Google  Professional Data Engineer on Google Cloud Platform Exam  Dumps and Practice Test Questions Set 1  Q 1 – 20 

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

Q1

You are designing a data pipeline on Google Cloud Platform for processing streaming IoT sensor data. The pipeline must ensure exactly-once processing semantics and low latency. Which service combination is most appropriate?

A) Cloud Pub/Sub with Dataflow using Apache Beam

B) Cloud Storage with BigQuery batch loading

C) Cloud Pub/Sub with Cloud Functions

D) Cloud SQL with App Engine

Answer

A) Cloud Pub/Sub with Dataflow using Apache Beam

Explanation

A) Cloud Pub/Sub with Dataflow using Apache Beam is designed for real-time data ingestion and processing. Cloud Pub/Sub acts as a messaging system that reliably collects data from various sources, ensuring messages are not lost. Dataflow, using the Apache Beam SDK, allows building pipelines that can apply transformations on streaming data with strong guarantees. Apache Beam’s model provides exactly-once processing semantics by handling deduplication and state management internally, which is crucial for IoT sensor data where each reading matters. Additionally, Dataflow optimizes execution for low latency, dynamically scaling resources based on input rates and processing complexity. Together, these services meet both requirements of exactly-once semantics and low latency processing.

B) Cloud Storage with BigQuery batch loading is not ideal for real-time streaming. Cloud Storage is a storage service optimized for durability and cost efficiency but is not suitable for low-latency ingestion. BigQuery batch loading involves periodically loading data files from Cloud Storage, which introduces delays and prevents real-time processing. This setup might be acceptable for historical analysis or daily aggregates but fails to meet the low-latency requirement of streaming IoT data. There is also no inherent exactly-once processing guarantee in this pipeline.

C) Cloud Pub/Sub with Cloud Functions can handle streaming events, but Cloud Functions is better suited for lightweight processing and simple transformations. While Cloud Functions can trigger on Pub/Sub messages, it does not natively provide exactly-once processing semantics. There can be retries or duplicates, especially under high message volume. For complex transformations or stateful processing, this combination becomes difficult to scale efficiently. Low latency is achievable, but exactly-once semantics are not guaranteed, making this unsuitable for critical IoT scenarios where every sensor reading must be processed exactly once.

D) Cloud SQL with App Engine is not suitable for large-scale streaming pipelines. Cloud SQL is a relational database optimized for transactional workloads, not high-throughput streaming. App Engine can host applications that interact with the database, but this combination would struggle with massive streaming data from IoT sensors. Latency would increase, and exactly-once semantics would be difficult to maintain because relational databases are not designed to manage high-throughput deduplication and stateful stream processing.

In only the combination of Cloud Pub/Sub and Dataflow using Apache Beam satisfies both exactly-once semantics and low-latency requirements. Cloud Pub/Sub reliably ingests messages, and Dataflow provides scalable, stateful, and fault-tolerant stream processing with strong guarantees. Other combinations either compromise latency, scalability, or processing guarantees.

Q2

Your team needs to store petabytes of structured and semi-structured data for analytical queries on GCP. Cost efficiency is important, and the solution must allow SQL-like queries without managing infrastructure. Which storage and query solution is most appropriate?

A) BigQuery

B) Cloud SQL

C) Cloud Bigtable

D) Dataproc with HDFS

Answer

A) BigQuery

Explanation

A) BigQuery is Google Cloud’s fully managed, serverless data warehouse that excels at analyzing large datasets. It can store structured and semi-structured data, including JSON, Avro, and Parquet formats. BigQuery allows running SQL-like queries using standard SQL syntax without managing infrastructure. Its columnar storage and query engine are optimized for petabyte-scale datasets, providing cost efficiency through on-demand query pricing or flat-rate options. BigQuery also automatically handles scaling, replication, and availability, so teams don’t need to worry about clusters, sharding, or hardware management. This combination of scalability, performance, and serverless architecture makes it the ideal choice for large-scale analytics with cost control.

B) Cloud SQL is a managed relational database suitable for transactional workloads. While it supports SQL queries, it is not designed for petabyte-scale analytics. Query performance would degrade with massive datasets, and cost would increase significantly due to scaling requirements. Cloud SQL requires managing instances, replication, and backups, which increases operational complexity. It is ideal for OLTP use cases but unsuitable for large-scale analytical queries.

C) Cloud Bigtable is a NoSQL wide-column database optimized for very large-scale, low-latency transactional workloads. It is excellent for time-series data, IoT, or key-value access patterns. However, it does not provide SQL-like querying and is not cost-efficient for analytical queries because its storage and access patterns are optimized for lookups, not aggregations or complex joins. Using Bigtable for large-scale analytics would require additional tools like Dataflow or custom MapReduce jobs, increasing complexity and cost.

D) Dataproc with HDFS is a managed Hadoop and Spark service on GCP. While it can handle large datasets and supports SQL-like queries through Hive or SparkSQL, it requires cluster management, including sizing, scaling, and optimization. Cost efficiency is lower because clusters must run continuously or be started and stopped with some overhead. BigQuery provides similar functionality in a fully serverless, managed manner without infrastructure overhead.

