Google  Professional Data Engineer on Google Cloud Platform Exam  Dumps and Practice Test Questions Set 9 Q 161- 180

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Q161

A company wants to build a real-time analytics pipeline for online gaming telemetry. The system must ingest millions of events per second, guarantee exact-once processing, perform session-based and windowed aggregations, and provide low-latency dashboards for game analytics, player engagement, and fraud detection. 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 capable of handling millions of online gaming events per second. Cloud Pub/Sub can ingest massive bursts of events from game clients, such as player actions, in-game purchases, chat messages, and matchmaking events. It ensures durable delivery and supports high-throughput streaming with acknowledgement-based guarantees to prevent data loss.

Dataflow guarantees exactly-once processing, critical for accurate player statistics, revenue calculations, and real-time leaderboard updates. Its stateful and windowed processing capabilities allow session-based aggregations, such as calculating average session length, time spent in specific game modes, and player retention over sliding or tumbling windows. Fraud detection can be implemented in the pipeline using custom transformations or rules, enabling near real-time detection of anomalies like multiple logins from the same IP or unusual purchase patterns.

BigQuery serves as the analytics layer, storing raw events and aggregated metrics for real-time dashboards. Its serverless columnar architecture scales seamlessly to petabyte-level datasets and supports low-latency queries for dashboards, enabling product managers, analysts, and game designers to make decisions based on live data. Integration with Looker, Data Studio, or other BI tools allows detailed player engagement reports, campaign analysis, and monetization insights.

B) Cloud Storage → Dataproc → BigQuery is batch-oriented. Storing events and processing them in batches introduces latency that prevents near real-time analysis. Dataproc requires cluster management and does not guarantee exactly-once semantics, potentially leading to duplicate or missing events.

C) Cloud SQL → Cloud Functions → BigQuery cannot handle millions of events per second. Cloud SQL is optimized for transactional workloads, and Cloud Functions have execution time and memory limits, making them unsuitable for high-throughput, low-latency pipelines. Implementing exactly-once processing and session-based aggregations would require complex orchestration and additional infrastructure.

D) Bigtable → Cloud Run → BigQuery can store raw telemetry efficiently, but it lacks stream processing and exactly-once guarantees. Session-based and windowed aggregations would require additional orchestration, increasing latency and operational complexity. It also complicates integration with real-time dashboards.

Q162

A company wants to store IoT device telemetry data in BigQuery. 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 the relevant partitions, reducing the amount of data processed and lowering costs. Clustering by device type and region organizes rows within each partition, optimizing query performance for common filters. This design scales efficiently to petabyte-level datasets and ensures consistent query performance as the dataset grows.

A single partitioned and clustered table reduces operational complexity by eliminating the need to manage multiple tables or datasets. Partitioned and clustered tables support streaming and batch ingestion, enabling real-time anomaly detection, predictive maintenance, and trend analysis. BigQuery automatically manages partition metadata, query optimization, and compute scaling. For IoT telemetry, where queries typically involve multiple dimensions like timestamp, device type, and region, this design ensures high performance and cost efficiency.

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, leading to higher latency and cost.

C) A single unpartitioned table is inefficient at petabyte scale. Queries filtered by timestamp, device type, or region scan the entire dataset, resulting in 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 risk of errors.

Q163

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 GDPR-compliant pipelines. DLP provides pre-built detectors for common PII types, including names, emails, phone numbers, social security numbers, and financial data. It supports transformations like masking, tokenization, redaction, and format-preserving encryption, enabling 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 provide traceability, documenting how PII was detected and transformed. DLP scales to petabyte-level datasets, automating protection of sensitive data and reducing the risk of non-compliance. This allows organizations to perform analytics without exposing sensitive information while maintaining operational efficiency.

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 it requires development effort, is less scalable, and lacks the automation and reliability of Cloud DLP.

Q164

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 serverless approach scales automatically with query volume and requires minimal operational effort.

Custom thresholds can be defined to monitor specific users, queries, or workloads. Cloud Functions and Pub/Sub enable near real-time monitoring and proactive cost management, preventing runaway queries and unexpected charges. This architecture 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 efficiently handle large metadata volumes, and manual processes introduce latency, preventing 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 increases operational complexity. Polling is less efficient and does not scale as effectively as a serverless Cloud Functions + Pub/Sub solution.

Q165

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 filters 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 automatically handles scaling, metadata, and query optimization. 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.

