Google  Professional Data Engineer on Google Cloud Platform Exam  Dumps and Practice Test Questions Set 2  Q 21- 40

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Q21

You are designing a GCP data pipeline to process millions of events per second from IoT devices. The pipeline must guarantee exactly-once processing, scale automatically, and allow complex aggregations. 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 acts as a high-throughput, durable messaging system capable of ingesting millions of events per second. It buffers incoming messages to handle spikes in traffic and ensures at-least-once delivery. Dataflow, using Apache Beam, processes streaming data with exactly-once semantics through stateful operations and deduplication. It supports windowed aggregations, joins, and transformations required for complex analytics. BigQuery serves as the storage layer for both raw and aggregated data, providing a serverless, highly scalable analytics engine. This architecture is fully managed, automatically scales with data volume, and supports low-latency processing for real-time analytics.

B) Cloud Storage → Dataproc → BigQuery is more suitable for batch processing. Cloud Storage collects files, and Dataproc processes them in batch mode. This introduces latency, making it unsuitable for real-time event processing. Exactly-once semantics are not natively supported, and scaling is manual.

C) Cloud SQL → Cloud Functions → BigQuery cannot handle high-throughput streaming workloads. Cloud SQL is a relational database optimized for transactions, not millions of streaming events per second. Cloud Functions have execution time limits and are stateless, making exactly-once semantics difficult to enforce at scale.

D) Bigtable → Cloud Run → BigQuery can store large amounts of time-series data but Cloud Run is stateless and cannot perform distributed, stateful stream processing. Implementing exactly-once processing and complex aggregations would require additional orchestration, increasing complexity.

Q22

Your team needs to preprocess large amounts of semi-structured JSON data stored in Cloud Storage and load it into BigQuery. The pipeline must scale automatically and handle transformations efficiently. Which service should you use for preprocessing?

A) Dataflow

B) Cloud Functions

C) Cloud SQL

D) Dataproc

Answer

A) Dataflow

Explanation

A) Dataflow is a fully managed, serverless service for batch and stream processing. It natively integrates with Cloud Storage as a source and BigQuery as a sink. Using Apache Beam, Dataflow can parse, transform, and enrich JSON data efficiently at large scale. Dataflow handles dynamic scaling, checkpointing, and retries automatically, allowing reliable processing of massive datasets without manual infrastructure management. It supports complex transformations like flattening nested JSON, filtering, and aggregations, making it ideal for ETL pipelines.

B) Cloud Functions can handle event-driven transformations but are limited in execution time and resources. For large-scale JSON preprocessing, Cloud Functions cannot efficiently process multi-terabyte datasets.

C) Cloud SQL is a relational database optimized for transactional workloads. It does not provide batch or stream processing capabilities at scale, nor is it optimized for JSON transformations for analytics purposes.

D) Dataproc provides managed Hadoop and Spark clusters, capable of large-scale transformations. However, clusters must be managed, sized, and scaled, adding operational overhead. Dataflow provides a fully serverless approach, reducing complexity while offering equivalent or superior processing capabilities.

Q23

You are tasked with monitoring BigQuery for anomalous spikes in query costs and sending automated alerts. The system must be serverless, scalable, and low maintenance. Which approach is most suitable?

A) Query INFORMATION_SCHEMA with Cloud Functions and Pub/Sub

B) Query BigQuery in 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 with Cloud Functions and Pub/Sub

Explanation

A) BigQuery’s INFORMATION_SCHEMA provides metadata about queries, including job cost, duration, and resource usage. By querying these tables periodically, Cloud Functions can detect unusual cost patterns and publish alerts to Pub/Sub or send notifications. This setup is serverless, scales automatically, and requires no infrastructure management. Alerts are generated in near real-time, enabling proactive monitoring of expensive queries and operational cost control.

B) Querying BigQuery from Cloud SQL manually introduces unnecessary complexity. Cloud SQL is not designed to hold large amounts of query metadata and manual monitoring does not support automated real-time alerts.

C) Exporting logs to Cloud Storage and processing offline introduces latency. Alerts are delayed, which is unsuitable for proactive monitoring of high-cost queries.

