enables analytics teams to gain insights without exposing sensitive customer data, ensuring secure and compliant operations.

B) Cloud KMS secures encryption keys but does not detect or transform PII, so it cannot enforce GDPR-compliant analytics.

C) Cloud Identity-Aware Proxy secures access to applications but does not provide automated PII detection or anonymization, making it unsuitable for GDPR analytics.

D) Cloud Functions can implement custom PII detection and masking but require significant development effort, are less scalable, and lack built-in automation and reliability compared to DLP.

Q149

A company wants to monitor BigQuery query costs and trigger near real-time alerts for expensive queries. The solution must be serverless, scalable, and low-maintenance. Which approach is most effective?

A) Query INFORMATION_SCHEMA tables with Cloud Functions and Pub/Sub alerts

B) Query BigQuery from Cloud SQL manually

C) Export logs to Cloud Storage and process offline

D) Store query metadata in Bigtable and poll for alerts

Answer

A) Query INFORMATION_SCHEMA tables with Cloud Functions and Pub/Sub alerts

Explanation

A) INFORMATION_SCHEMA tables provide metadata about BigQuery jobs, including execution time, bytes processed, and costs. Cloud Functions can periodically query these tables to detect expensive queries and trigger alerts through Pub/Sub, email, or other channels. This approach is fully serverless, scales automatically, and requires minimal operational effort.

Custom thresholds can monitor specific users, queries, or workloads. Serverless Cloud Functions and Pub/Sub enable near real-time monitoring and proactive cost management, ensuring organizations can optimize query efficiency and avoid unexpected charges. This solution is cost-effective, low-maintenance, and follows best practices for monitoring BigQuery costs at scale.

B) Querying BigQuery from Cloud SQL manually is not scalable. Cloud SQL cannot handle high metadata volumes efficiently, and manual processes introduce latency, preventing near real-time alerts.

C) Exporting logs to Cloud Storage and processing offline introduces latency, making real-time monitoring impractical.

D) Storing query metadata in Bigtable and polling for alerts increases operational complexity. Polling adds overhead and is less efficient than a serverless Cloud Functions + Pub/Sub approach.

Q150

A company wants to implement a multi-tenant SaaS analytics solution on BigQuery. Each tenant’s data must remain isolated, but occasional cross-tenant queries are required. The dataset will grow to petabytes. Which table design is most suitable?

A) Single table with tenant_id column and clustered by tenant_id

B) Separate BigQuery projects per tenant

C) Store data in Cloud SQL and replicate to BigQuery

D) Multiple unpartitioned tables per tenant

Answer

A) Single table with tenant_id column and clustered by tenant_id

Explanation

A) A single table with a tenant_id column and clustering by tenant_id provides the most scalable, operationally efficient, and cost-effective design for multi-tenant SaaS analytics. Clustering improves query performance for tenant-specific filters and cross-tenant aggregations. Partitioning by ingestion timestamp further optimizes queries filtered by time.

This design scales to petabyte-level datasets without the overhead of managing multiple tables or projects. Cross-tenant queries are straightforward, requiring only filtering or grouping by tenant_id. Centralized schema management ensures consistent schema evolution. BigQuery’s serverless architecture handles scaling, metadata, and query optimization automatically. This design minimizes operational complexity, reduces costs, maintains high performance, and supports dashboards and ad hoc analytics efficiently while providing tenant isolation and analytical flexibility.

B) Separate BigQuery projects per tenant increases operational complexity, including billing, IAM configuration, and schema management. Cross-tenant queries are cumbersome and inefficient.

C) Storing data in Cloud SQL and replicating to BigQuery introduces latency and operational complexity, making it unsuitable for petabyte-scale multi-tenant analytics.

D) Multiple unpartitioned tables per tenant increase operational overhead. Queries across multiple tables require unions or joins, reducing efficiency and maintainability.

Q151

A company wants to build a real-time analytics pipeline for mobile app events. The system must ingest millions of events per second, guarantee exactly-once processing, perform session-based and windowed aggregations, and support low-latency dashboards for user engagement and retention metrics. Which architecture is most suitable?