In BigQuery is the most appropriate solution because it allows SQL queries on petabyte-scale structured and semi-structured data with cost-efficient, serverless architecture. Other solutions either lack SQL support, scalability, or cost efficiency.

Q3

You are designing a machine learning pipeline on GCP that requires training models on massive datasets stored in Cloud Storage. You want to use distributed training without managing the underlying compute resources manually. Which service should you choose?

A) AI Platform Training (Vertex AI Training)

B) Cloud Functions

C) Cloud Run

D) Cloud Dataflow

Answer

A) AI Platform Training (Vertex AI Training)

Explanation

A) AI Platform Training, now Vertex AI Training, allows distributed machine learning model training on GCP without managing underlying compute infrastructure. It can scale automatically based on the dataset size and complexity of the training job. Vertex AI Training integrates seamlessly with Cloud Storage for input datasets, supports TensorFlow, PyTorch, and scikit-learn, and handles GPU or TPU provisioning for accelerated training. It also provides hyperparameter tuning, job monitoring, and logging, making distributed training straightforward for large-scale ML workloads. This removes the operational burden of manually setting up clusters or orchestrating parallel workloads, enabling teams to focus on model development and experimentation.

B) Cloud Functions is designed for lightweight, event-driven code execution. It cannot handle distributed training, GPU/TPU management, or large-scale datasets efficiently. Using Cloud Functions for training models would be impractical due to execution time limits and resource constraints.

C) Cloud Run is a container-based serverless platform for running stateless applications. While it is scalable and flexible, it does not support distributed ML training with GPU/TPU acceleration. It is better suited for serving models or running microservices, not performing compute-intensive training tasks.

D) Cloud Dataflow is optimized for stream and batch data processing. While it can be used to preprocess datasets before ML training, it is not designed for model training. Dataflow cannot execute distributed training algorithms natively and would require significant custom implementation, making it inefficient compared to Vertex AI Training.

In Vertex AI Training is the optimal choice because it provides managed distributed training with seamless scaling, GPU/TPU support, and integration with Cloud Storage. Other services are either too lightweight, not designed for ML training, or lack distributed computation capabilities.

Q4

A company needs to ensure that sensitive data stored in BigQuery is encrypted at rest using customer-managed encryption keys (CMEK). Which approach allows this requirement?

A) Create a Cloud KMS key and configure BigQuery dataset to use it

B) Enable default BigQuery encryption

C) Use Cloud Storage bucket with default encryption

D) Encrypt data manually before loading into BigQuery

Answer

A) Create a Cloud KMS key and configure BigQuery dataset to use it

Explanation

A) Creating a Cloud Key Management Service (KMS) key and configuring a BigQuery dataset to use it allows customer-managed encryption keys (CMEK) for data at rest. With CMEK, organizations maintain control over key rotation, revocation, and lifecycle. BigQuery integrates with Cloud KMS so that data is encrypted using the customer’s key rather than Google-managed default keys. This meets compliance and regulatory requirements for sensitive data protection. It also allows auditing key usage, setting IAM policies on key access, and ensures encryption standards are maintained consistently across datasets.

B) Enabling default BigQuery encryption uses Google-managed encryption keys. While data is encrypted at rest, organizations do not have control over key management, rotation, or revocation. This approach does not meet strict CMEK requirements, making it unsuitable for regulatory compliance scenarios requiring customer control over keys.

C) Using a Cloud Storage bucket with default encryption only applies to objects stored in Cloud Storage. It does not affect BigQuery datasets. While Cloud Storage supports CMEK, BigQuery requires separate CMEK configuration. Therefore, this approach does not satisfy the requirement for BigQuery encryption at rest.

D) Encrypting data manually before loading into BigQuery is technically possible, but it complicates querying. BigQuery cannot natively query encrypted blobs without decryption. This approach adds operational overhead and may degrade performance for analytical workloads. It is less practical than using CMEK directly with BigQuery.

In using a Cloud KMS key configured for a BigQuery dataset ensures CMEK encryption at rest, providing control, compliance, and native integration. Other methods either rely on Google-managed keys or introduce operational challenges.

Q5

You need to move 50 TB of on-premises relational data to BigQuery for analytics. Network bandwidth is limited, and the data transfer must minimize downtime. Which strategy is most appropriate?

A) Use BigQuery Data Transfer Service with Google Cloud Storage staging and parallel load jobs

B) Export data to CSV and upload manually

C) Use Cloud SQL replication directly to BigQuery

D) Use Cloud Pub/Sub to stream all data

Answer

A) Use BigQuery Data Transfer Service with Google Cloud Storage staging and parallel load jobs

Explanation

A) Using BigQuery Data Transfer Service (DTS) with Google Cloud Storage as staging, combined with parallel load jobs, is the most practical approach for large data transfers. Data Transfer Service can handle bulk ingestion from multiple sources efficiently. Staging data in Cloud Storage allows the transfer to be broken into parallelized chunks, maximizing throughput even with limited network bandwidth. It also allows incremental loading to minimize downtime, ensuring analytics can begin as soon as initial data is available. This method leverages managed services, error handling, and scalable ingestion patterns suitable for multi-terabyte datasets.