D) Using Cloud Bigtable to store query metadata and poll for alerts is operationally complex. Bigtable is optimized for high-throughput transactional workloads, but integrating it with BigQuery metadata and implementing alert logic adds unnecessary complexity.

 

Q24

You are designing a data warehouse in BigQuery for a global e-commerce company. The dataset includes millions of customer transactions per day. Queries often filter by date and product category. The dataset is expected to grow to petabytes. Which design pattern optimizes performance and cost?

A) Partition by transaction date and cluster by product category

B) Partition by product category and cluster by transaction date

C) Store all data in a single unpartitioned table

D) Use multiple tables per day per category

Answer

A) Partition by transaction date and cluster by product category

Explanation

A) Partitioning by transaction date ensures that queries filtering by date scan only relevant partitions, significantly reducing query cost and improving performance. Clustering by product category physically organizes similar categories together, optimizing filtering and aggregation. This design pattern leverages BigQuery’s partitioning and clustering features, making it highly efficient for petabyte-scale datasets where queries frequently filter on both date and category.

B) Partitioning by product category is not ideal because there could be thousands of categories, leading to many small partitions. Clustering by transaction date provides limited benefit, as queries usually filter by both date and category, making the combination less efficient.

C) Storing all data in a single unpartitioned table forces BigQuery to scan the entire dataset for each query, increasing cost and latency, especially at petabyte scale.

D) Using multiple tables per day per category introduces operational overhead and complicates query management. Cross-day or cross-category queries require table unions or scripting, which is less efficient than a single partitioned and clustered table.

Q25

A company wants to analyze logs from multiple applications in real-time for operational insights. The pipeline must tolerate spikes in traffic, provide exactly-once processing, and allow low-latency aggregation. 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 reliably ingests high-volume logs from multiple applications and buffers messages to handle traffic spikes. Dataflow processes the events in real-time, providing exactly-once semantics through deduplication and stateful transformations. Windowed aggregations allow timely metrics and low-latency insights. BigQuery stores both raw and processed logs, enabling high-performance analytics queries. This architecture is fully managed, serverless, and scales automatically with incoming data, satisfying requirements for reliability, low latency, and exactly-once processing.

B) Cloud Storage → Dataproc → BigQuery is batch-oriented. Collecting logs in Cloud Storage and processing with Dataproc introduces latency, making it unsuitable for real-time operational insights. Exactly-once semantics are not natively supported.

C) Cloud SQL → Cloud Functions → BigQuery cannot scale efficiently for high-volume streaming logs. Cloud SQL is transactional, and Cloud Functions are stateless with execution limits, making exactly-once semantics difficult.

D) Bigtable → Cloud Run → BigQuery can store large datasets, but Cloud Run is stateless and cannot provide distributed stream processing or exactly-once semantics. Aggregations require additional orchestration, increasing operational complexity.

 Q26

You need to design a pipeline that ingests streaming clickstream events from multiple websites, performs transformations, and loads them into BigQuery for near real-time analytics. The system must handle bursts of traffic and provide exactly-once processing. 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 acts as a fully managed messaging system, capable of ingesting events from multiple websites at scale. It buffers traffic spikes, ensuring messages are not lost during bursts of activity. Dataflow, using Apache Beam, processes streaming data with exactly-once semantics through stateful operations and deduplication. It allows windowed transformations, aggregations, and enrichment of clickstream events before writing to BigQuery. BigQuery provides a serverless, highly scalable analytics engine that supports near real-time querying. This architecture is fully managed, scales automatically, and is ideal for high-throughput, low-latency streaming analytics.

B) Cloud Storage → Dataproc → BigQuery is a batch-oriented architecture. Cloud Storage collects events as files, which Dataproc processes periodically. This introduces latency and does not provide exactly-once semantics out-of-the-box. Handling bursts of traffic efficiently would require additional cluster management and scaling.