A) Cloud Pub/Sub → Dataflow → BigQuery

B) Cloud Storage → Dataproc → BigQuery

C) Cloud SQL → Cloud Functions → BigQuery

D) Bigtable → Cloud Run → BigQuery

Answer

A) Cloud Pub/Sub → Dataflow → BigQuery

Explanation

A) Cloud Pub/Sub, Dataflow, and BigQuery together form a serverless, fully managed architecture capable of ingesting and processing millions of mobile app events per second. Cloud Pub/Sub automatically scales to accommodate traffic spikes, such as during app launches, marketing campaigns, or viral events, while providing durable message storage and reliable delivery through acknowledgments.

Dataflow ensures exactly-once processing, preventing duplicate event counts and ensuring accurate user engagement metrics. It supports stateful and windowed processing, allowing session-based aggregations like average session duration, retention rate calculations, and funnel analysis. Sliding and tumbling windows can be used to monitor short-term engagement patterns or hourly metrics. This enables product teams to react in near real-time to changes in user behavior.

BigQuery acts as the analytics layer, storing both raw event data and aggregated metrics. Its serverless, columnar architecture scales seamlessly to petabyte-level datasets and supports low-latency queries for dashboards and reporting. Integration with Looker, Data Studio, or other BI tools provides near real-time visualization of retention metrics, engagement trends, and campaign effectiveness.

B) Cloud Storage → Dataproc → BigQuery is batch-oriented. Storing events and processing them in batches introduces latency, making it unsuitable for real-time engagement monitoring. Dataproc requires manual cluster management, and exactly-once processing is not guaranteed, leading to potential inaccuracies.

C) Cloud SQL → Cloud Functions → BigQuery cannot handle millions of events per second. Cloud SQL is designed for transactional workloads, and Cloud Functions have memory and execution limits. Implementing exactly-once processing and windowed aggregations at scale would require complex orchestration and increase operational risk.

D) Bigtable → Cloud Run → BigQuery can store raw mobile app events efficiently, but lacks distributed stream processing and exactly-once semantics. Windowed and session-based aggregations require additional orchestration, increasing complexity, latency, and operational overhead.

Q152

A company wants to store IoT device telemetry data in BigQuery. Queries frequently filter by timestamp, device type, and region. The dataset is expected to grow to petabytes. Which table design is most suitable for performance and cost efficiency?

A) Partition by ingestion timestamp and cluster by device type and region

B) Partition by device type and cluster by timestamp

C) Use a single unpartitioned table

D) Create multiple tables per device type and region

Answer

A) Partition by ingestion timestamp and cluster by device type and region

Explanation

A) Partitioning by ingestion timestamp ensures that queries filtered by time scan only relevant partitions, reducing data scanned and lowering costs. Clustering by device type and region physically organizes rows within each partition, optimizing query performance for these common filters. This design scales efficiently for multi-petabyte datasets and maintains high query performance as the dataset grows.

A single partitioned and clustered table reduces operational complexity by avoiding the need to manage multiple tables or datasets. Partitioned and clustered tables support both streaming and batch ingestion, enabling real-time anomaly detection, trend analysis, and predictive maintenance. BigQuery manages partition metadata, query optimization, and auto-scaling, ensuring a consistent and high-performance experience. This architecture is particularly effective for IoT telemetry workloads, where queries often involve multiple dimensions such as device type, region, and timestamp.

B) Partitioning by device type creates many small partitions, increasing metadata overhead and decreasing query efficiency. Clustering by timestamp alone does not optimize common queries filtered by region, resulting in higher latency and cost.

C) A single unpartitioned table is inefficient at petabyte scale. Queries filtered by timestamp, device type, or region require scanning the entire dataset, increasing costs and reducing performance.

D) Multiple tables per device type and region increase operational complexity. Schema evolution, cross-device queries, and table maintenance become cumbersome. Queries spanning multiple tables require unions or joins, which reduce efficiency and increase the risk of errors.

