Google  Professional Data Engineer on Google Cloud Platform Exam  Dumps and Practice Test Questions Set 6 Q 101- 120

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

Q101

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

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 designed for high-throughput, real-time analytics pipelines. Cloud Pub/Sub can ingest millions of mobile app events per second, such as screen views, button clicks, in-app purchases, and feature interactions. Its ability to automatically scale ensures that bursts during app launches or promotional campaigns do not overwhelm the system, while its durable storage guarantees no data loss. Dataflow, built on Apache Beam, guarantees exactly-once processing through deduplication, checkpointing, and stateful transformations, which is critical for accurate session-level analytics. Windowed aggregations allow metrics such as average session duration, retention rates per day, and feature usage trends to be computed in near real-time. BigQuery serves as the analytics layer, storing both raw and aggregated data and providing low-latency queries for dashboards, reporting, and historical analytics. Its serverless architecture supports petabyte-scale datasets without requiring cluster management or manual scaling. This architecture reduces operational overhead while delivering reliable, real-time insights into user behavior, enabling the company to optimize app features and improve engagement.

B) Cloud Storage → Dataproc → BigQuery is a batch-oriented approach. Events must first be written to storage and then processed using Dataproc clusters. This introduces latency and makes real-time dashboards infeasible. Dataproc clusters require manual provisioning and scaling, and exactly-once processing is not inherently supported.

C) Cloud SQL → Cloud Functions → BigQuery cannot handle millions of events per second. Cloud SQL is designed for transactional workloads, and Cloud Functions have execution time and memory limits. Exactly-once processing and session-level aggregations would require complex custom orchestration, increasing operational risk and system complexity.

D) Bigtable → Cloud Run → BigQuery can store raw events efficiently but lacks distributed stream processing and exactly-once semantics. Aggregations and session analytics would require additional orchestrations, such as batching or custom scheduling, which adds complexity and increases the risk of inconsistent analytics.

Q102

A company wants to store historical IoT telemetry data in BigQuery. Queries often filter by timestamp and sensor location. The dataset is expected to grow to petabytes. Which table design is most effective for performance and cost optimization?

A) Partition by ingestion timestamp and cluster by sensor location

B) Partition by sensor location and cluster by timestamp

C) Use a single unpartitioned table

D) Create multiple tables per sensor location

Answer

A) Partition by ingestion timestamp and cluster by sensor location

Explanation

A) Partitioning by ingestion timestamp enables queries filtered by time ranges to scan only the relevant partitions, reducing data scanned and lowering query costs significantly. Clustering by sensor location organizes rows physically within each partition, optimizing queries that filter or aggregate based on sensor location. This design supports petabyte-scale datasets and ensures efficient performance even as the dataset grows. A single partitioned and clustered table simplifies operational management since only one table needs to be maintained. This approach also supports both streaming and batch ingestion, low-latency analytics for anomaly detection, predictive maintenance, trend analysis, and real-time monitoring of IoT devices. BigQuery automatically handles partition metadata, scaling, and query optimization, providing a cost-efficient, high-performance solution for large IoT deployments.

B) Partitioning by sensor location is less effective in IoT scenarios with millions of devices, creating numerous small partitions that increase metadata overhead and can degrade query performance. Clustering by timestamp alone does not optimize queries filtered by sensor location, a common access pattern.

C) A single unpartitioned table is inefficient for petabyte-scale datasets. Queries filtered by timestamp or sensor location would scan the entire table, resulting in higher cost and slower performance.

D) Creating multiple tables per sensor location increases operational complexity. Schema updates, cross-location queries, and maintenance become cumbersome. Queries across multiple tables require unions or joins, increasing complexity and reducing efficiency.

Q103

A company wants to implement a GDPR-compliant BigQuery analytics pipeline. Customer PII must be automatically detected, masked, or anonymized while still allowing analytics. Which GCP service is best suited for this requirement?

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 purpose-built for detecting, classifying, and transforming sensitive data, including PII. It integrates with BigQuery, Cloud Storage, and Pub/Sub. DLP provides pre-configured detectors for common PII such as names, emails, phone numbers, social security numbers, and credit card numbers. It supports transformations including masking, tokenization, redaction, and format-preserving encryption, enabling analytics on anonymized data while protecting sensitive information. Transformations can be applied inline during ingestion or query execution, reducing operational overhead. Audit logging provides traceability to satisfy GDPR compliance requirements, showing how sensitive data is processed and transformed. DLP scales automatically to handle petabyte-scale datasets, making it highly reliable and efficient for large-scale GDPR-compliant analytics pipelines.