C) Cloud SQL → Cloud Functions → BigQuery is not suitable for high-throughput streaming workloads. Cloud SQL cannot handle millions of events per second, and Cloud Functions have execution time limits and stateless behavior, making exactly-once processing difficult to achieve.

D) Bigtable → Cloud Run → BigQuery provides scalable storage for time-series or key-value data, but Cloud Run is stateless and does not natively handle distributed, stateful stream processing. Exactly-once semantics and complex aggregations would require additional orchestration, increasing complexity.

Q27

A company wants to run analytics on petabytes of historical log data stored in Cloud Storage. The solution should allow SQL-like queries without managing infrastructure. Which service is most appropriate?

A) BigQuery

B) Cloud SQL

C) Cloud Bigtable

D) Dataproc with Hive

Answer

A) BigQuery

Explanation

A) BigQuery is a fully managed, serverless data warehouse that can query petabyte-scale datasets stored in Cloud Storage. Using external tables, BigQuery allows SQL-like queries without moving data, enabling cost-efficient analytics. Its columnar storage and distributed query engine provide high performance for aggregations, filtering, and joins. BigQuery automatically handles scaling, replication, and availability, freeing teams from infrastructure management. It is optimized for both structured and semi-structured data formats, such as JSON, Avro, and Parquet, making it ideal for analyzing historical log datasets.

B) Cloud SQL is a relational database optimized for transactional workloads. It does not scale to petabytes of data efficiently and would require extensive infrastructure management, making it unsuitable for this use case.

C) Cloud Bigtable is a NoSQL wide-column store optimized for low-latency lookups, time-series, or key-value access patterns. While it can handle large datasets, it does not provide SQL-like querying natively and is not optimized for analytical workloads across massive historical datasets.

D) Dataproc with Hive can process large datasets using Hadoop/Spark. However, it requires cluster management, scaling, and operational overhead. Queries are less flexible and slower compared to BigQuery, and running petabyte-scale analytics with minimal maintenance would be challenging.

Q28

You are building a GCP pipeline that must anonymize sensitive customer data before storing it for analytics. The solution should automatically detect PII and allow configurable anonymization techniques. Which service is best suited for this task?

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 DLP is purpose-built for discovering, classifying, and anonymizing sensitive data. It provides built-in detectors for PII such as names, emails, credit card numbers, and addresses. DLP supports configurable anonymization techniques, including masking, tokenization, and redaction, allowing selective anonymization before storing data in BigQuery or Cloud Storage. It integrates seamlessly with multiple GCP services, supports both structured and unstructured data, and logs anonymization events for compliance reporting.

B) Cloud KMS provides encryption and key management. While it secures data at rest, it does not perform selective anonymization or masking required for analytics. Encryption alone does not prevent analysts from accessing raw PII after decryption.

C) Cloud IAP enforces authentication and access control for applications. It secures access but does not detect or anonymize sensitive data, making it unsuitable for GDPR-compliant pipelines.

D) Cloud Functions can be used to implement custom anonymization logic, but it requires manual development and testing. Unlike DLP, it lacks built-in PII detection and predefined anonymization techniques, making it less reliable and more error-prone.

Q29

A team needs to monitor BigQuery jobs for unusually high query costs and automatically send alerts. The system must be fully serverless and scalable. Which approach is most appropriate?

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

B) Query BigQuery directly in Cloud SQL manually

C) Export logs to Cloud Storage and analyze offline

D) Use Cloud Bigtable to store metadata and poll for alerts

Answer

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

Explanation

A) BigQuery’s INFORMATION_SCHEMA provides metadata about queries, including job cost, duration, and resource usage. Cloud Functions can periodically query these tables, detect anomalous cost patterns, and publish alerts to Pub/Sub. This approach is fully serverless, automatically scales, and requires minimal operational maintenance. Alerts are near real-time, allowing proactive monitoring of high-cost queries.

B) Querying BigQuery from Cloud SQL manually introduces unnecessary infrastructure and operational overhead. Cloud SQL is not optimized to store or analyze large volumes of query metadata, and manual monitoring does not provide timely alerts.

C) Exporting logs to Cloud Storage and analyzing offline adds latency, delaying alerts and reducing the effectiveness of cost monitoring.