Q153

A company wants to implement a GDPR-compliant analytics pipeline in BigQuery. Customer PII must be automatically detected, masked, or anonymized while still allowing analytics. Which GCP service is most suitable?

A) Cloud Data Loss Prevention (DLP)

B) Cloud KMS

C) Cloud Identity-Aware Proxy (IAP)

D) Cloud Functions

Answer

A) Cloud Data Loss Prevention (DLP)

Explanation

A) Cloud Data Loss Prevention (DLP) is designed to detect, classify, and transform sensitive data such as PII. It integrates with BigQuery, Cloud Storage, and Pub/Sub, allowing large-scale analytics pipelines to comply with GDPR. DLP provides pre-built detectors for common PII types, including names, email addresses, phone numbers, social security numbers, and financial data. Transformations include masking, tokenization, redaction, and format-preserving encryption, enabling analytics on anonymized datasets while protecting sensitive information.

DLP can apply transformations inline during ingestion or query execution, minimizing operational overhead and ensuring regulatory compliance. Audit logs provide traceability, documenting how PII was detected and transformed. DLP scales to petabyte-level datasets, automating sensitive data protection and reducing risk of non-compliance. This enables organizations to perform analytics without exposing customer data, ensuring secure and compliant operations.

B) Cloud KMS secures encryption keys but does not detect or transform PII, so it cannot enforce GDPR-compliant analytics pipelines.

C) Cloud Identity-Aware Proxy secures access to applications but does not provide automated PII detection or anonymization, making it unsuitable for GDPR compliance.

D) Cloud Functions can implement custom PII detection and masking, but this requires development effort, is less scalable, and lacks the automation and reliability provided by Cloud DLP.

Q154

A company wants to monitor BigQuery query costs and trigger near real-time alerts for expensive queries. The solution must be serverless, scalable, and low-maintenance. Which approach is most effective?

A) Query INFORMATION_SCHEMA tables with Cloud Functions and Pub/Sub alerts

B) Query BigQuery from Cloud SQL manually

C) Export logs to Cloud Storage and process offline

D) Store query metadata in Bigtable and poll for alerts

Answer

A) Query INFORMATION_SCHEMA tables with Cloud Functions and Pub/Sub alerts

Explanation

A) INFORMATION_SCHEMA tables provide metadata about BigQuery jobs, including execution time, bytes processed, and costs. Cloud Functions can periodically query these tables to detect expensive queries and trigger alerts via Pub/Sub, email, or other notification channels. This approach is fully serverless, scales automatically with query volume, and requires minimal operational effort.

Custom thresholds can monitor specific users, queries, or workloads. Using serverless Cloud Functions and Pub/Sub enables near real-time monitoring, proactive cost management, and prevention of unexpected charges. This solution is cost-effective, low-maintenance, and aligns with best practices for monitoring BigQuery query costs at scale.

B) Querying BigQuery from Cloud SQL manually is not scalable. Cloud SQL cannot handle high metadata volumes efficiently, and manual processes introduce latency, preventing near real-time alerts.

C) Exporting logs to Cloud Storage and processing offline introduces latency, making near real-time monitoring impractical.

D) Storing query metadata in Bigtable and polling for alerts increases operational complexity. Polling adds overhead and is less efficient than a serverless Cloud Functions + Pub/Sub solution.

Q155

A company wants to implement a multi-tenant SaaS analytics solution on BigQuery. Each tenant’s data must remain isolated, but occasional cross-tenant queries are required. The dataset will grow to petabytes. Which table design is most suitable?

A) Single table with tenant_id column and clustered by tenant_id

B) Separate BigQuery projects per tenant

C) Store data in Cloud SQL and replicate to BigQuery

D) Multiple unpartitioned tables per tenant

Answer

A) Single table with tenant_id column and clustered by tenant_id

Explanation

A) A single table with a tenant_id column and clustering by tenant_id is the most scalable, operationally efficient, and cost-effective design for multi-tenant SaaS analytics. Clustering improves query performance for tenant-specific filtering and cross-tenant aggregations. Partitioning by ingestion timestamp further optimizes queries filtered by time.