B) Cloud KMS manages encryption keys and protects data at rest but does not detect, classify, or transform PII. It cannot provide the granular data masking or anonymization needed for GDPR compliance in analytics workflows.

C) Cloud Identity-Aware Proxy (IAP) secures access to applications but does not perform PII detection or anonymization for analytics data, so it cannot ensure GDPR compliance for datasets in BigQuery.

D) Cloud Functions can implement custom PII detection and masking logic, but they require significant development and maintenance effort. Functions do not have built-in detection and transformation capabilities, making them less reliable and less scalable than Cloud DLP.

Q104

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

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 queries, including execution runtime, bytes processed, and cost. Cloud Functions can periodically query these tables to detect high-cost queries and trigger alerts via Pub/Sub, email, or other notification mechanisms. This architecture is fully serverless, scales automatically with query volume, and requires minimal maintenance. It enables near real-time cost monitoring, allowing operational teams to quickly react to expensive queries and prevent budget overruns. Custom thresholds and alerting rules can be configured to monitor specific query types, patterns, or resource usage. The combination of Cloud Functions and Pub/Sub ensures reliability, scalability, and low operational overhead. Additionally, serverless architecture removes the need to manage compute resources or clusters, providing a highly efficient cost monitoring system.

B) Querying BigQuery from Cloud SQL manually is not scalable. Cloud SQL cannot efficiently process large volumes of metadata and manual queries introduce latency, making real-time alerting infeasible.

C) Exporting logs to Cloud Storage and processing offline introduces significant delays, preventing near real-time monitoring of query costs and limiting proactive cost control.

D) Storing query metadata in Bigtable and polling for alerts increases operational complexity. While Bigtable supports high-throughput writes, the polling and alerting orchestration adds overhead and complexity compared to serverless Cloud Functions + Pub/Sub.

 

Q105

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 optimal?

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 best combination of scalability, performance, and operational simplicity. Clustering organizes rows by tenant, improving query performance for filtering and aggregation. Partitioning by ingestion timestamp further optimizes time-based queries. This architecture supports petabyte-scale datasets without creating thousands of separate tables or projects, significantly reducing operational overhead. Cross-tenant queries are straightforward, requiring only filtering or grouping by tenant_id. Centralized schema evolution ensures consistency, and BigQuery’s serverless architecture automatically handles scaling, metadata management, and query optimization. This design minimizes operational complexity while delivering high performance and cost efficiency for large-scale multi-tenant SaaS analytics workloads.

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

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

D) Multiple unpartitioned tables per tenant increase operational complexity. Queries across many tables require unions and are error-prone, reducing efficiency and maintainability.

Q106

A company wants to build a real-time analytics pipeline for financial transactions. The system must handle millions of events per second, guarantee exactly-once processing, support windowed aggregations for fraud detection, and provide low-latency dashboards. 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 real-time analytics of financial transactions. Cloud Pub/Sub can ingest millions of transactions per second with automatic scaling to handle spikes during market events, promotions, or peak trading hours. It ensures reliable message delivery and provides durable storage to prevent data loss. Dataflow, based on Apache Beam, guarantees exactly-once processing through deduplication, checkpointing, and stateful processing, which is crucial for preventing duplicate transactions and ensuring accurate fraud detection. Windowed aggregations allow computations such as transaction volume per minute, average transaction value per account, and detection of anomalous patterns indicative of fraudulent activity. BigQuery stores both raw and aggregated data, enabling low-latency queries for dashboards, regulatory reporting, and historical trend analysis. Its serverless, columnar architecture scales seamlessly to petabytes of data without manual cluster management, ensuring high performance and operational simplicity. This architecture supports robust analytics for fraud detection, transaction monitoring, and real-time business intelligence.

B) Cloud Storage → Dataproc → BigQuery is batch-oriented, introducing latency unsuitable for real-time fraud detection. Dataproc clusters require manual scaling and management, and exactly-once processing is not guaranteed.

C) Cloud SQL → Cloud Functions → BigQuery cannot process millions of events per second. Cloud SQL is designed for transactional workloads, and Cloud Functions have execution and memory limits, making exactly-once processing and windowed aggregations difficult to implement.