D) Using Cloud Bigtable for metadata storage introduces operational complexity. Bigtable is optimized for high-throughput transactional workloads, and integrating it with BigQuery metadata and alerting logic adds unnecessary complexity compared to the serverless Cloud Functions approach.

Q30

You need to process streaming financial transactions in real-time to detect fraud. The pipeline must handle high throughput, provide exactly-once processing, and scale automatically. 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 ingests high-throughput streaming transactions reliably, buffering spikes in traffic. Dataflow processes events in real-time, providing exactly-once semantics with stateful transformations, deduplication, and windowed aggregations. Fraud detection rules can be applied during processing, and results are stored in BigQuery for analytics and reporting. This architecture is fully managed, serverless, and scales automatically, allowing near real-time detection of anomalies.

B) Cloud Storage → Dataproc → BigQuery is batch-oriented. Collecting transactions in files and processing them in Dataproc introduces latency, making real-time fraud detection impractical. Exactly-once processing is not inherently supported.

C) Cloud SQL → Cloud Functions → BigQuery cannot scale for high-throughput streaming. Cloud SQL is not optimized for large numbers of concurrent inserts, and Cloud Functions have resource and execution time limits, making exactly-once processing difficult.

D) Bigtable → Cloud Run → BigQuery can store raw transaction data efficiently, but Cloud Run is stateless and does not provide distributed stream processing. Achieving exactly-once semantics and low-latency aggregation requires additional orchestration and infrastructure, increasing complexity.

Q31

You are designing a GCP pipeline to process streaming sensor data from hundreds of thousands of IoT devices. The pipeline must support real-time aggregation, fault tolerance, exactly-once processing, and scalable ingestion. 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) Using Cloud Pub/Sub, Dataflow, and BigQuery is the optimal choice for streaming IoT data because each component is designed to address the specific requirements outlined. Cloud Pub/Sub acts as a highly scalable message broker that can ingest events from hundreds of thousands of IoT devices simultaneously. It provides durable message storage and at-least-once delivery guarantees, buffering spikes in traffic to prevent data loss. Dataflow, using Apache Beam, is fully managed and serverless, meaning it automatically scales resources to match workload demands, which is essential for handling the variable volume characteristic of IoT streaming data. Dataflow also offers exactly-once processing semantics, achieved through deduplication, stateful processing, and checkpointing, which ensures that each event is processed precisely once, regardless of failures or retries. Additionally, Dataflow supports windowed aggregations, which are crucial for real-time metrics such as rolling averages, counts, and anomaly detection over time. BigQuery serves as the analytics layer, allowing both real-time and historical analysis on streaming and batch data, and its serverless, columnar architecture ensures high performance even at petabyte scale. Together, this architecture provides a fully managed, fault-tolerant, scalable, and low-latency pipeline capable of meeting the requirements of IoT data processing.

B) Cloud Storage → Dataproc → BigQuery is more suited to batch processing scenarios. Cloud Storage collects files that are later processed by Dataproc using Spark or Hadoop clusters. While Dataproc can process large volumes of data and supports parallelism, this approach introduces significant latency because data must first be persisted to Cloud Storage before processing. Furthermore, Dataproc requires cluster provisioning, monitoring, and scaling decisions, adding operational overhead. Dataproc does not natively provide exactly-once semantics for streaming data, making it difficult to guarantee that each IoT event is processed precisely once, particularly in cases of failure or retry. This option is unsuitable for real-time streaming requirements.

C) Cloud SQL → Cloud Functions → BigQuery is limited in scalability for high-throughput streaming data. Cloud SQL is a transactional relational database, not designed to ingest millions of streaming events per second, and would quickly become a bottleneck. Cloud Functions can process small event-driven tasks but have execution time and memory limits, which prevent efficient handling of large-scale, continuous streams of IoT data. Furthermore, Cloud Functions are stateless, so implementing exactly-once semantics and complex stateful transformations requires additional orchestration, increasing complexity.