B) Exporting data to CSV and uploading manually is error-prone, slow, and does not scale well for 50 TB. Network constraints would cause long transfer times, and there is no built-in mechanism for incremental updates. Downtime could be significant, and manual management introduces operational risk.

C) Cloud SQL replication directly to BigQuery is not supported. While Cloud SQL can replicate to other databases, there is no native direct replication to BigQuery. Custom ETL would be required, increasing complexity and transfer time.

D) Using Cloud Pub/Sub to stream all data is impractical for bulk data transfer. Pub/Sub is optimized for real-time event streaming rather than terabyte-scale batch ingestion. Streaming 50 TB through Pub/Sub would be inefficient, costly, and slow, making it unsuitable for this migration.

In leveraging BigQuery Data Transfer Service with Cloud Storage staging and parallel load jobs ensures efficient, scalable, and low-downtime migration of large datasets to BigQuery. Other approaches are either too slow, manual, or operationally complex.

Q6

You are designing a data pipeline for aggregating clickstream events from a high-traffic website. The pipeline must support real-time analytics and alerting for unusual activity. Which architecture is most appropriate?

A) Cloud Pub/Sub → Dataflow → BigQuery → Data Studio

B) Cloud Storage → Dataproc → BigQuery

C) Cloud SQL → Cloud Functions → BigQuery

D) Bigtable → Cloud Run → BigQuery

Answer

A) Cloud Pub/Sub → Dataflow → BigQuery → Data Studio

Explanation

A) Using Cloud Pub/Sub, Dataflow, BigQuery, and Data Studio provides a fully managed, scalable, and real-time data pipeline suitable for high-traffic clickstream events. Cloud Pub/Sub collects events reliably from the website, ensuring no data loss even under spikes in traffic. Dataflow, using Apache Beam, processes these events in real-time, performing transformations, filtering, and aggregation. It supports windowed computations, which are crucial for time-based metrics and detecting unusual patterns. BigQuery stores aggregated and raw data, providing high-performance queries for analytics. Data Studio connects to BigQuery to visualize real-time dashboards and alerts. This combination satisfies requirements for low-latency processing, real-time analytics, and scalable alerting.

B) Cloud Storage → Dataproc → BigQuery is more suitable for batch processing. Cloud Storage ingests data in files, and Dataproc processes it using Spark or Hadoop. While this supports large-scale analytics, it is not optimized for real-time processing. There is inherent latency due to file-based ingestion and batch processing, which would hinder timely alerting for unusual activity.

C) Cloud SQL → Cloud Functions → BigQuery can handle lightweight events, but Cloud SQL cannot scale efficiently to high-traffic clickstream workloads. Cloud Functions can trigger processing, but complex aggregation over millions of events per second becomes challenging. There is also no native support for windowed computations or exactly-once semantics, which are often required in clickstream analytics.

D) Bigtable → Cloud Run → BigQuery is designed for low-latency transactional workloads, not real-time analytics pipelines. Bigtable can store large volumes of events, but performing aggregations and analytics across massive datasets requires additional processing. Cloud Run can host applications, but it is stateless and does not inherently provide distributed processing capabilities for real-time aggregation.

In the combination of Cloud Pub/Sub, Dataflow, BigQuery, and Data Studio is optimal for real-time clickstream aggregation, analytics, and alerting. Other options either introduce latency, lack scalability, or are operationally complex for this use case.

Q7

A company wants to implement a GDPR-compliant data pipeline on GCP. The pipeline must allow selective anonymization of personally identifiable information (PII) before storing it for analytics. Which service should be used for anonymizing sensitive data?

A) Cloud Data Loss Prevention (DLP)

B) Cloud KMS

C) Cloud Identity-Aware Proxy

D) Cloud Functions

Answer

A) Cloud Data Loss Prevention (DLP)

Explanation

A) Cloud Data Loss Prevention (DLP) is specifically designed to discover, classify, and anonymize sensitive data such as PII. DLP provides tools for masking, tokenization, and redaction, allowing selective anonymization before data is stored for analytics. It integrates with multiple data sources, including BigQuery, Cloud Storage, and Pub/Sub, ensuring that sensitive data is handled appropriately at ingestion or before querying. DLP also supports structured and unstructured data, making it flexible for pipelines handling JSON, CSV, or log data. Using DLP helps meet GDPR and other privacy regulations by allowing configurable anonymization policies while preserving data utility for analytics.

B) Cloud KMS is used for encryption and key management. While KMS secures data at rest, it does not provide data anonymization or masking. Encrypting PII with KMS ensures confidentiality but does not allow selective analysis without decryption, which makes it less suitable for analytics pipelines requiring anonymized data.