D) Bigtable → Cloud Run → BigQuery can store raw transactions efficiently, but Cloud Run lacks distributed stream processing and exactly-once semantics. Aggregations and low-latency dashboards require additional orchestration, increasing complexity and risk.

Q107

A company wants to store IoT telemetry data in BigQuery for historical analysis. Queries often filter by timestamp and device type, and the dataset is expected to grow to petabytes. Which table design is most efficient?

A) Partition by ingestion timestamp and cluster by device type

B) Partition by device type and cluster by timestamp

C) Use a single unpartitioned table

D) Create multiple tables per device type

Answer

A) Partition by ingestion timestamp and cluster by device type

Explanation

A) Partitioning by ingestion timestamp ensures queries filtered by time ranges scan only relevant partitions, reducing data scanned and lowering costs. Clustering by device type physically organizes rows within partitions, optimizing queries that filter or aggregate by device type. This design supports petabyte-scale datasets, maintaining high performance even as the dataset grows. A single partitioned and clustered table reduces operational complexity, as only one table is maintained. Partitioned and clustered tables also support low-latency analytics, anomaly detection, trend analysis, and predictive maintenance for IoT devices. BigQuery automatically manages partition metadata, scaling, and query optimization, making this approach cost-efficient and high-performance.

B) Partitioning by device type creates numerous small partitions, increasing metadata overhead and reducing query efficiency. Clustering by timestamp alone does not optimize queries filtered by device type, which is common in IoT analytics.

C) A single unpartitioned table is inefficient for petabyte-scale data. Queries filtered by timestamp or device type would scan the entire dataset, resulting in high costs and slower performance.

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

Q108

A company needs 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 automatically detect, classify, and transform sensitive data such as PII. It integrates with BigQuery, Cloud Storage, and Pub/Sub, and comes with pre-built detectors for names, emails, phone numbers, social security numbers, and financial information. DLP supports transformations including masking, tokenization, redaction, and format-preserving encryption, enabling analytics on anonymized datasets while protecting sensitive data. Transformations can occur during ingestion or query execution, minimizing operational overhead. Audit logs provide traceability for GDPR compliance, showing how sensitive data is handled. DLP scales to petabyte-level datasets without requiring additional infrastructure management, ensuring efficiency and reliability for GDPR-compliant analytics pipelines.

B) Cloud KMS manages encryption keys and secures data at rest but does not detect, classify, or transform PII. It cannot enforce GDPR-compliant transformations for analytics workflows.

C) Cloud Identity-Aware Proxy (IAP) secures access to applications but does not detect, mask, or anonymize sensitive data, making it unsuitable for GDPR analytics compliance.

D) Cloud Functions can implement custom detection and masking logic but require development, maintenance, and testing. Functions lack built-in PII detection and transformation capabilities, making them less reliable than Cloud DLP for GDPR compliance.

Q109

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 contain metadata about BigQuery jobs, including query runtime, bytes processed, and cost. Cloud Functions can query these tables periodically to identify 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 maintenance. It enables near real-time monitoring of query costs, allowing teams to take immediate action to control spending. Custom thresholds and rules can be defined to monitor specific query patterns or resource consumption. The combination of Cloud Functions and Pub/Sub ensures reliable, scalable, and low-maintenance alerting without the need for managing servers or clusters. This approach is cost-effective, efficient, and aligns with modern serverless design principles.

B) Querying BigQuery from Cloud SQL manually is not scalable. Cloud SQL cannot handle metadata from large volumes of queries efficiently, and manual processing introduces latency, preventing near real-time alerts.

C) Exporting logs to Cloud Storage and processing offline introduces delays, making it unsuitable for real-time monitoring and alerting.

D) Storing query metadata in Bigtable and polling for alerts increases operational complexity. While Bigtable is high-throughput, the manual polling and alert orchestration is more complex than using serverless Cloud Functions and Pub/Sub.

Q110

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 efficient design for multi-tenant SaaS analytics on BigQuery. Clustering organizes rows by tenant, improving query performance for filtering and aggregations. Partitioning by ingestion timestamp further optimizes queries filtered by time ranges. This design supports petabyte-scale datasets without creating thousands of separate tables or projects, significantly reducing operational overhead. Cross-tenant queries are straightforward and efficient, requiring only filtering or grouping by tenant_id. Centralized schema evolution ensures consistency, and BigQuery’s serverless architecture automatically manages scaling, metadata, and query optimization. This approach delivers high performance, cost efficiency, and operational simplicity, making it ideal for multi-tenant SaaS analytics.