D) Bigtable → Cloud Run → BigQuery could store raw IoT events efficiently due to Bigtable’s low-latency, high-throughput capabilities. However, Cloud Run is stateless and does not provide distributed stream processing or native support for exactly-once semantics. Aggregations and transformations would need to be implemented manually, potentially requiring additional infrastructure such as Dataflow or Dataproc. While this approach is feasible for certain workloads, it introduces operational complexity and lacks the integrated fault-tolerant streaming capabilities provided by the Pub/Sub → Dataflow → BigQuery pipeline.

Q32

A company wants to perform GDPR-compliant analytics on sensitive customer data stored in BigQuery. The solution must detect PII, mask or tokenize it, and allow analytics on anonymized data. 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 purpose-built for handling sensitive information such as personally identifiable information (PII). DLP provides automated discovery, classification, and transformation capabilities that allow organizations to anonymize or mask sensitive data before analytics. It supports structured data in BigQuery tables as well as unstructured data in Cloud Storage and Pub/Sub streams. DLP comes with built-in PII detectors for common identifiers such as names, addresses, phone numbers, social security numbers, and financial information. It also allows configurable anonymization techniques, including masking, redaction, tokenization, and format-preserving encryption. This ensures compliance with GDPR by enabling selective anonymization of data while still maintaining utility for analytical operations. DLP integrates seamlessly with BigQuery, allowing pre-processing or inline transformation of sensitive fields before queries are executed. Logging and audit trails ensure that organizations can demonstrate compliance.

B) Cloud KMS provides encryption key management for data at rest and in transit. While it secures sensitive data, it does not perform detection, masking, or anonymization of PII. Analysts would still need access to decrypted data for queries, making it insufficient for GDPR-compliant anonymized analytics.

C) Cloud Identity-Aware Proxy (IAP) enforces authentication and access control to applications. It prevents unauthorized access to services but does not detect or anonymize sensitive data in datasets. IAP addresses security at the access level rather than data-level privacy compliance.

D) Cloud Functions can implement custom PII detection and anonymization logic. However, this approach requires extensive development and testing, is error-prone, and does not provide the built-in pre-trained PII detection and transformation capabilities that DLP offers. Maintaining and scaling such custom logic for large datasets would also increase operational complexity.

Q33

A team needs to detect unusually expensive queries in BigQuery automatically and send alerts. The solution must be serverless, scalable, and provide near real-time notifications. Which approach is most appropriate?

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

B) Query BigQuery in 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’s INFORMATION_SCHEMA provides metadata about executed queries, including query cost, bytes processed, runtime, and job statistics. By creating scheduled Cloud Functions that query these tables, anomalous query costs can be detected programmatically. Cloud Functions can then publish alerts to Pub/Sub topics or send notifications through email, Slack, or other channels. This solution is serverless, scales automatically with query volume, and requires minimal operational overhead. The approach also supports near real-time monitoring, as Cloud Functions can run frequently and analyze recent query data to provide timely alerts. This ensures proactive cost management and operational visibility for BigQuery workloads.

B) Querying BigQuery from Cloud SQL manually introduces unnecessary complexity. Cloud SQL is not designed to store or analyze large amounts of query metadata and requires manual intervention, which delays alerting and reduces reliability.

C) Exporting logs to Cloud Storage for offline analysis introduces latency. Alerts will not be generated in near real-time, reducing the ability to respond to high-cost queries promptly.

D) Using Cloud Bigtable to store query metadata and polling for alerts is operationally complex. Bigtable is optimized for high-throughput key-value workloads, but managing metadata ingestion, query logic, and polling adds unnecessary overhead compared to the simpler Cloud Functions + Pub/Sub approach.

Q34

A company needs to analyze time-series metrics from IoT devices in BigQuery. Queries frequently filter by timestamp and device ID. The dataset is expected to grow to petabytes. Which table design optimizes performance and cost?