Q166

A company wants to build a real-time analytics pipeline for live video streaming metrics, including viewer counts, watch time, and engagement events. The system must ingest millions of events per second, guarantee exactly-once processing, perform session-based and windowed aggregations, and provide low-latency dashboards for product managers and content creators. 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 ideal for high-throughput, low-latency streaming analytics. Cloud Pub/Sub can ingest millions of events per second from video clients, capturing metrics such as viewer joins, exits, watch duration, chat messages, and engagement actions like likes or shares. Its auto-scaling ensures consistent ingestion even during sudden spikes, such as live sports events or viral video releases.

Dataflow ensures exactly-once processing, which is crucial for accurate reporting of metrics like total watch time, unique viewers, and engagement counts. It supports stateful and windowed processing, enabling session-based metrics like average watch time per session or peak concurrent viewers over sliding time windows. This capability allows real-time insights for product managers and content creators to optimize streaming quality, user engagement, and content recommendations.

BigQuery acts as the analytics engine, storing both raw event data and aggregated metrics. Its serverless, columnar architecture can scale to petabytes of data and support low-latency queries for dashboards, reporting, and ad hoc analysis. Integration with Looker, Data Studio, or other BI tools allows stakeholders to visualize viewer metrics and engagement trends dynamically.

B) Cloud Storage → Dataproc → BigQuery is batch-oriented. Events stored in Cloud Storage and processed in batches introduce latency, making near real-time dashboards infeasible. Dataproc requires cluster management, and exactly-once processing is not guaranteed, potentially leading to inaccuracies.

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 execution and memory limits. Implementing exactly-once semantics and windowed aggregations at this scale would require complex orchestration.

D) Bigtable → Cloud Run → BigQuery can store raw metrics efficiently but lacks distributed stream processing and exactly-once guarantees. Implementing windowed or session-based aggregations requires additional orchestration, increasing latency and operational complexity. Dashboard queries would also be more complex and less performant.

Q167

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 the volume of data processed and lowering costs. Clustering by device type and region physically organizes rows within each partition, optimizing query performance for common filters. This architecture scales efficiently to petabyte-level datasets, maintaining consistent query performance even as the dataset grows.

Partitioned and clustered tables reduce operational complexity, avoiding the need to manage multiple tables or datasets. This design supports both streaming and batch ingestion, enabling real-time anomaly detection, predictive maintenance, and trend analysis. BigQuery automatically manages partition metadata, optimizes queries, and scales compute and storage seamlessly. For IoT telemetry, where queries often involve multiple dimensions such as timestamp, device type, and region, this design ensures high performance and cost efficiency.

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, resulting in higher latency and cost.

C) A single unpartitioned table is inefficient for petabyte-scale datasets. Queries filtered by timestamp, device type, or region require scanning the entire dataset, increasing costs and query time.

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 the risk of errors.

Q168

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 GDPR-compliant pipelines. 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, allowing analytics on anonymized datasets while protecting sensitive information.

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

B) Cloud KMS secures encryption keys but does not detect or transform PII, making it unsuitable for GDPR-compliant analytics pipelines.

C) Cloud Identity-Aware Proxy secures access to applications but does not provide automated PII detection or anonymization. It cannot ensure compliance for analytics pipelines.

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

Q169

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 query 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 with query volume, and requires minimal operational effort.

Custom thresholds can be set to monitor specific users, queries, or workloads. The serverless combination of Cloud Functions and Pub/Sub allows near real-time cost monitoring and proactive query management. This ensures that runaway queries are detected early, preventing unexpected billing spikes and enabling teams to optimize queries for efficiency. The architecture 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 efficiently handle metadata at large scale, and manual execution introduces latency that prevents real-time alerts.

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

D) Storing metadata in Bigtable and polling for alerts increases operational complexity. Polling is less efficient and does not scale as effectively as serverless Cloud Functions + Pub/Sub.

Q170

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 approach for multi-tenant SaaS analytics. Clustering improves query performance for tenant-specific filters and cross-tenant aggregations. Partitioning by ingestion timestamp further optimizes time-filtered queries.

This design scales to petabyte-level datasets without requiring management of multiple tables or projects. Cross-tenant queries are straightforward, needing 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 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 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.

Q171

A company wants to build a real-time analytics pipeline for financial transactions. The system must ingest millions of events per second, guarantee exactly-once processing, perform session-based and windowed aggregations, and provide low-latency dashboards for fraud detection, revenue monitoring, and customer insights. 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 form a fully managed, serverless architecture designed for high-throughput, low-latency streaming analytics, which is essential for financial transactions. Cloud Pub/Sub can handle millions of events per second, ingesting transactions such as payments, account updates, and user activity. It ensures durable delivery with acknowledgments, preventing data loss even during periods of high transaction volume, such as Black Friday sales or end-of-quarter banking operations.