C) Cloud Identity-Aware Proxy (IAP) controls access to applications and services based on identity, enforcing authentication and authorization. IAP is useful for securing web applications or APIs but does not provide data anonymization or transformation features. It does not modify the underlying dataset to meet GDPR requirements.

D) Cloud Functions can be used to implement custom logic for anonymization, but doing so requires significant development effort and testing. Cloud Functions alone does not provide built-in detection or masking of sensitive data. Using DLP is more reliable and compliant because it offers predefined PII detectors and anonymization techniques.

In Cloud Data Loss Prevention (DLP) is the correct choice because it is purpose-built for discovering, classifying, and anonymizing sensitive data in compliance with regulations like GDPR. Other services either focus on encryption, access control, or require custom implementation, making them less suitable for automated and compliant data anonymization.

Q8

Your team needs to implement a solution that monitors BigQuery queries and alerts when unusually high-cost queries are detected. The solution should be automated and serverless. Which approach is most appropriate?

A) Use BigQuery INFORMATION_SCHEMA with Cloud Functions and Pub/Sub alerts

B) Query BigQuery directly in Cloud SQL and trigger alerts manually

C) Export logs to Cloud Storage and analyze offline

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

Answer

A) Use BigQuery INFORMATION_SCHEMA with Cloud Functions and Pub/Sub alerts

Explanation

A) BigQuery provides INFORMATION_SCHEMA tables that expose metadata about queries, jobs, and billing information. By querying these tables, teams can monitor query cost, runtime, and other metrics in near real-time. Cloud Functions can run periodically or be triggered by events to process this metadata, compare it to thresholds, and send alerts via Pub/Sub or email. This setup is fully serverless, requiring no infrastructure management, and can scale to monitor thousands of queries automatically. It ensures timely detection of unusually expensive queries and enables automated alerting and auditing.

B) Querying BigQuery in Cloud SQL and triggering alerts manually introduces unnecessary complexity. Cloud SQL is not designed for storing or processing large volumes of query metadata from BigQuery. Manual alerting also does not provide the automation or scalability required to monitor high-cost queries efficiently.

C) Exporting logs to Cloud Storage and analyzing offline is a batch-oriented approach. While it can identify expensive queries, it introduces latency, meaning alerts will not be in real-time. This does not satisfy the requirement for automated, timely monitoring of query costs.

D) Using Cloud Bigtable to store query metadata and polling for alerts is operationally complex. Bigtable is optimized for high-throughput transactional workloads but does not provide built-in integration with BigQuery metadata. Additional pipelines are needed to extract, transform, and load metadata, increasing operational overhead. It also does not provide a simple serverless solution like Cloud Functions + Pub/Sub.

In querying BigQuery INFORMATION_SCHEMA with Cloud Functions and Pub/Sub provides a serverless, scalable, and automated solution for monitoring high-cost queries, meeting both technical and operational requirements. Other approaches are either manual, slow, or unnecessarily complex.

Q9

You are tasked with designing a GCP data warehouse solution for a global company. The dataset includes time-series metrics from IoT devices. Queries will be frequent, and latency must be low. The company expects high concurrency and petabyte-scale storage. Which storage solution is most appropriate?

A) BigQuery

B) Cloud SQL

C) Cloud Bigtable

D) Firestore

Answer

A) BigQuery

Explanation

A) BigQuery is a fully managed, serverless data warehouse designed for petabyte-scale datasets. It uses columnar storage and a distributed query engine optimized for analytics workloads. For time-series data from IoT devices, BigQuery can efficiently perform aggregations, filtering, and joins, providing low-latency queries even under high concurrency. Its serverless architecture scales automatically to handle thousands of concurrent queries without provisioning infrastructure. BigQuery also supports partitioned and clustered tables, which improves query performance on time-series data. It allows analysts to perform complex queries while keeping operational costs predictable through on-demand or flat-rate pricing models.

B) Cloud SQL is a managed relational database suitable for transactional workloads. While it can handle small-scale time-series data, it is not optimized for petabyte-scale storage or high-concurrency analytical queries. Query performance degrades significantly as data volume grows, and scaling requires manual instance resizing and replication, adding operational overhead.

C) Cloud Bigtable is a NoSQL wide-column database optimized for low-latency read/write operations. While it excels at storing large-scale time-series data, it does not support SQL-like queries for analytics. Aggregations, joins, and complex queries require additional processing, often using Dataflow or Spark. Bigtable is excellent for ingestion and retrieval but not ideal as a data warehouse for analytical workloads requiring low-latency queries.

D) Firestore is a NoSQL document database for transactional workloads and real-time apps. It is not designed for large-scale analytics or time-series data aggregation. Query capabilities are limited compared to BigQuery, and scaling to petabyte-level datasets is not practical.

In BigQuery is the best choice for a data warehouse that handles petabyte-scale time-series data with low-latency queries, high concurrency, and analytics-focused workloads. Other options either lack analytical query support, cannot scale efficiently, or are better suited for transactional use cases.