B) Separate BigQuery projects per tenant increases operational complexity, including billing, IAM management, and schema maintenance. 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 many tables require unions and are error-prone, reducing efficiency and maintainability.

Q111

A company wants to build a real-time analytics pipeline for e-commerce clickstream data. The system must ingest millions of events per second, guarantee exactly-once processing, perform session-based windowed aggregations, and support low-latency dashboards for user engagement 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 fully managed, serverless architecture ideal for real-time e-commerce clickstream analytics. Cloud Pub/Sub can handle millions of clickstream events per second, such as page views, product clicks, cart additions, and purchases. Its automatic scaling ensures that traffic spikes during sales or promotional events do not lead to data loss. Cloud Pub/Sub’s durable message storage ensures reliable delivery to downstream consumers. Dataflow, built on Apache Beam, guarantees exactly-once processing through deduplication, checkpointing, and stateful transformations, which is critical for accurate session-level metrics such as average session duration, cart abandonment rates, and funnel conversion rates. Windowed aggregations allow computation of metrics in fixed or sliding time windows, providing insights into short-term user behavior and enabling timely marketing interventions. BigQuery serves as the analytics layer, storing raw and aggregated data, and providing low-latency queries for dashboards, ad hoc reporting, and trend analysis. Its serverless, columnar architecture scales seamlessly to petabytes, allowing for high-performance analytics without manual cluster management. This combination reduces operational overhead while providing robust, real-time insights into user behavior, conversion optimization, and marketing effectiveness.

B) Cloud Storage → Dataproc → BigQuery is batch-oriented. Events must first be written to storage and processed in batches, introducing latency that makes real-time dashboards impractical. Dataproc clusters require manual management, and exactly-once semantics are not guaranteed.

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

D) Bigtable → Cloud Run → BigQuery can store raw clickstream data efficiently but lacks distributed stream processing and exactly-once semantics. Aggregations and low-latency dashboards would require additional orchestration and custom batching, increasing complexity and the potential for errors.

Q112

A company wants to store historical IoT telemetry data in BigQuery. Queries frequently filter by timestamp and device region, and 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 region

B) Partition by device region and cluster by timestamp

C) Use a single unpartitioned table

D) Create multiple tables per device region

Answer

A) Partition by ingestion timestamp and cluster by device region

Explanation

A) Partitioning by ingestion timestamp ensures that queries filtered by time ranges scan only relevant partitions, which reduces the amount of data scanned and lowers query costs. Clustering by device region physically organizes rows within partitions, optimizing queries that filter or aggregate by region. This design scales efficiently for petabyte-scale datasets, providing consistent query performance even as data grows. Using a single partitioned and clustered table simplifies operational management by eliminating the need to maintain multiple tables or datasets. This approach also supports streaming ingestion and batch processing, enabling real-time analytics and trend analysis. Partitioned and clustered tables are ideal for anomaly detection, predictive maintenance, and monitoring IoT devices across regions. BigQuery automatically handles partition metadata, scaling, and query optimization, providing high performance and cost efficiency.

B) Partitioning by device region creates many small partitions, which increases metadata overhead and reduces performance for queries filtered by timestamp. Clustering by timestamp alone does not optimize queries filtered by region, a common access pattern.

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

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

Q113

A company wants to implement a GDPR-compliant analytics pipeline in BigQuery. Sensitive 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 specifically designed to detect, classify, and transform sensitive data such as PII. It integrates seamlessly with BigQuery, Cloud Storage, and Pub/Sub. 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 such as masking, tokenization, redaction, and format-preserving encryption, enabling analytics on anonymized datasets without exposing sensitive information. Transformations can be applied inline during ingestion or query execution, reducing operational overhead and ensuring compliance with GDPR requirements. Audit logs provide traceability, documenting how PII is detected, transformed, and processed. DLP can scale to handle petabyte-level datasets, providing reliable, automated protection for large-scale analytics pipelines.

B) Cloud KMS manages encryption keys to secure data at rest but does not detect, classify, or anonymize PII. It is insufficient for GDPR analytics requirements.

C) Cloud Identity-Aware Proxy (IAP) secures access to applications but does not detect or anonymize PII, so it cannot enforce GDPR compliance for BigQuery analytics.

D) Cloud Functions can implement custom PII detection and masking logic, but require significant development and maintenance. Functions lack built-in PII detection and transformation capabilities, making them less reliable and less scalable than Cloud DLP.

