Google  Professional Data Engineer on Google Cloud Platform Exam  Dumps and Practice Test Questions Set 4 Q 61- 80

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Q61

A company wants to build a real-time analytics pipeline for online video streaming events. The pipeline must ingest millions of events per second, guarantee exactly-once processing, perform windowed aggregations, and support near real-time 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 fully managed, serverless pipeline suitable for high-throughput real-time analytics. Cloud Pub/Sub acts as a durable, scalable ingestion system that can handle millions of events per second from video streaming clients. It buffers spikes in traffic automatically, preventing data loss during peak viewing hours or global live events. Dataflow, built on Apache Beam, processes events in real-time and supports exactly-once processing through deduplication, checkpointing, and stateful transformations. Windowed aggregations can be applied to calculate metrics like concurrent viewers per time window, session duration averages, and ad impressions. BigQuery serves as the analytics layer for storing raw and aggregated data, enabling low-latency SQL queries for dashboards, reporting, and ad hoc analysis. This architecture scales automatically without manual cluster management, provides high reliability, and minimizes operational overhead, making it ideal for streaming video analytics.

B) Cloud Storage → Dataproc → BigQuery is batch-oriented and introduces latency unsuitable for near real-time dashboards. Events must be written to Cloud Storage and processed in batches with Dataproc, which delays insights. Exactly-once semantics are not inherent, and scaling clusters for peak traffic requires operational intervention.

C) Cloud SQL → Cloud Functions → BigQuery cannot handle millions of streaming events per second. Cloud SQL is designed for transactional workloads, and Cloud Functions are stateless with execution time limits. Achieving exactly-once processing and windowed aggregations would require complex orchestration and scaling, increasing complexity and risk.

D) Bigtable → Cloud Run → BigQuery could store raw events efficiently, but Cloud Run is stateless and does not provide distributed stream processing or exactly-once semantics. Implementing windowed aggregations, deduplication, and low-latency dashboards would require additional orchestration, making this architecture less practical.

Q62

A company wants to store historical IoT sensor data in BigQuery. Queries often filter by timestamp and device type, and the dataset will grow to petabytes. Which table design optimizes performance and cost?

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 reduces the volume of data scanned for queries filtered by date, lowering costs. Clustering by device type organizes data physically, optimizing queries that filter or aggregate by device type. This design scales to petabyte-level datasets and supports both streaming and batch ingestion. Operational management is simplified because only one table needs to be maintained. Partitioned and clustered tables in BigQuery enable low-latency analytics for IoT use cases such as anomaly detection, trend analysis, and predictive modeling. BigQuery automatically handles partition maintenance, metadata management, and scaling, providing a cost-efficient and high-performance solution for time-series IoT data.

B) Partitioning by device type is inefficient because large-scale IoT deployments may have millions of devices, creating numerous small partitions. Clustering by timestamp alone does not optimize queries filtered by device type.

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

D) Multiple tables per device type increase operational complexity. Schema updates, cross-device queries, and maintenance become cumbersome. Querying across multiple tables requires unions, reducing performance and increasing cost.

Q63

A company wants to implement a GDPR-compliant analytics pipeline for customer data in BigQuery. Sensitive data must be automatically detected, anonymized, or masked, and analytics should be possible on the transformed data. Which GCP service is best suited?

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 and manage sensitive data such as personally identifiable information (PII). It automatically discovers, classifies, and transforms PII in BigQuery, Cloud Storage, and Pub/Sub using pre-built detectors for names, emails, phone numbers, credit cards, and other sensitive information. DLP provides transformations including masking, tokenization, redaction, and format-preserving encryption, allowing analytics on anonymized data without exposing raw PII. DLP supports structured and semi-structured data, integrates directly with BigQuery, and allows inline anonymization during ingestion or queries. Audit logging ensures compliance reporting and traceability, which is critical for GDPR. This reduces operational complexity, ensures legal compliance, and allows analysts to safely perform analytics on transformed sensitive data.