A) Partition by ingestion time and cluster by device ID

B) Partition by device ID and cluster by timestamp

C) Use a single unpartitioned table

D) Create multiple tables per device

Answer

A) Partition by ingestion time and cluster by device ID

Explanation

A) Partitioning by ingestion time ensures that queries filtering by date scan only relevant partitions, dramatically reducing query cost and improving performance. Clustering by device ID physically organizes data for each device together, making queries that filter or aggregate on specific devices more efficient. Partitioned and clustered tables also reduce storage and metadata overhead compared to creating multiple small tables, and they support automatic maintenance by BigQuery. This combination allows for petabyte-scale datasets with low-latency queries for time-series analytics while controlling costs.

B) Partitioning by device ID is less efficient because IoT deployments can have millions of devices, resulting in many small partitions and poor performance. Clustering by timestamp provides limited performance improvement if the query filters primarily by device and time.

C) Using a single unpartitioned table is inefficient at petabyte scale. Queries filtering by date or device would scan the entire dataset, leading to higher costs and slower performance.

D) Creating multiple tables per device introduces significant operational complexity. Managing schema updates, cross-device queries, and maintaining performance across millions of tables becomes unmanageable.

Q35

A company wants to implement a real-time fraud detection pipeline for financial transactions. The solution must handle high throughput, provide low-latency alerts, and guarantee exactly-once processing. 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 ingests financial transactions reliably, buffering high-volume events during traffic spikes. Dataflow processes the streaming transactions in real-time using stateful operations, providing exactly-once processing semantics through deduplication and checkpointing. Fraud detection logic, including pattern matching, anomaly detection, and aggregation, can be applied in-flight. BigQuery stores raw and aggregated results for analytics and reporting. This architecture is fully managed, serverless, and automatically scales to match workload demands, ensuring low-latency processing for real-time fraud detection.

B) Cloud Storage → Dataproc → BigQuery is batch-oriented. Writing transactions to files and processing them periodically introduces latency, making real-time fraud detection impractical. Exactly-once processing is not guaranteed, and spikes in traffic may overwhelm batch processes.

C) Cloud SQL → Cloud Functions → BigQuery cannot scale to high-throughput streaming workloads. Cloud SQL is a transactional database and would become a bottleneck, while Cloud Functions’ execution limits prevent long-running or large-scale stream processing.

D) Bigtable → Cloud Run → BigQuery can store data efficiently, but Cloud Run is stateless and does not provide distributed, exactly-once stream processing. Fraud detection logic would require additional orchestration, increasing operational complexity.

Q36

Your company wants to design a pipeline that ingests and processes streaming user activity events from a mobile application. The system must handle spikes in traffic, support exactly-once processing, and provide real-time analytics. 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 streaming analytics of mobile app events. Cloud Pub/Sub provides high-throughput, durable message ingestion, buffering spikes in event traffic and ensuring that no messages are lost. Pub/Sub supports horizontal scaling, meaning it can handle sudden bursts in user activity seamlessly. Dataflow, which leverages Apache Beam, processes the streaming events with exactly-once semantics through checkpointing and stateful processing. It also allows complex transformations, aggregations, and windowing to generate real-time insights, such as active user counts, session duration metrics, and event trend analysis. BigQuery, as the analytics layer, enables fast querying of both raw and aggregated data. Its serverless architecture scales automatically and is optimized for large datasets, making it suitable for real-time dashboards and ad hoc reporting. Using this architecture, mobile analytics teams can monitor engagement, detect anomalies, and create operational reports in near real-time, all without managing infrastructure or clusters.

B) Cloud Storage → Dataproc → BigQuery is more suitable for batch processing workloads. Cloud Storage collects events as files, which Dataproc processes in batches. While Dataproc can handle large-scale transformations, it requires cluster provisioning, manual scaling, and monitoring. Batch processing introduces latency, making this approach unsuitable for near real-time analytics or alerting. Exactly-once processing is not guaranteed, and spikes in event traffic may overwhelm batch pipelines, causing delays in data availability.

C) Cloud SQL → Cloud Functions → BigQuery cannot efficiently handle high-throughput streaming data. Cloud SQL is a relational database optimized for transactional workloads, which would become a bottleneck under millions of events per second. Cloud Functions have execution time limits and are stateless, requiring complex orchestration to achieve exactly-once semantics. Implementing large-scale aggregations or windowed computations with this setup is operationally complex and error-prone.