Q10

A company wants to ensure that its streaming analytics pipeline can tolerate sudden spikes in traffic without losing messages. Which GCP service is most suitable for decoupling ingestion from processing to achieve high reliability?

A) Cloud Pub/Sub

B) Cloud Storage

C) Cloud SQL

D) Cloud Memorystore

Answer

A) Cloud Pub/Sub

Explanation

A) Cloud Pub/Sub is a fully managed messaging service that allows decoupling of data producers and consumers. It can ingest large volumes of messages from multiple sources and deliver them to processing pipelines reliably. Pub/Sub automatically scales to handle traffic spikes, ensuring messages are buffered if downstream processing is slower than ingestion. It guarantees at-least-once delivery and, when combined with Dataflow, can achieve exactly-once processing semantics. This architecture provides fault tolerance and high reliability for streaming analytics pipelines, ensuring no messages are lost during sudden spikes in traffic.

B) Cloud Storage is object storage suitable for batch ingestion. While it can hold large amounts of data, it is not optimized for real-time streaming or decoupling high-throughput producers and consumers. Using Cloud Storage would require additional polling or batch jobs, introducing latency and potential data loss under spikes.

C) Cloud SQL is a relational database for transactional workloads. It cannot reliably buffer high-throughput streaming events. Sudden traffic spikes may overwhelm connections and lead to failed writes, making it unsuitable for high-reliability message ingestion in streaming pipelines.

D) Cloud Memorystore is an in-memory caching service using Redis or Memcached. While it is fast, it is not designed for durable message storage or streaming ingestion. Data stored in Memorystore can be lost if the instance fails, and it does not provide message delivery guarantees needed for reliable streaming pipelines.

In Cloud Pub/Sub is the most suitable service for decoupling ingestion from processing, providing durability, automatic scaling, and reliable message delivery, which are essential for handling spikes in streaming analytics pipelines. Other services lack durability, reliability, or scalability for this use case.

Q11

Your company wants to implement an ETL pipeline that reads structured and semi-structured data from Cloud Storage, transforms it, and loads it into BigQuery for analytics. The team wants a fully managed, scalable, serverless solution. Which service should be used for the transformation step?

A) Dataflow

B) Dataproc

C) Cloud Functions

D) Cloud SQL

Answer

A) Dataflow

Explanation

A) Dataflow is a fully managed, serverless service for both batch and streaming data processing. It integrates natively with Cloud Storage as a source and BigQuery as a sink, making it ideal for ETL pipelines. Dataflow supports complex transformations, aggregations, and filtering using the Apache Beam SDK. It handles dynamic scaling, checkpointing, and fault tolerance automatically. With Dataflow, the team does not need to provision or manage clusters, which aligns perfectly with the requirement for a fully managed and serverless solution. It can handle both structured and semi-structured data, such as JSON, Avro, and CSV, providing flexibility for diverse ETL scenarios.

B) Dataproc is a managed Hadoop and Spark service. While it can perform ETL transformations, it requires cluster management, which introduces operational overhead. Clusters need to be provisioned, sized, and monitored, and although autoscaling is available, it is not as seamless or serverless as Dataflow. For teams prioritizing a serverless, fully managed approach, Dataproc adds unnecessary complexity.

C) Cloud Functions can perform simple transformations on small datasets or individual events. However, it is not suitable for large-scale ETL because of execution time limits, stateless nature, and lack of built-in support for complex batch processing. Cloud Functions are better suited for event-driven pipelines rather than full ETL workflows at scale.

D) Cloud SQL is a managed relational database. It is not a data processing service and cannot perform scalable ETL transformations on large datasets. Using Cloud SQL for transformations would require external processing logic, which increases operational complexity and does not satisfy the requirement for a serverless ETL solution.

In  Dataflow is the best choice for a fully managed, scalable, serverless ETL solution. It integrates seamlessly with Cloud Storage and BigQuery, supports complex transformations, and handles batch and streaming workloads without infrastructure management. Other options either require cluster management, are unsuitable for large-scale processing, or lack serverless capabilities.

Q12

You need to design a BigQuery solution for analyzing IoT sensor data, with frequent queries that include filtering, aggregation, and time-based analysis. The dataset is expected to grow to petabytes. Which design strategy will optimize performance and cost?

A) Partition tables by ingestion time and cluster by sensor ID

B) Store all data in a single unpartitioned table

C) Partition tables by sensor ID and cluster by timestamp

D) Use multiple small tables per day without partitioning

Answer

A) Partition tables by ingestion time and cluster by sensor ID

Explanation

A) Partitioning by ingestion time allows BigQuery to scan only relevant partitions for time-based queries, which reduces query cost and improves performance. Clustering by sensor ID ensures that data for the same sensor is stored physically close together, making filtering and aggregations on sensor-specific queries more efficient. This combination is optimal for large time-series datasets because it minimizes scanned data, lowers cost, and accelerates query performance for frequent analytics, especially when queries often filter by sensor and time range.