B) Cloud KMS manages encryption keys and secures data at rest but does not provide PII detection, masking, or transformation for analytics purposes.

C) Cloud Identity-Aware Proxy (IAP) controls access to applications but does not detect, mask, or anonymize sensitive data for analytics.

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

 

Q64

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

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) BigQuery INFORMATION_SCHEMA tables provide metadata about queries, including runtime, bytes processed, and job costs. Cloud Functions can periodically query these tables and trigger alerts for queries exceeding cost thresholds. Alerts can be published via Pub/Sub, email, or other notification channels. This approach is fully serverless, scales automatically with query volume, and requires minimal operational overhead. It enables near real-time detection of costly queries, allowing teams to act promptly to control costs. Thresholds and alerting rules can be customized for specific use cases, providing operational visibility and proactive cost management.

B) Querying BigQuery from Cloud SQL manually is not scalable. Cloud SQL is not optimized for analyzing query metadata at scale, and alerts would be delayed due to manual processing.

C) Exporting logs to Cloud Storage and processing offline introduces latency, preventing near real-time alerting.

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

Q65

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

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 optimal scalability, performance, and operational simplicity for multi-tenant SaaS analytics. Clustering organizes rows by tenant, improving query performance for filtering and aggregation. Partitioning by ingestion time further optimizes time-based queries. This design supports petabyte-scale datasets without creating thousands of separate tables or projects. Cross-tenant queries are simple and efficient, requiring only a filter or GROUP BY on tenant_id. Schema evolution is centralized, and BigQuery’s serverless architecture automatically manages scaling, metadata, and query optimization. This reduces operational overhead while ensuring high performance and cost efficiency for large-scale multi-tenant analytics.

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

C) Storing data in Cloud SQL and replicating to BigQuery adds latency, operational overhead, and is not suitable for petabyte-scale analytics.

D) Multiple unpartitioned tables per tenant increase operational complexity. Managing thousands of tables, updating schemas, and performing cross-tenant queries through unions is error-prone and inefficient.

Q66

A company wants to build a serverless real-time analytics pipeline for stock market data. The system must handle millions of events per second, guarantee exactly-once processing, perform 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 form a fully managed, serverless pipeline optimized for high-throughput real-time analytics. Cloud Pub/Sub can ingest millions of stock market events per second, providing durable storage and automatic scaling to handle bursts during market openings or volatility spikes. Dataflow, built on Apache Beam, ensures exactly-once processing through stateful operations, checkpointing, and deduplication, which is essential for accurate stock trade and price analysis. Windowed aggregations allow calculations like per-second trading volume, moving averages, and anomaly detection in near real-time. BigQuery stores raw and aggregated data for dashboards, ad hoc queries, and historical trend analysis. Its columnar storage, serverless architecture, and automatic scaling make it suitable for petabyte-scale datasets, enabling low-latency analytics without operational overhead. This architecture is ideal for financial data streams where latency, scalability, and accuracy are critical.

B) Cloud Storage → Dataproc → BigQuery is batch-oriented and introduces significant latency, making real-time analysis and dashboards impractical. Cluster management adds operational complexity, and exactly-once semantics are not guaranteed.

C) Cloud SQL → Cloud Functions → BigQuery cannot handle millions of streaming events per second. Cloud SQL is transactional and would become a bottleneck, while Cloud Functions are stateless and have execution limits, making exactly-once processing and windowed aggregations challenging.

D) Bigtable → Cloud Run → BigQuery could store raw events efficiently, but Cloud Run lacks distributed stream processing and exactly-once semantics. Implementing windowed aggregations and low-latency dashboards would require additional orchestration, increasing complexity.