This design scales to petabyte-level datasets without the operational overhead of managing multiple tables or projects. Cross-tenant queries are straightforward, requiring only filtering or grouping by tenant_id. Centralized schema management ensures consistent schema evolution. BigQuery’s serverless architecture handles scaling, metadata, and query optimization automatically. This design minimizes operational complexity, reduces costs, maintains high performance, and supports both dashboards and ad hoc analytics efficiently while providing tenant isolation and analytical flexibility.

B) Separate BigQuery projects per tenant increases operational complexity, including billing, IAM management, and schema evolution. Cross-tenant queries are cumbersome and inefficient.

C) Storing data in Cloud SQL and replicating to BigQuery introduces latency and operational complexity, making it unsuitable for petabyte-scale multi-tenant analytics.

D) Multiple unpartitioned tables per tenant increase operational overhead. Queries across multiple tables require unions or joins, reducing efficiency and maintainability.

Q156

A company wants to implement a real-time analytics pipeline for e-commerce transactions. The system must ingest millions of events per second, ensure exactly-once processing, perform windowed and session-based aggregations, and provide low-latency dashboards for sales, inventory, and customer behavior. Which architecture is most suitable?

A) Cloud Pub/Sub → Dataflow → BigQuery

B) Cloud Storage → Dataproc → BigQuery

C) Cloud SQL → Cloud Functions → BigQuery

D) Bigtable → Cloud Run → BigQuery

Answer

A) Cloud Pub/Sub → Dataflow → BigQuery

Explanation

A) Cloud Pub/Sub, Dataflow, and BigQuery provide a fully managed, serverless architecture designed for high-throughput, low-latency streaming analytics, making it ideal for e-commerce transactions. Cloud Pub/Sub acts as the ingestion layer, capable of handling millions of events per second from sources such as product views, clicks, purchases, and inventory updates. It provides durable message storage with acknowledgment guarantees, ensuring no events are lost even during peak shopping periods like Black Friday.

Dataflow provides stream processing with exactly-once semantics, guaranteeing that each transaction is processed once and only once. This is critical for revenue calculations, inventory management, and fraud detection. Dataflow supports windowed and session-based aggregations, enabling metrics such as total sales per hour, average purchase value per session, and active users per region. Sliding or tumbling windows allow marketing teams to track short-term trends, monitor campaign effectiveness, and react quickly to anomalies.

BigQuery acts as the analytics engine, storing both raw event data and pre-aggregated metrics for dashboards. Its serverless, columnar architecture scales automatically to petabytes of data, allowing low-latency queries for near real-time analytics. Integration with BI tools like Looker or Data Studio enables dashboards that track revenue, inventory, and customer behavior dynamically.

B) Cloud Storage → Dataproc → BigQuery is a batch-oriented architecture. Events are first stored and then processed, introducing latency unsuitable for real-time dashboards. Dataproc requires manual cluster management, and exactly-once semantics are not guaranteed, increasing the risk of duplicate or missing data.

C) Cloud SQL → Cloud Functions → BigQuery is not scalable for millions of events per second. Cloud SQL is optimized for transactional workloads, and Cloud Functions have memory and execution time limits. Implementing exactly-once processing and windowed aggregations at scale is complex, prone to errors, and operationally heavy.

D) Bigtable → Cloud Run → BigQuery can store raw transactional data efficiently, but it lacks distributed stream processing and exactly-once guarantees. Windowed and session-based aggregations would require custom orchestration, increasing latency and operational complexity. This design also complicates dashboard queries and may not support near real-time analytics at scale.

Q157

A company wants to store IoT sensor telemetry data in BigQuery. Queries often filter by timestamp, device type, and geographic region. The dataset is projected to reach petabytes. Which table design is most suitable for performance and cost efficiency?

A) Partition by ingestion timestamp and cluster by device type and region

B) Partition by device type and cluster by timestamp

C) Use a single unpartitioned table

D) Create multiple tables per device type and region

Answer

A) Partition by ingestion timestamp and cluster by device type and region

Explanation

A) Partitioning by ingestion timestamp ensures queries filtered by time only scan relevant partitions, reducing data processed and lowering query costs. Clustering by device type and region physically organizes rows within each partition, optimizing query performance for these common filters. This architecture scales efficiently for multi-petabyte datasets and maintains predictable query performance as the dataset grows.