D) Bigtable → Cloud Run → BigQuery could handle storage of raw events efficiently because Bigtable supports high-throughput writes. However, Cloud Run is stateless and cannot perform distributed, stateful stream processing required for exactly-once semantics. Windowed aggregations and real-time analytics would require additional processing components, adding operational complexity. This architecture is less integrated than Pub/Sub → Dataflow → BigQuery for real-time mobile analytics.

Q37

A team is designing a BigQuery dataset for a multi-tenant SaaS application. Each tenant’s data must be isolated, but queries occasionally require cross-tenant aggregation. 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 BigQuery table with a tenant_id column and clustering by tenant_id is the most scalable and efficient design for multi-tenant SaaS applications. Partitioning and clustering features in BigQuery reduce query cost and improve performance. Clustering by tenant_id physically co-locates rows for the same tenant, enabling efficient filtering and aggregation. Queries spanning multiple tenants for reporting or benchmarking are simpler with a single table, requiring only filters on tenant_id rather than unions of separate tables. This approach supports schema evolution centrally, avoids operational overhead, and scales to petabyte-level datasets. BigQuery’s serverless architecture ensures automatic scaling, and clustering allows frequent queries on individual tenants without scanning unnecessary data, controlling costs effectively.

B) Creating separate BigQuery projects per tenant introduces significant operational complexity. Each project requires individual IAM configurations, billing setup, monitoring, and schema maintenance. Running cross-tenant queries is cumbersome and inefficient because unions or federated queries across projects are required. This approach is not scalable when hundreds or thousands of tenants exist.

C) Storing data in Cloud SQL and replicating to BigQuery is less efficient. Cloud SQL is not optimized for petabyte-scale analytics, and replication pipelines add latency and operational overhead. Cross-tenant analytics becomes more complex due to distributed storage and manual ETL processes.

D) Using multiple unpartitioned tables per tenant leads to high operational overhead. Querying across tenants requires table unions or dynamic SQL, and schema updates must be applied consistently to all tables. This approach is difficult to manage at scale and increases the risk of errors.

Q38

A company wants to analyze billions of sensor readings per day from industrial IoT devices. Queries often filter by timestamp and device type, and the dataset will reach petabytes. Which BigQuery table design optimizes performance and cost?

A) Partition by ingestion time and cluster by device type

B) Partition by device type and cluster by timestamp

C) Use a single unpartitioned table

D) Create separate tables for each device type

Answer

A) Partition by ingestion time and cluster by device type

Explanation

A) Partitioning by ingestion time allows BigQuery to scan only the relevant partitions for time-based queries, which reduces scanned bytes and lowers costs. Clustering by device type organizes similar records physically, improving filtering and aggregation for queries that group or filter by device. This combination is ideal for petabyte-scale IoT datasets where queries often target specific devices and date ranges. It also simplifies data management, as only one table needs maintenance, and partitioned tables automatically handle ingestion time boundaries efficiently. BigQuery’s serverless architecture ensures automatic scaling, and clustering enables low-latency queries for individual device types.

B) Partitioning by device type is inefficient because IoT deployments may have thousands or millions of device types, creating numerous small partitions, which increases metadata overhead and decreases query performance. Clustering by timestamp alone does not optimize filtering on device types, which is a common query pattern.

C) Using a single unpartitioned table forces queries to scan the entire dataset for filtering or aggregation, resulting in higher costs and slower performance, especially at petabyte scale.

D) Creating separate tables per device type increases operational complexity. Managing thousands of tables for schema updates, cross-device queries, and reporting is cumbersome. Querying across devices requires unions, which reduces performance and increases the likelihood of errors.