B) Storing all data in a single unpartitioned table is inefficient for petabyte-scale datasets. Queries would scan the entire table even for small date ranges or subsets of sensors, resulting in higher costs and longer query times. This design does not leverage BigQuery’s partitioning or clustering features, which are essential for large datasets.

C) Partitioning by sensor ID is not ideal because sensor IDs are typically numerous and may have uneven data distribution. This can lead to many small partitions, causing poor performance and increased metadata overhead. Clustering by timestamp provides limited performance benefit for time-based queries, and partitioning should primarily be based on query patterns like ingestion time.

D) Using multiple small tables per day without partitioning increases management overhead and can complicate queries. Queries across multiple tables require table unions or scripting, which is less efficient than using partitioned tables. It also increases the chance of errors and does not scale well for petabyte datasets.

In  partitioning by ingestion time and clustering by sensor ID optimizes BigQuery performance and cost. It aligns with typical IoT query patterns of filtering by time and sensor, while avoiding the inefficiencies of unpartitioned or per-sensor partitioning strategies.

Q13

A company wants to build a serverless machine learning pipeline on GCP to preprocess, train, and deploy models. They want to minimize infrastructure management and integrate with other GCP services. Which architecture is most appropriate?

A) Cloud Storage → Vertex AI Pipelines → Vertex AI Training → Vertex AI Endpoint

B) Cloud Functions → Cloud Run → Cloud SQL

C) Dataproc → AI Platform Training → Cloud ML Engine

D) BigQuery → Cloud Dataflow → Cloud SQL

Answer

A) Cloud Storage → Vertex AI Pipelines → Vertex AI Training → Vertex AI Endpoint

Explanation

A) This architecture leverages fully managed services to create a serverless ML pipeline. Cloud Storage acts as the central data repository for raw datasets. Vertex AI Pipelines orchestrates preprocessing, training, and deployment steps with automated pipeline management, versioning, and monitoring. Vertex AI Training allows distributed model training with support for GPUs/TPUs, handling scalability and infrastructure concerns automatically. Vertex AI Endpoint serves the trained models for online inference with autoscaling and low latency. This architecture integrates tightly with other GCP services and reduces operational complexity, providing a fully serverless, end-to-end ML solution.

B) Cloud Functions → Cloud Run → Cloud SQL is not suitable for large-scale ML pipelines. Cloud Functions are limited in execution time and resources, making preprocessing or training on large datasets impractical. Cloud Run is stateless and cannot manage distributed training effectively. Cloud SQL is a transactional database, not optimized for ML storage or model management. This architecture lacks serverless ML orchestration and scalability.

C) Dataproc → AI Platform Training → Cloud ML Engine is partially managed but not fully serverless. Dataproc requires cluster management for preprocessing, adding operational overhead. While AI Platform Training can handle distributed training, combining it with Dataproc introduces more complexity and manual scaling than using Vertex AI Pipelines. Cloud ML Engine is now part of Vertex AI, making this approach less current and less streamlined.

D) BigQuery → Cloud Dataflow → Cloud SQL focuses on analytics pipelines rather than ML workflows. BigQuery can analyze data, and Dataflow can preprocess it, but Cloud SQL is not suitable for storing models or serving predictions. This architecture does not provide a complete ML training and deployment solution.

In  using Cloud Storage, Vertex AI Pipelines, Vertex AI Training, and Vertex AI Endpoint provides a fully serverless, integrated, and scalable architecture for ML pipelines. It minimizes infrastructure management while supporting preprocessing, training, and deployment. Other options either require manual cluster management, are unsuitable for ML workloads, or do not provide end-to-end automation.

Q14

You are designing a BigQuery solution to support a multi-tenant SaaS application. Each tenant has separate datasets, but queries must aggregate across tenants efficiently. Which design pattern is most appropriate?

A) Use a single table with a tenant ID column and cluster by tenant ID

B) Create separate BigQuery projects per tenant

C) Store data in Cloud SQL and replicate to BigQuery

D) Use multiple unpartitioned tables per tenant

Answer

A) Use a single table with a tenant ID column and cluster by tenant ID

Explanation

A) Using a single table with a tenant ID column and clustering by tenant ID provides an efficient multi-tenant design. Queries filtering or aggregating by tenant can benefit from clustering, which organizes data physically on disk by tenant ID. This reduces scanned data, improves performance, and lowers query costs. Maintaining a single table simplifies schema evolution, management, and analytics across tenants. It also supports ad hoc queries spanning multiple tenants for cross-tenant reporting or benchmarking.

B) Creating separate BigQuery projects per tenant introduces significant operational overhead. Each project requires separate IAM management, billing, dataset management, and monitoring. Cross-tenant queries become complex and expensive, requiring either table unions or federated queries. This approach is not scalable for hundreds or thousands of tenants.

C) Storing data in Cloud SQL and replicating to BigQuery is inefficient for multi-tenant analytics. Cloud SQL is not designed for petabyte-scale data or high-concurrency queries. Replicating to BigQuery adds complexity and latency, and managing multiple tenants across both systems increases operational burden.