Q67

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

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 that queries scanning a specific time range only read relevant partitions, reducing data scanned and lowering cost. Clustering by device type organizes rows physically within each partition, optimizing performance for queries filtered by device type. This combination supports petabyte-scale datasets and simplifies operational management, since only a single table requires maintenance. Partitioned and clustered tables in BigQuery enable low-latency queries for analytics such as trend analysis, anomaly detection, and predictive modeling. BigQuery automatically manages partition metadata, scaling, and query optimization, making this approach highly cost-efficient and performant for large-scale IoT telemetry data.

B) Partitioning by device type is inefficient for large-scale IoT deployments with potentially millions of devices, creating numerous small partitions that increase metadata overhead. Clustering by timestamp alone does not optimize common query patterns that filter by device type.

C) A single unpartitioned table is not suitable for petabyte-scale datasets. Queries filtering by timestamp or device type would scan the entire table, leading to higher costs and slower performance.

D) Multiple tables per device type increase operational complexity. Schema changes, cross-device queries, and maintenance become cumbersome. Querying across multiple tables requires unions, increasing complexity and reducing performance.

Q68

A company wants to ensure GDPR compliance for analytics on BigQuery datasets containing sensitive customer information. The system must automatically detect PII, anonymize or mask it, and allow analytics on the transformed data. Which service is best suited?

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) provides automated detection, classification, and transformation of sensitive data, including personally identifiable information (PII), in BigQuery, Cloud Storage, and Pub/Sub. Pre-built detectors identify common PII types such as names, emails, social security numbers, and credit card numbers. DLP enables transformations like masking, tokenization, redaction, and format-preserving encryption, allowing analysts to perform analytics on anonymized datasets while maintaining compliance. Inline transformations during ingestion or query execution reduce operational overhead, while audit logs provide traceability for compliance reporting. DLP is scalable, serverless, and designed to handle large datasets, making it ideal for GDPR-compliant analytics pipelines.

B) Cloud KMS manages encryption keys and protects data at rest but does not detect or transform PII for analytics, making it insufficient for GDPR compliance.

C) Cloud Identity-Aware Proxy (IAP) secures application access but does not detect or transform sensitive data for analytics purposes.

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

Q69

A company wants to monitor BigQuery query costs and trigger near real-time alerts for expensive queries. The solution must be serverless and scalable. 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 in BigQuery provide metadata on query execution, including runtime, bytes processed, and cost. Cloud Functions can query these tables on a schedule to detect expensive queries. Alerts can be published through Pub/Sub, email, or other channels. This solution is fully serverless, automatically scales with query volume, and requires minimal operational management. It enables near real-time monitoring and cost control by detecting anomalous queries promptly. Customizable thresholds allow organizations to define what constitutes an expensive query and trigger alerts proactively, helping optimize cost management and operational efficiency.

B) Querying BigQuery from Cloud SQL manually is not scalable. Cloud SQL cannot efficiently store or analyze large volumes of query metadata, and manual processes delay alerting.

C) Exporting logs to Cloud Storage for offline processing introduces latency and prevents near real-time alerting.

D) Storing query metadata in Bigtable and polling for alerts adds operational complexity. While Bigtable can handle high-throughput ingestion, managing polling and alert logic introduces unnecessary overhead compared to serverless Cloud Functions + Pub/Sub.

Q70

A company wants to build a multi-tenant SaaS analytics solution on BigQuery. Each tenant’s data must be 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 balance of scalability, performance, and operational simplicity. Clustering physically organizes rows by tenant, improving filtering and aggregation performance. Partitioning by ingestion time further optimizes time-based queries. This approach supports petabyte-scale datasets without creating thousands of separate tables or projects. Cross-tenant queries are straightforward and efficient, requiring only filtering or grouping on tenant_id. Schema evolution is centralized, and BigQuery’s serverless architecture handles scaling, metadata management, and query optimization automatically. This reduces operational overhead while ensuring high performance and cost efficiency.

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

C) Storing data in Cloud SQL and replicating to BigQuery adds latency and operational overhead and is not suitable for petabyte-scale analytics.

D) Multiple unpartitioned tables per tenant increase operational complexity. Managing thousands of tables, performing cross-tenant queries through unions, and updating schemas is error-prone and inefficient.