Dataflow provides exactly-once processing, crucial for financial accuracy, revenue calculation, and fraud detection. It supports stateful and windowed aggregations, enabling metrics like total revenue per hour, average transaction per session, and identification of suspicious transaction patterns. Sliding windows can be applied to monitor near real-time trends, while session windows help track user behavior over discrete periods. Fraud detection rules can be applied within Dataflow using custom transformations or machine learning models to flag anomalies in real time.

BigQuery acts as the analytics layer, storing both raw and aggregated data. Its columnar architecture scales to petabytes of data and supports low-latency queries, enabling dashboards to reflect near real-time transaction volumes, revenue, and customer insights. Integration with BI tools such as Looker or Data Studio allows finance and operations teams to monitor and react to trends dynamically.

B) Cloud Storage → Dataproc → BigQuery is a batch-oriented architecture. Processing stored events in batches introduces latency, which is unsuitable for real-time dashboards. Dataproc requires manual cluster management, and exactly-once processing is not guaranteed, potentially causing inaccurate revenue reporting or missed fraud alerts.

C) Cloud SQL → Cloud Functions → BigQuery cannot handle millions of events per second. Cloud SQL is optimized for transactional workloads, and Cloud Functions have execution and memory limits, making it unsuitable for high-throughput financial data. Implementing exactly-once semantics and windowed aggregations at scale would require complex orchestration.

D) Bigtable → Cloud Run → BigQuery can store raw transaction data efficiently but lacks distributed stream processing and exactly-once guarantees. Implementing session-based or windowed aggregations would require additional orchestration, increasing latency and operational complexity. Dashboard queries would also be less efficient, making near real-time analytics difficult.

Q172

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 that queries filtered by time only scan relevant partitions, reducing data scanned and query costs. Clustering by device type and region physically organizes rows within each partition, optimizing query performance for commonly used filters. This design scales efficiently to petabyte-level datasets, maintaining consistent query performance even as the dataset grows.

Partitioned and clustered tables reduce operational complexity, eliminating the need to manage multiple tables or datasets. This design supports streaming and batch ingestion, enabling real-time anomaly detection, predictive maintenance, and trend analysis. BigQuery automatically manages partition metadata, query optimization, and scaling of compute and storage resources. For IoT telemetry workloads, where queries involve multiple dimensions such as timestamp, device type, and region, this design ensures high performance and cost efficiency.

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, 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 across multiple tables require unions or joins, reducing efficiency and increasing operational risk.

Q173

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 automated detection, classification, and transformation of sensitive data, including PII. It integrates with BigQuery, Cloud Storage, and Pub/Sub, enabling GDPR-compliant pipelines. DLP provides pre-built detectors for common PII types such as names, email addresses, phone numbers, social security numbers, and financial data. It supports transformations including 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, reducing operational overhead and ensuring compliance. Audit logs provide traceability, documenting how PII was detected and transformed. DLP scales to petabyte-level datasets, automating protection of sensitive data and reducing compliance risk. This enables organizations to perform analytics securely without exposing sensitive customer information while maintaining operational efficiency.

B) Cloud KMS secures encryption keys but does not detect or transform PII, making it unsuitable for GDPR-compliant analytics pipelines.

C) Cloud Identity-Aware Proxy secures access to applications but does not provide automated PII detection or anonymization. It cannot ensure compliance for analytics pipelines.

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

Q174

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 detect expensive queries and trigger alerts through 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 be defined to monitor specific users, queries, or workloads. Using Cloud Functions with Pub/Sub allows near real-time cost monitoring and proactive query management. This ensures that runaway queries are detected early, preventing unexpected billing spikes and enabling teams to optimize queries efficiently. This architecture 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 efficiently handle large metadata volumes, and manual execution introduces latency, preventing near real-time alerts.

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

D) Storing metadata in Bigtable and polling for alerts increases operational complexity. Polling is inefficient and does not scale as effectively as serverless Cloud Functions + Pub/Sub.

 

Q175

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 filters and cross-tenant aggregations. Partitioning by ingestion timestamp further optimizes queries filtered by time.

This design scales to petabyte-level datasets without requiring the management of 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 automatically handles scaling, metadata management, and query optimization. This approach 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.