D) Using multiple unpartitioned tables per tenant leads to similar challenges as separate projects. Table management, schema evolution, and cross-tenant queries become cumbersome. Query costs increase because BigQuery must scan multiple tables rather than leveraging partitioning and clustering in a single table.

In   a single BigQuery table with a tenant ID column and clustering provides an efficient, scalable, and maintainable solution for multi-tenant SaaS analytics. Other approaches introduce operational complexity, scalability challenges, or higher query costs.

Q15

A team is building a GCP pipeline to ingest streaming logs from multiple applications, transform them, and store them for analytics. They require low-latency ingestion, fault tolerance, and exactly-once processing semantics. 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 durable, highly scalable message broker that buffers streaming logs and handles spikes in traffic. Dataflow processes the events using Apache Beam, providing exactly-once processing semantics through stateful processing and deduplication. Dataflow supports low-latency stream processing with windowed aggregations, transformations, and enrichment. Finally, BigQuery serves as the analytics store, optimized for fast querying of large datasets. This architecture provides a fault-tolerant, fully managed, scalable, and serverless solution suitable for ingesting and analyzing streaming logs in real time.

B) Cloud Storage → Dataproc → BigQuery is more suitable for batch ingestion. Cloud Storage stores logs as files, Dataproc processes them in batch mode, and then BigQuery ingests the processed data. This approach introduces latency and does not guarantee exactly-once semantics for streaming events, making it unsuitable for low-latency streaming analytics.

C) Cloud SQL → Cloud Functions → BigQuery cannot scale for high-throughput streaming logs. Cloud SQL is transactional and cannot handle large volumes efficiently. Cloud Functions are stateless with execution limits, making them unsuitable for exactly-once semantics and stateful processing.

D) Bigtable → Cloud Run → BigQuery can store raw logs efficiently, but Cloud Run is stateless and does not provide exactly-once processing or stream processing capabilities. Aggregations and transformations require additional pipelines, increasing operational complexity.

In   using Cloud Pub/Sub, Dataflow, and BigQuery provides a fully managed, serverless, fault-tolerant architecture with exactly-once processing and low-latency analytics. Other architectures either rely on batch processing, lack scalability, or cannot guarantee exactly-once semantics.

Q16

You are designing a pipeline to transfer large amounts of on-premises relational data to BigQuery. The network bandwidth is limited, and downtime must be minimized. Which approach is most appropriate?

A) BigQuery Data Transfer Service with Cloud Storage staging and parallel load jobs

B) Export data to CSV and manually upload

C) Cloud SQL replication directly to BigQuery

D) Stream data continuously using Cloud Pub/Sub

Answer

A) BigQuery Data Transfer Service with Cloud Storage staging and parallel load jobs

Explanation

A) BigQuery Data Transfer Service (DTS) allows for automated and managed bulk data transfer to BigQuery. By staging the data in Cloud Storage, the transfer can be split into parallel load jobs to maximize throughput even under limited network bandwidth. DTS supports incremental loading, which enables minimal downtime because only new or changed data is transferred after the initial load. It is fully managed, handles retries and errors, and provides visibility into job status, making it a reliable choice for moving large datasets.

B) Exporting data to CSV and manually uploading is slow and error-prone. For multi-terabyte datasets, this approach would be impractical due to limited bandwidth, operational overhead, and risk of data corruption or missed files. Manual uploads cannot efficiently support incremental loads or minimize downtime.

C) Cloud SQL replication directly to BigQuery is not natively supported. Cloud SQL can replicate to other databases but does not provide a direct pipeline to BigQuery. Implementing such a solution would require complex custom ETL, adding operational risk and increasing downtime during migration.

D) Streaming data continuously through Cloud Pub/Sub is better suited for real-time events rather than bulk historical data migration. Transferring large volumes like tens of terabytes through Pub/Sub is inefficient, potentially costly, and prone to throttling or message loss under bandwidth constraints.

Q17

You are designing a BigQuery dataset to store user activity logs. Queries often filter by event date and user ID, and the table is expected to grow to petabytes. Which table design optimizes performance and cost?

A) Partition by event date and cluster by user ID

B) Partition by user ID and cluster by event date

C) Store all data in a single unpartitioned table

D) Create separate tables for each user

Answer

A) Partition by event date and cluster by user ID

Explanation

A) Partitioning by event date enables queries that filter by time to scan only relevant partitions, reducing query cost and improving performance. Clustering by user ID organizes the data physically on disk for efficient filtering and aggregation on a per-user basis. This combination is ideal for large-scale time-series data with frequent filtering on both date and user, providing cost efficiency and faster query performance for petabyte-scale datasets.

B) Partitioning by user ID is not ideal because there could be millions of users, creating an excessive number of small partitions. This leads to poor performance and metadata overhead. Clustering by event date provides limited benefit because most queries filter by both user ID and event date.