Q71

A company wants to build a real-time analytics pipeline for mobile app events. The pipeline must ingest millions of events per second, provide exactly-once processing, perform session-based windowed aggregations, and support near real-time 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 form a fully managed, serverless architecture optimized for high-throughput streaming analytics. Cloud Pub/Sub ingests millions of mobile app events per second, providing automatic scaling, durable message storage, and buffering to handle traffic spikes, which are common during app updates, campaigns, or viral trends. Dataflow, built on Apache Beam, processes these events in real-time and ensures exactly-once processing through deduplication, checkpointing, and stateful operations. Windowed aggregations enable session-based metrics such as average session duration, user engagement per time window, and event funnels. BigQuery stores raw and aggregated data for dashboards, ad hoc queries, and historical analysis. Its columnar, serverless architecture scales automatically to petabyte-level datasets, providing low-latency analytics without operational overhead. This architecture is ideal for mobile analytics where latency, reliability, and scalability are critical.

B) Cloud Storage → Dataproc → BigQuery is batch-oriented, introducing latency that prevents near real-time dashboards. Dataproc clusters require manual management, and exactly-once semantics are not guaranteed. Scaling for high-throughput mobile events adds operational complexity.

C) Cloud SQL → Cloud Functions → BigQuery cannot handle millions of streaming events per second. Cloud SQL is transactional, and Cloud Functions are stateless with execution limits, making exactly-once processing and windowed aggregations challenging. Additional orchestration is required, increasing complexity.

D) Bigtable → Cloud Run → BigQuery can store raw events efficiently, but Cloud Run lacks distributed stream processing and exactly-once semantics. Windowed aggregations and near real-time dashboards would require additional orchestration, making this approach less practical.

Q72

A company wants to store historical IoT telemetry in BigQuery. Queries often filter by timestamp and device type, and the dataset is expected to grow to petabytes. Which table design provides optimal performance and cost-efficiency?

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 allows queries filtered by date to scan only relevant partitions, significantly reducing scanned data and lowering costs. Clustering by device type organizes rows physically within partitions, optimizing query performance for filtering or aggregation by device type. This design supports petabyte-scale datasets and simplifies operational management, as only one table is maintained. Partitioned and clustered tables enable low-latency analytics for IoT use cases like anomaly detection, trend analysis, and predictive modeling. BigQuery automatically manages partition metadata, scaling, and query optimization, providing a cost-efficient and high-performance solution for large-scale IoT telemetry data.

B) Partitioning by device type is inefficient for large-scale IoT deployments with millions of devices, creating many small partitions that increase metadata overhead. Clustering by timestamp alone does not optimize common query patterns filtered by device type.

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

D) Multiple tables per device type increase operational complexity. Schema updates, cross-device queries, and maintenance become cumbersome. Querying across multiple tables requires unions, increasing complexity and reducing performance.

 

Q73

A company wants to ensure GDPR compliance for BigQuery analytics on sensitive customer data. PII must be automatically detected, masked, or anonymized, while still allowing analytics. Which GCP service is best suited?

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 discovering, classifying, and transforming sensitive data, including personally identifiable information (PII). It works on BigQuery, Cloud Storage, and Pub/Sub. DLP provides pre-built detectors for PII such as names, emails, phone numbers, credit card numbers, and social security numbers. It supports transformations like masking, tokenization, redaction, and format-preserving encryption, allowing analysts to query anonymized data safely. Inline transformations during ingestion or query execution reduce operational complexity, while audit logging ensures traceability for GDPR compliance. DLP is serverless, scalable, and capable of handling large datasets, making it the ideal choice for GDPR-compliant analytics pipelines.

B) Cloud KMS manages encryption keys and secures data at rest but does not detect, classify, or transform PII for analytics purposes, making it insufficient for GDPR compliance.

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

D) Cloud Functions can implement custom detection and anonymization logic but require significant development and maintenance. Functions lack built-in PII detection, making them less reliable for GDPR compliance compared to Cloud DLP.