A single partitioned and clustered table minimizes operational complexity, eliminating the need to manage multiple tables or datasets. Partitioned and clustered tables support both streaming and batch ingestion, allowing real-time anomaly detection, predictive maintenance, and trend analysis. BigQuery automatically manages partition metadata, optimizes queries, and scales compute and storage seamlessly.

B) Partitioning by device type creates many small partitions, increasing metadata overhead and decreasing query efficiency. Clustering by timestamp alone does not optimize common queries filtered by region, leading to higher latency and cost.

C) A single unpartitioned table is inefficient at petabyte scale. Queries filtered by timestamp, device type, or region would scan the entire dataset, significantly increasing costs and reducing performance.

D) Multiple tables per device type and region increase operational complexity. Schema evolution, cross-device queries, and table maintenance become cumbersome. Queries spanning multiple tables require unions or joins, reducing efficiency and increasing operational risk.

Q158

A company wants to implement a GDPR-compliant analytics pipeline in BigQuery. Customer PII must be automatically detected, masked, or anonymized while still allowing analytics. Which GCP service is most suitable?

A) Cloud Data Loss Prevention (DLP)

B) Cloud KMS

C) Cloud Identity-Aware Proxy (IAP)

D) Cloud Functions

Answer

A) Cloud Data Loss Prevention (DLP)

Explanation

A) Cloud Data Loss Prevention (DLP) is designed for detection, classification, and transformation of sensitive data, such as PII. It integrates with BigQuery, Cloud Storage, and Pub/Sub, enabling automated GDPR-compliant pipelines. DLP provides pre-built detectors for common PII types, including names, emails, phone numbers, social security numbers, and financial data. Transformations include masking, tokenization, redaction, and format-preserving encryption, allowing analytics on anonymized datasets while protecting sensitive information.

DLP can apply transformations inline during ingestion or query execution, minimizing operational overhead and ensuring compliance. Audit logs provide traceability, documenting how PII was detected and transformed. It scales to petabyte-level datasets, automating data protection, and reducing compliance risk. DLP enables organizations to perform analytics securely while ensuring sensitive customer information is protected.

B) Cloud KMS secures encryption keys but does not detect or transform PII, so it cannot enforce GDPR-compliant analytics.

C) Cloud Identity-Aware Proxy secures access to applications but does not provide automated detection or anonymization, making it unsuitable for GDPR compliance.

D) Cloud Functions can implement custom PII detection and masking but require significant development effort, are less scalable, and lack the built-in automation and reliability of Cloud DLP.

Q159

A company wants to monitor BigQuery query costs and trigger near real-time alerts for expensive queries. The solution must be serverless, scalable, and low-maintenance. Which approach is most effective?

A) Query INFORMATION_SCHEMA tables with Cloud Functions and Pub/Sub alerts

B) Query BigQuery from Cloud SQL manually

C) Export logs to Cloud Storage and process offline

D) Store query metadata in Bigtable and poll for alerts

Answer

A) Query INFORMATION_SCHEMA tables with Cloud Functions and Pub/Sub alerts

Explanation

A) INFORMATION_SCHEMA tables provide metadata about BigQuery jobs, including execution time, bytes processed, and costs. Cloud Functions can query these tables periodically to identify expensive queries and trigger alerts via Pub/Sub, email, or other channels. This approach is fully serverless, automatically scales with query volume, and requires minimal operational effort.

Custom thresholds allow monitoring of specific users, queries, or workloads. Using Cloud Functions and Pub/Sub enables near real-time monitoring and proactive cost management. This ensures organizations can detect runaway queries, prevent unexpected charges, and optimize query efficiency. The serverless architecture minimizes operational burden while maintaining high responsiveness for alerts and notifications.

B) Querying BigQuery from Cloud SQL manually is not scalable. Cloud SQL cannot efficiently handle high metadata volumes, and manual processes introduce latency that prevents near real-time alerts.