Q39

A team is implementing a real-time analytics pipeline for financial transactions. The system must scale automatically, provide exactly-once processing, and deliver low-latency alerts for anomalies. 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 provides highly durable and scalable ingestion of streaming transaction data, buffering traffic spikes to prevent data loss. Dataflow processes the streaming transactions in real-time using stateful transformations, deduplication, and windowed computations, guaranteeing exactly-once processing. Anomaly detection logic, such as pattern recognition, threshold-based alerts, or machine learning models, can be applied in-flight. BigQuery serves as the analytics layer for storing raw and aggregated data and allows rapid queries for monitoring dashboards or ad hoc analysis. This fully managed, serverless architecture scales automatically, handles failure recovery, and provides low-latency analytics, making it ideal for financial transaction monitoring.

B) Cloud Storage → Dataproc → BigQuery is batch-oriented. Transactions are written to files and processed periodically, introducing latency that prevents real-time anomaly detection. Exactly-once semantics are not inherently supported, and spikes in transaction volume may overwhelm batch processing jobs.

C) Cloud SQL → Cloud Functions → BigQuery cannot handle high-throughput streaming workloads. Cloud SQL is optimized for transactional workloads and will become a bottleneck under heavy streaming data. Cloud Functions are stateless with execution limits, making exactly-once processing difficult for continuous streams.

D) Bigtable → Cloud Run → BigQuery could store raw transactions at scale, but Cloud Run is stateless and cannot provide distributed stream processing with exactly-once guarantees. Implementing anomaly detection logic would require additional orchestration, increasing operational complexity and reducing reliability.

Q40

A company wants to run analytics on large volumes of historical log data stored in Cloud Storage. The solution must allow SQL queries without managing infrastructure, provide high performance, and handle petabyte-scale datasets. Which service is most appropriate?

A) BigQuery

B) Cloud SQL

C) Dataproc with Hive

D) Cloud Bigtable

Answer

A) BigQuery

Explanation

A) BigQuery is a fully managed, serverless data warehouse that is specifically designed for analytics at petabyte scale. It allows users to run SQL queries directly on large datasets without worrying about infrastructure provisioning, scaling, or maintenance. BigQuery’s architecture separates storage and compute, enabling dynamic scaling based on query demands, which ensures high performance even for complex analytical workloads.

Data can be queried in BigQuery either by loading it into native tables or by creating external tables that reference data stored in Cloud Storage in formats such as JSON, Avro, Parquet, or ORC. Its columnar storage format is optimized for analytical queries, allowing it to scan only the necessary columns, thereby reducing latency and cost. The distributed query engine parallelizes computations across multiple nodes, providing rapid aggregation, join operations, and filtering across billions of rows.

BigQuery also supports advanced features that further enhance analytics performance and cost efficiency. Partitioning allows tables to be divided based on date or other fields, so queries can scan only relevant partitions instead of the entire dataset. Clustering organizes data based on the values of specific columns, improving the speed of selective queries. Additionally, BigQuery ML enables machine learning model training and prediction directly within the platform, and integration with tools like Looker, Data Studio, and third-party BI tools allows seamless visualization and reporting.

By contrast:

B) Cloud SQL is a fully managed relational database service suited for transactional workloads rather than analytical processing. It is limited in terms of scale, typically handling terabytes rather than petabytes of data. Complex analytical queries on massive historical logs would require significant manual optimization, indexing, and sharding, and performance would degrade under petabyte-scale workloads.

C) Dataproc with Hive provides a managed Hadoop/Spark environment for large-scale batch processing. While it can handle large datasets, it is not serverless; users must provision and manage clusters, configure scaling, and handle maintenance. Query performance is generally slower for ad hoc analytics, and operations incur higher management overhead compared to BigQuery’s serverless model.

D) Cloud Bigtable is optimized for high-throughput, low-latency key-value and time-series workloads, but it does not natively support SQL queries. It is designed for operational workloads rather than analytical processing, making it unsuitable for large-scale historical log analytics that require ad hoc queries and aggregations.

In   BigQuery is the most appropriate choice because it meets all the requirements: serverless operation, SQL support, high performance, and the ability to handle petabyte-scale datasets efficiently. Its ecosystem of analytical, machine learning, and visualization integrations makes it ideal for organizations that need actionable insights from large volumes of historical data stored in Cloud Storage.