Q74

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 queries, including bytes processed, runtime, 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 solution is serverless, scales automatically with query volume, and requires minimal operational overhead. It enables near real-time cost monitoring, allowing teams to proactively address expensive queries. Thresholds and alert rules can be customized to suit organizational needs, ensuring efficient cost management and operational visibility without manual intervention.

B) Querying BigQuery from Cloud SQL manually is not scalable. Cloud SQL is not optimized for storing and analyzing query metadata at large scale, and manual processing would introduce latency and complexity.

C) Exporting logs to Cloud Storage and processing offline introduces significant delay, preventing near real-time alerting and cost control.

D) Storing metadata in Bigtable and polling for alerts adds operational complexity. While Bigtable supports high-throughput writes, implementing polling and alert logic introduces unnecessary management overhead compared to a serverless Cloud Functions + Pub/Sub approach.

Q75

A company wants to implement a multi-tenant SaaS analytics solution on BigQuery. Each tenant’s data must be 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 efficiency for filtering and aggregation. Partitioning by ingestion time further optimizes time-based queries. This design supports petabyte-scale datasets without creating thousands of separate tables or projects. Cross-tenant queries are straightforward, requiring only filtering or grouping on tenant_id. Schema evolution is centralized, and BigQuery’s serverless architecture automatically manages scaling, metadata, and query optimization. This approach minimizes operational overhead while maintaining high performance and cost efficiency.

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

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

D) Multiple unpartitioned tables per tenant increase operational complexity. Managing thousands of tables and performing cross-tenant queries through unions is error-prone and inefficient.

Q76

A company wants to build a real-time analytics pipeline for financial transactions. The pipeline must ingest millions of events per second, guarantee exactly-once processing, perform windowed aggregations, and provide low-latency dashboards for fraud detection. 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 form a fully managed, serverless pipeline optimized for high-throughput streaming analytics in financial applications. Cloud Pub/Sub can handle millions of transactions per second, providing durable, low-latency message ingestion. It automatically scales to handle peak trading hours or transaction bursts. Dataflow ensures exactly-once processing through deduplication, checkpointing, and stateful transformations, which is critical for financial accuracy and fraud detection. Windowed aggregations allow metrics like per-minute transaction totals, account-level anomaly detection, and fraud scoring in near real-time. BigQuery stores raw and aggregated data for dashboards, ad hoc queries, and historical analytics. Its columnar storage, serverless scaling, and low-latency query capabilities make it ideal for petabyte-scale datasets. This architecture minimizes operational overhead while providing accurate, real-time financial insights essential for compliance and fraud prevention.

B) Cloud Storage → Dataproc → BigQuery is batch-oriented. Transactions must first be stored in Cloud Storage and processed in batches with Dataproc, introducing latency and preventing real-time dashboards. Exactly-once semantics are not guaranteed, and cluster management adds operational complexity.

C) Cloud SQL → Cloud Functions → BigQuery cannot scale to millions of transactions per second. Cloud SQL is not designed for high-throughput streaming, and Cloud Functions are stateless with execution time limits. Achieving exactly-once processing and windowed aggregations would require complex orchestration, increasing risk and overhead.

D) Bigtable → Cloud Run → BigQuery can store raw transaction data efficiently, but Cloud Run lacks distributed stream processing and exactly-once semantics. Windowed aggregations and near real-time dashboards would require additional orchestration, making this solution less practical.

Q77

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

A) Partition by ingestion timestamp and cluster by sensor type

B) Partition by sensor type and cluster by timestamp

C) Use a single unpartitioned table

D) Create multiple tables per sensor type

Answer

A) Partition by ingestion timestamp and cluster by sensor type

Explanation

A) Partitioning by ingestion timestamp reduces the amount of data scanned for time-based queries, lowering cost. Clustering by sensor type physically organizes rows within partitions, improving performance for queries filtered by sensor type. This combination supports petabyte-scale datasets, simplifies operational management, and allows streaming or batch ingestion. Partitioned and clustered tables in BigQuery enable low-latency analytics for IoT applications such as trend analysis, anomaly detection, and predictive modeling. BigQuery handles partition maintenance, metadata management, and scaling automatically, making this design cost-efficient and high-performance for large IoT telemetry datasets.