C) Exporting logs to Cloud Storage and processing offline introduces delays, making real-time monitoring impractical.

D) Storing query metadata in Bigtable and polling for alerts adds operational complexity. Polling is inefficient and does not scale as well as the serverless Cloud Functions + Pub/Sub approach.

Q160

A company wants to implement a multi-tenant SaaS analytics solution on BigQuery. Each tenant’s data must remain isolated, but occasional cross-tenant queries are required. The dataset will grow to petabytes. Which table design is most suitable?

A) Single table with tenant_id column and clustered by tenant_id
B) Separate BigQuery projects per tenant
C) Store data in Cloud SQL and replicate to BigQuery
D) Multiple unpartitioned tables per tenant

Answer

A) Single table with tenant_id column and clustered by tenant_id

Explanation

A) A single table with a tenant_id column and clustering by tenant_id provides the most scalable, cost-efficient, and operationally manageable solution for multi-tenant SaaS analytics in BigQuery. In this design, all tenants share the same table, but data is logically separated using the tenant_id column. This ensures that each tenant’s data remains isolated for standard queries while still allowing for occasional cross-tenant analytics without requiring complex data movement or restructuring.

Clustering in BigQuery organizes data based on the values in the specified columns (in this case, tenant_id). This allows queries that filter by tenant_id to read only the relevant portions of the table, significantly reducing scan costs and improving query performance. For analytics workloads where queries often filter by tenant or aggregate data across tenants, clustering provides high efficiency while maintaining flexibility. Additionally, partitioning by ingestion timestamp or event date can further optimize queries that are time-bound, a common requirement for large-scale analytics workloads, allowing queries to skip over irrelevant historical data.

This single-table approach also excels at scaling to petabyte-level datasets. BigQuery’s serverless architecture automatically handles scaling, storage optimization, and query execution in a distributed fashion. Unlike managing separate tables or projects for each tenant, this design eliminates the operational burden of tracking multiple schemas, managing independent metadata, or handling complex cross-tenant query patterns. Centralized schema management ensures consistent schema evolution across tenants, reducing the risk of schema drift or compatibility issues. This design also simplifies the implementation of new features or schema changes, as only one table needs to be updated, rather than multiple tables across different projects.

For cross-tenant analytics, a single table enables straightforward operations. Aggregations, joins, or group-by queries that span multiple tenants require only filtering or grouping by tenant_id. Security and data isolation can still be maintained by using row-level security policies or authorized views, allowing tenants to query only their own data while permitting administrators or analysts to perform cross-tenant analytics when necessary. This balances security, tenant isolation, and analytical flexibility in a seamless way.

B) Using separate BigQuery projects per tenant introduces significant operational complexity. Each project requires independent billing, IAM configuration, and schema management. Performing cross-tenant queries in this setup is cumbersome because queries would need to access multiple projects simultaneously, often requiring unions or external data references. Managing petabyte-scale datasets across multiple projects can also lead to inefficient storage and compute usage, increasing costs and reducing performance.

C) Storing data in Cloud SQL and replicating it to BigQuery is inefficient for petabyte-scale analytics. Cloud SQL is designed for transactional workloads rather than analytics at this scale. Replicating data adds operational overhead, introduces latency, and complicates data pipelines. Querying petabyte-scale datasets in Cloud SQL would be slow and costly, making it unsuitable for high-performance analytics workloads that SaaS platforms require.

D) Creating multiple unpartitioned tables per tenant increases operational and maintenance overhead. Queries that span tenants require unions or joins across multiple tables, complicating query logic and reducing performance. Without partitioning or clustering, each query scans entire tables, leading to higher costs and slower performance. Managing schema changes across many tables also increases the risk of inconsistency and operational errors.

A single, clustered table with a tenant_id column combines scalability, cost-efficiency, operational simplicity, and analytical flexibility. It supports both tenant-level isolation and cross-tenant analytics, making it the most suitable design for a multi-tenant SaaS analytics solution at petabyte scale.