Q114

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 queries, including execution runtime, bytes processed, and cost. Cloud Functions can periodically query these tables 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 maintenance. It allows near real-time monitoring of query costs, enabling proactive cost management and immediate response to high-cost queries. Custom thresholds and alerting rules can be defined to track specific query patterns, users, or workloads. The combination of serverless Cloud Functions and Pub/Sub ensures reliable, scalable, and low-maintenance alerting without managing infrastructure. This approach aligns with best practices for cost monitoring in large-scale BigQuery environments.

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

C) Exporting logs to Cloud Storage and processing offline introduces delays, preventing real-time alerts and reducing responsiveness to expensive queries.

D) Storing metadata in Bigtable and polling for alerts increases operational complexity. While Bigtable supports high-throughput writes, polling and orchestration adds unnecessary complexity compared to serverless Cloud Functions + Pub/Sub.

Q115

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 optimal combination of scalability, performance, and operational simplicity. Clustering organizes rows by tenant, improving query performance for filtering and aggregation. Partitioning by ingestion timestamp further optimizes queries filtered by date ranges. This design scales to petabyte-level datasets without creating thousands of separate tables or projects, reducing operational overhead. Cross-tenant queries are straightforward and efficient, requiring only filtering or grouping by tenant_id. Centralized schema evolution ensures consistency, and BigQuery’s serverless architecture automatically manages scaling, metadata, and query optimization. This approach ensures high performance, cost efficiency, and operational simplicity, making it ideal for large-scale multi-tenant SaaS analytics environments.

B) Separate BigQuery projects per tenant introduces significant operational overhead, including billing management, IAM configuration, and schema maintenance. Cross-tenant queries are cumbersome and inefficient.

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

D) Multiple unpartitioned tables per tenant increase operational overhead. Queries across many tables require unions and are error-prone, reducing efficiency and maintainability.

Q116

A company wants to build a real-time analytics pipeline for streaming telemetry data from connected vehicles. 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 fleet management. 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 highly scalable, fully managed, serverless architecture ideally suited for real-time analytics of vehicle telemetry data. Cloud Pub/Sub can ingest millions of events per second, including GPS coordinates, speed, engine performance metrics, and sensor data. Its auto-scaling capabilities ensure that bursts of events from high-traffic regions or fleet updates are handled without dropping messages. Pub/Sub also provides durable message storage with acknowledgment mechanisms, ensuring reliable delivery to downstream processing pipelines.

Dataflow, built on Apache Beam, provides exactly-once processing semantics through checkpointing, deduplication, and stateful transformations, which is crucial for telemetry analytics where double counting could distort performance metrics, fleet utilization rates, or maintenance schedules. Windowed aggregations allow the computation of metrics over fixed or sliding windows, such as average speed per vehicle per minute, total fuel consumption per hour, or detecting anomalies in vehicle behavior in real time. Session-based aggregations enable fleet managers to analyze trips, idling periods, and driver behavior efficiently.

BigQuery acts as the analytics layer, storing both raw telemetry data and aggregated metrics. Its serverless, columnar architecture can scale seamlessly to petabyte-scale datasets and supports low-latency queries for dashboards, reporting, and historical analytics. Fleet managers can run ad hoc queries to analyze patterns, detect maintenance needs, or optimize routes. BigQuery’s integration with BI tools like Looker or Data Studio allows dashboards to refresh in near real time, enabling operational decision-making.

B) Cloud Storage → Dataproc → BigQuery is batch-oriented. Ingested telemetry events would be stored in Cloud Storage and processed in batches via Dataproc, introducing latency unsuitable for real-time fleet monitoring. Dataproc clusters require manual scaling and management, and exactly-once processing is not guaranteed, which could result in inconsistent metrics for fleet management and safety-critical analytics.

C) Cloud SQL → Cloud Functions → BigQuery is not suitable for millions of events per second. Cloud SQL is optimized for transactional workloads and cannot handle high-throughput streaming data efficiently. Cloud Functions have execution and memory limits, and implementing exactly-once processing and windowed aggregations at scale would require complex custom orchestration, increasing operational risk and maintenance overhead.

D) Bigtable → Cloud Run → BigQuery can store raw telemetry events efficiently but lacks distributed stream processing capabilities and exactly-once semantics. Aggregation and session analysis would require additional orchestration layers or batch processing, increasing complexity, latency, and the potential for processing errors, which makes it suboptimal for real-time vehicle analytics.