B) Partitioning by sensor type is inefficient for large-scale IoT deployments with millions of sensors, resulting in many small partitions that increase metadata overhead. Clustering by timestamp alone does not optimize queries filtered by sensor type.

C) A single unpartitioned table is unsuitable for petabyte-scale datasets. Queries filtering by timestamp or sensor type would scan the entire table, increasing cost and reducing performance.

D) Multiple tables per sensor type increase operational complexity. Schema updates, cross-sensor queries, and maintenance are cumbersome. Querying across multiple tables requires unions, increasing complexity and reducing efficiency.

Q78

A company wants to ensure GDPR compliance for BigQuery analytics on customer data. Sensitive PII must be automatically detected, masked, or anonymized while allowing analytics. Which GCP service is most appropriate?

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, providing pre-built detectors for names, emails, phone numbers, social security numbers, and financial data. DLP supports transformations such as masking, tokenization, redaction, and format-preserving encryption, allowing analytics on anonymized datasets without exposing raw PII. Inline transformations during ingestion or query execution minimize operational overhead, and audit logging ensures compliance reporting for GDPR. DLP scales automatically and can handle large datasets, making it ideal for GDPR-compliant analytics pipelines.

B) Cloud KMS manages encryption keys and secures data at rest but does not detect or transform PII for analytics.

C) Cloud Identity-Aware Proxy (IAP) secures application access but does not detect or mask sensitive data for analytics.

D) Cloud Functions can implement custom detection and anonymization, but require development and maintenance, and lack built-in PII detection, making them less reliable for GDPR compliance compared to DLP.

Q79

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

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 queries, including runtime, bytes processed, and job costs. Cloud Functions can query these tables periodically 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 overhead. It supports near real-time cost monitoring, allowing teams to take immediate action to prevent budget overruns. Custom thresholds and rules enable targeted detection of expensive queries, providing operational visibility and proactive cost control without manual intervention.

B) Querying BigQuery from Cloud SQL manually is not scalable. Cloud SQL is not designed for large-scale query metadata analysis, and manual processes would delay alerting.

C) Exporting logs to Cloud Storage and processing offline introduces latency, preventing near real-time alerting and cost optimization.

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

Q80

A company wants to implement a multi-tenant SaaS analytics solution on BigQuery. Each tenant’s data must be 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) Single table with a tenant_id column and clustering by tenant_id provides the optimal combination of scalability, query performance, cost efficiency, and operational simplicity for multi-tenant SaaS analytics in BigQuery.

Using a single table design allows all tenant data to reside in one logical structure while maintaining tenant-level isolation through the tenant_id column. This approach simplifies schema management because any schema evolution—such as adding a new column or modifying data types—applies to all tenants in one central location, avoiding the complexity of propagating changes across thousands of separate tables or projects.

Clustering by tenant_id organizes rows physically by tenant, which drastically improves query performance for filtering, aggregating, or joining data on a per-tenant basis. When queries include WHERE tenant_id = X, BigQuery can scan only the relevant clusters rather than the entire table, reducing scanned data and improving both speed and cost-efficiency. This is particularly important when datasets grow to petabyte-scale, as scanning unnecessary data would be expensive and slow.

Additionally, combining clustering with time-based partitioning—such as partitioning by ingestion date—provides another layer of performance optimization. Time-based partitioning ensures that queries targeting specific time ranges (e.g., last month’s logs) only scan the relevant partitions, reducing query latency and costs. This design is essential for SaaS applications where most analytics are often time-oriented, such as monthly usage reports or trends over time.