Q176

A company wants to build a real-time analytics pipeline for e-commerce user activity. The system must ingest millions of events per second, guarantee exactly-once processing, perform session-based and windowed aggregations, and provide low-latency dashboards for product managers and marketing 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 form a fully managed, serverless architecture suitable for high-throughput, low-latency streaming analytics, essential for e-commerce platforms. Cloud Pub/Sub can handle millions of events per second from sources like clicks, purchases, cart updates, and user interactions. It ensures durable event delivery and supports acknowledgment-based guarantees to prevent data loss during traffic spikes, such as during flash sales or Black Friday.

Dataflow provides exactly-once processing, critical for accurate metrics on revenue, user engagement, and inventory management. It supports stateful and windowed aggregations, enabling session-based analytics such as average time spent per session, conversions per user session, and total revenue per hour. Sliding or tumbling windows allow marketing teams to monitor trends and optimize campaigns dynamically. Fraud detection can be integrated using custom transformations or ML models within Dataflow, enabling near real-time anomaly detection for suspicious activity.

BigQuery serves as the analytics engine, storing both raw events and aggregated metrics. Its serverless, columnar architecture scales to petabytes of data and supports low-latency queries for dashboards and ad hoc reporting. Integration with Looker, Data Studio, or other BI tools allows stakeholders to monitor KPIs in real-time, including revenue, customer behavior, and marketing performance.

B) Cloud Storage → Dataproc → BigQuery is batch-oriented. Storing events and processing them in batches introduces latency, making near real-time dashboards impractical. Dataproc requires cluster management, and exactly-once semantics are not guaranteed, potentially resulting in duplicate or missing data.

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 execution and memory limits. Implementing exactly-once processing and windowed aggregations at scale would require complex orchestration.

D) Bigtable → Cloud Run → BigQuery can store raw events efficiently but lacks distributed stream processing and exactly-once guarantees. Windowed and session-based aggregations require additional orchestration, increasing latency and operational complexity. Dashboard queries would also be less efficient.

Q177

A company wants to store IoT telemetry data in BigQuery. Queries often filter by timestamp, device type, and region. The dataset is projected 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 scan only the relevant partitions, reducing the data scanned and lowering query costs. Clustering by device type and region organizes rows physically within each partition, optimizing query performance for common filters. This design scales efficiently to petabyte-level datasets, maintaining high performance as data grows.

Partitioned and clustered tables reduce operational complexity by avoiding the need to manage multiple tables or datasets. Streaming and batch ingestion are both supported, enabling real-time anomaly detection, predictive maintenance, and trend analysis. BigQuery automatically handles partition metadata, query optimization, and scaling of compute and storage. Queries combining timestamp, device type, and region filters run efficiently, minimizing cost and latency.

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

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

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.

Q178

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, including PII. It integrates with BigQuery, Cloud Storage, and Pub/Sub, enabling GDPR-compliant pipelines. DLP provides pre-built detectors for common PII types such as names, emails, phone numbers, social security numbers, and financial identifiers. 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 effort and ensuring compliance. Audit logs document how PII is detected and transformed, providing full traceability. DLP scales to petabyte-level datasets, automating protection of sensitive data and reducing compliance risk. This allows organizations to analyze data securely while maintaining operational efficiency.

B) Cloud KMS secures encryption keys but does not detect or transform PII. 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. It cannot ensure compliance for analytics pipelines.

D) Cloud Functions can implement custom PII detection and masking, but this approach requires development effort, is less scalable, and lacks the built-in automation of Cloud DLP.

Q179

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 detect expensive queries and trigger alerts via Pub/Sub, email, or other notification channels. This serverless approach scales automatically with query volume and requires minimal operational effort.

Custom thresholds can monitor specific users, queries, or workloads. The serverless combination of Cloud Functions and Pub/Sub allows near real-time monitoring and proactive cost management. This ensures runaway queries are detected early, preventing unexpected billing spikes and enabling teams to optimize queries efficiently. The architecture 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 large metadata volumes efficiently, and manual execution introduces latency that prevents real-time alerts.

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

D) Storing metadata in Bigtable and polling for alerts increases operational complexity. Polling is inefficient and does not scale as effectively as serverless Cloud Functions + Pub/Sub.

Q180

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 filters and cross-tenant aggregations. Partitioning by ingestion timestamp further optimizes time-based queries.

This design scales to petabyte-level datasets without requiring management of 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 automatically handles scaling, metadata management, and query optimization. This approach 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 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.