Q117

A company wants to store historical IoT telemetry data in BigQuery. Queries frequently filter by timestamp, device type, and region. The dataset is expected to grow to multiple petabytes. Which table design is most efficient and cost-effective?

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 ranges scan only the relevant partitions, minimizing the amount of data scanned and reducing costs. Clustering by device type and region physically organizes the rows within each partition, which optimizes queries that filter or aggregate by these fields. This design scales efficiently for multi-petabyte datasets, providing consistent query performance even as the dataset grows. Using a single partitioned and clustered table reduces operational complexity because only one table must be maintained, rather than multiple tables or datasets.

This approach supports both streaming and batch ingestion, enabling real-time analytics, anomaly detection, and predictive maintenance. Partitioned and clustered tables allow low-latency queries for dashboards and ad hoc queries. BigQuery automatically manages partition metadata, optimizes query execution, and ensures high performance. This combination is particularly suitable for IoT telemetry scenarios where multiple dimensions such as device type, region, and timestamp are commonly used in queries.

B) Partitioning by device type creates many small partitions, which increases metadata overhead and reduces query performance. Clustering by timestamp alone does not optimize for common access patterns filtering by region.

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

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

Q118

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 purpose-built for detecting, classifying, and transforming sensitive data such as PII. It integrates with BigQuery, Cloud Storage, and Pub/Sub, allowing organizations to process and store large datasets securely. DLP provides pre-built detectors for common PII types such as names, emails, phone numbers, social security numbers, credit card information, and more. It supports transformations including masking, tokenization, redaction, and format-preserving encryption, enabling analytics on anonymized datasets without exposing sensitive information. Transformations can be applied inline during ingestion or during query execution, reducing operational overhead and ensuring GDPR compliance. Audit logs provide traceability, documenting when and how PII is detected and transformed.

Cloud DLP scales to handle petabyte-level datasets, making it suitable for large-scale analytics pipelines. It ensures consistent application of PII transformations and reduces operational risk compared to custom implementations. DLP also provides flexible policies, enabling organizations to meet compliance requirements while still supporting analytical queries, aggregation, and reporting.

B) Cloud KMS secures encryption keys for data at rest but does not detect, classify, or anonymize PII, so it cannot ensure GDPR compliance in analytics workflows.

C) Cloud Identity-Aware Proxy (IAP) secures access to applications but does not provide detection or anonymization of PII, making it insufficient for GDPR compliance in BigQuery analytics.

D) Cloud Functions can implement custom PII detection and masking logic, but this requires significant development effort and lacks the built-in, automated detection, and transformation capabilities that DLP provides. It is less scalable and more error-prone for large datasets.

Q119

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 suitable?

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 queries, including query 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 notification mechanisms. This approach is fully serverless, scales automatically with query volume, and requires minimal maintenance. It provides near real-time monitoring of query costs, allowing organizations to proactively manage spending and address inefficient queries promptly.

Custom thresholds and alerting rules can be defined to monitor specific users, query patterns, or workloads. Using serverless Cloud Functions and Pub/Sub reduces operational overhead, as there is no need to manage infrastructure. This architecture is highly cost-efficient and aligns with best practices for monitoring and alerting in large-scale BigQuery environments.

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

C) Exporting logs to Cloud Storage and processing offline introduces significant delay, making near real-time alerts impractical.

D) Storing metadata in Bigtable and polling for alerts adds operational complexity. While Bigtable can handle high-throughput writes, the need for polling and orchestration increases complexity compared to serverless Cloud Functions + Pub/Sub.

Q120

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 optimal design for multi-tenant SaaS analytics on BigQuery. Clustering organizes rows by tenant, improving query performance for filtering and aggregation. Partitioning by ingestion timestamp further optimizes queries filtered by date ranges. This design scales efficiently to petabyte-level datasets without creating multiple tables or projects, reducing operational complexity. Cross-tenant queries are straightforward, requiring only filtering or grouping by tenant_id. Centralized schema evolution ensures consistency, while BigQuery’s serverless architecture automatically handles scaling, metadata management, and query optimization. This approach delivers high performance, cost efficiency, and operational simplicity, making it ideal for large-scale multi-tenant SaaS analytics.

B) Separate BigQuery projects per tenant adds operational overhead, including billing, IAM management, and schema maintenance. Cross-tenant queries are 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 complexity. Queries across many tables require unions and are error-prone, reducing efficiency and maintainability.