C) Storing all data in a single unpartitioned table is inefficient at petabyte scale. Queries filtering by date or user would scan the entire dataset, resulting in higher costs and slower performance.

D) Creating separate tables for each user introduces significant operational complexity. Managing thousands or millions of tables becomes unmanageable, and cross-user queries require unions or scripting, which is less efficient and more error-prone than using a partitioned and clustered table.

Q18

A company wants to build a real-time analytics pipeline to detect fraud in financial transactions. The system must scale automatically, process high-volume streaming data, and provide exactly-once processing semantics. Which architecture is most suitable?

A) Cloud Pub/Sub → Dataflow → BigQuery

B) Cloud SQL → Cloud Functions → BigQuery

C) Cloud Storage → Dataproc → BigQuery

D) Bigtable → Cloud Run → BigQuery

Answer

A) Cloud Pub/Sub → Dataflow → BigQuery

Explanation

A) Cloud Pub/Sub ingests high-volume streaming data reliably and buffers messages to handle spikes in traffic. Dataflow processes data in real-time using Apache Beam, providing exactly-once processing semantics with stateful transformations and deduplication. Windowed operations allow timely detection of fraud patterns, aggregations, and anomaly detection. BigQuery serves as a scalable analytics store for both raw and aggregated data, enabling queries and dashboards for monitoring and reporting. This architecture is fully managed, serverless, and can automatically scale with incoming data, meeting all real-time analytics requirements.

B) Cloud SQL → Cloud Functions → BigQuery cannot scale efficiently for high-volume streaming data. Cloud SQL is designed for transactional workloads and would become a bottleneck. Cloud Functions are stateless and have execution time limits, making them unsuitable for large-scale, exactly-once stream processing.

C) Cloud Storage → Dataproc → BigQuery is batch-oriented. Cloud Storage collects data in files, and Dataproc processes them in batches. This introduces latency, making real-time fraud detection difficult, and does not inherently support exactly-once semantics.

D) Bigtable → Cloud Run → BigQuery can store large amounts of data, but Cloud Run is stateless and not designed for distributed stream processing. Processing and aggregation logic must be implemented manually, increasing complexity, and exactly-once guarantees are difficult to achieve without additional frameworks.

Q19

You are tasked with implementing GDPR-compliant data handling in BigQuery. The dataset contains personally identifiable information (PII) that must be anonymized before analytics. Which service is best suited for this purpose?

A) Cloud Data Loss Prevention (DLP)

B) Cloud KMS

C) Cloud Identity-Aware Proxy

D) Cloud Functions

Answer

A) Cloud Data Loss Prevention (DLP)

Explanation

A) Cloud Data Loss Prevention (DLP) is designed for discovering, classifying, and anonymizing sensitive data. It provides tools such as masking, tokenization, redaction, and pseudonymization, enabling teams to selectively anonymize PII before storing or analyzing data in BigQuery. DLP integrates directly with BigQuery, Cloud Storage, and Pub/Sub, allowing automated anonymization during data ingestion or query execution. It supports structured and unstructured data and provides audit logs for compliance reporting, making it suitable for GDPR requirements.

B) Cloud KMS provides encryption key management, which secures data at rest but does not anonymize or mask data for analytics. While KMS protects confidentiality, analysts would still need decrypted data to query, which does not satisfy GDPR requirements for selective anonymization.

C) Cloud Identity-Aware Proxy (IAP) secures access to applications by enforcing authentication and authorization. It does not perform data anonymization or masking and is unrelated to transforming PII for analytics.

D) Cloud Functions can implement custom anonymization logic, but this requires manual development and testing. There are no built-in PII detection or masking features, making it less reliable and more error-prone compared to using Cloud DLP.

Q20

A team needs to monitor BigQuery for unusually high-cost queries and generate alerts automatically. The solution must be serverless and scalable. Which approach is most appropriate?

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

B) Query BigQuery directly in Cloud SQL and trigger alerts manually

C) Export logs to Cloud Storage and analyze offline

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

Answer

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

Explanation

A) BigQuery’s INFORMATION_SCHEMA tables provide metadata about queries, including cost, execution time, and job details. By periodically querying these tables, Cloud Functions can detect unusually expensive queries in near real-time. Cloud Functions can then publish alerts to Pub/Sub or send notifications via email or other channels. This approach is serverless, scales automatically, and requires no infrastructure management. It allows teams to monitor query costs proactively and respond immediately to anomalies.

B) Querying BigQuery from Cloud SQL and triggering alerts manually introduces unnecessary complexity. Cloud SQL is not designed to store large volumes of query metadata efficiently, and manual monitoring does not provide real-time alerting.

C) Exporting logs to Cloud Storage and analyzing offline introduces latency. Alerts will not be generated in near real-time, which is a significant drawback when monitoring high-cost queries.

D) Using Cloud Bigtable to store query metadata and poll for alerts is operationally complex. Bigtable is optimized for transactional workloads and does not natively integrate with BigQuery metadata. Implementing polling and alerting logic adds unnecessary complexity compared to the serverless Cloud Functions approach.