The single-table approach also simplifies cross-tenant queries, which are occasionally required for aggregated reporting, benchmarking, or system-wide analytics. Instead of performing complex joins or unions across multiple tables or projects, these queries can simply filter or group by tenant_id within the same table. This ensures high performance even at scale and avoids operational complexity.

From an operational perspective, this design leverages BigQuery’s serverless architecture to automatically handle resource allocation, query optimization, scaling, and replication. There is no need to provision clusters, manage storage, or handle indexes manually. BigQuery’s internal query planner can optimize queries for clustered and partitioned data automatically, which allows engineers to focus on analytics and business logic rather than infrastructure management.

Cost efficiency is another major advantage. With all tenants in one table, storage and query costs are easier to manage because BigQuery charges are based on scanned bytes, and clustering ensures only relevant data is scanned. Conversely, maintaining multiple tables or projects per tenant would increase metadata storage and could lead to higher overhead, both operationally and financially.

B) Separate BigQuery projects per tenant introduces significant operational and administrative complexity. Managing separate projects for hundreds or thousands of tenants requires careful coordination of billing accounts, IAM policies, quotas, and schema changes. Performing cross-tenant queries would require either exporting and merging datasets or using federated queries, which are slower, more cumbersome, and prone to errors. Additionally, schema evolution becomes labor-intensive because any change must be replicated across all projects, increasing the likelihood of inconsistencies. While project separation provides strong isolation, it does so at the expense of efficiency, scalability, and maintainability.

C) Store data in Cloud SQL and replicate to BigQuery adds unnecessary latency and operational overhead. Cloud SQL is a relational database optimized for transactional workloads, not for analytics on petabyte-scale datasets. Replicating large volumes of data to BigQuery introduces delays and requires additional pipelines, monitoring, and error handling. Real-time or near-real-time analytics become challenging, and operational complexity grows significantly with data size. For a SaaS analytics solution designed for large-scale, multi-tenant datasets, Cloud SQL is not a feasible option.

D) Multiple unpartitioned tables per tenant also increases operational complexity and reduces performance. Maintaining thousands of individual tables becomes difficult as the number of tenants grows. Cross-tenant queries require UNION ALL statements across tables or building dynamic queries, which is inefficient and error-prone. Unpartitioned tables cannot efficiently handle time-based queries, leading to large amounts of scanned data and higher costs. Schema updates also become cumbersome because changes must be applied individually to each table, increasing the risk of inconsistencies and operational errors.

 Security and Access Control: While all tenant data resides in one table, row-level security can enforce tenant-specific access policies. Using BigQuery’s row-level security ensures that each tenant can only access their own data while enabling authorized users to perform cross-tenant analytics safely.Schema Evolution: Centralized schema management allows new features or data types to be added without disrupting tenants individually. BigQuery supports additive schema changes, making it easier to evolve the dataset over time.Query Optimization: Clustering combined with partitioning allows BigQuery’s query planner to automatically prune irrelevant data, significantly improving query speed and reducing costs. Additionally, materialized views or summary tables can further accelerate repetitive analytical queries without duplicating data.Scalability: Single-table design scales seamlessly with BigQuery’s serverless infrastructure. Petabyte-scale datasets can be handled without manual sharding or infrastructure tuning. BigQuery automatically distributes storage and compute resources across its internal architecture to optimize query performance.Cost Management: By reducing the amount of data scanned per query through clustering and partitioning, the single-table approach minimizes query costs. Separate tables or projects would increase metadata and storage costs and complicate cost tracking across tenants.
Using a single table with a tenant_id column and clustering by tenant_id provides the most effective, scalable, and cost-efficient approach for implementing a multi-tenant SaaS analytics solution in BigQuery. It balances tenant isolation, operational simplicity, query performance, and cost efficiency while supporting petabyte-scale datasets and occasional cross-tenant queries. Alternative approaches either introduce significant management complexity, reduce query performance, or are unsuitable for large-scale analytics workloads.