Professional Data Engineer Professional Data Engineer on Google Cloud Platform Free Practice Test — 30 Questions

Question 1

A multinational e-commerce company, "NovaCart," initially designed its daily sales anomaly detection system using a Dataproc cluster to process large volumes of transaction data. This batch-oriented approach, running nightly, proved effective for identifying significant deviations in sales patterns. However, a recent business mandate requires the system to identify anomalies in near real-time, with a target latency of under one minute, to enable immediate customer service interventions. The data ingestion point is Google Cloud Pub/Sub, and the processed anomaly data needs to be stored in BigQuery tables. The engineering team must adapt their strategy to meet these new stringent requirements while maintaining operational efficiency and considering the existing GCP infrastructure.

Which strategic adjustment to the data processing architecture would best address NovaCart\'s evolving needs?

Accepted Answer

Transition the anomaly detection pipeline to Cloud Dataflow, utilizing its unified stream and batch processing capabilities to ingest data from Pub/Sub, process it with a low-latency streaming pattern, and write results to BigQuery.

Question 2

A Google Cloud data engineering team, tasked with building a real-time customer churn prediction pipeline for a global fintech company, is suddenly informed of an impending industry-wide regulatory mandate requiring enhanced data anonymization and immutable audit trails for all personally identifiable information (PII) processed. This mandate is set to take effect in six months, but the specific technical implementation details are still being finalized by the regulatory body. The team\'s current project roadmap is heavily focused on feature engineering and model training, with minimal emphasis on advanced anonymization techniques beyond basic masking. Given this sudden shift in strategic direction and the inherent ambiguity surrounding the precise technical requirements of the new regulation, what would be the most prudent and effective course of action for the data engineering lead to ensure project success and client satisfaction?

Accepted Answer

Initiate a cross-functional working group to thoroughly research the emerging regulatory requirements, assess the impact on the existing pipeline architecture, and collaboratively develop a phased approach for implementing robust anonymization and audit trail capabilities, prioritizing critical data elements first while maintaining open communication with the client regarding potential timeline adjustments.

Question 3

A multinational e-commerce company, operating on Google Cloud Platform, receives a legally binding request from a customer, exercising their \"right to be forgotten\" under global data privacy regulations. The customer\'s personal data is distributed across various GCP services, including BigQuery for transactional data, Cloud Storage for product images associated with customer orders, and Cloud Logging for audit trails of user activity. As the lead data engineer responsible for compliance, you must devise a strategy to fulfill this request. Which of the following approaches best ensures the complete and verifiable removal of the customer\'s personal data while adhering to potential data retention policies and regulatory obligations?

Accepted Answer

Execute `DELETE` statements for the customer's records in BigQuery, implement object versioning and lifecycle policies on Cloud Storage to delete associated images after a defined period, and rely on Cloud Logging's default retention to eventually purge the audit data.

Question 4

Anya, a lead data engineer on Google Cloud Platform, is overseeing a mission-critical real-time data pipeline that feeds a fraud detection system. During a high-volume trading period, the pipeline abruptly stops ingesting data into BigQuery, threatening significant financial losses. The incident response plan is unclear due to the unprecedented nature of the failure. Which of the following actions best demonstrates Anya\'s ability to adapt, lead, and solve problems under extreme pressure in this scenario?

Accepted Answer

Initiate immediate efforts to restore partial data availability by rerouting essential data streams to a secondary, less performant BigQuery dataset, while concurrently tasking a senior engineer to explore architectural changes for parallel processing to enhance future resilience.

Question 5

During a critical operational period, a data engineering team on Google Cloud Platform observes a sudden, unprecedented 500% increase in inbound event data for a key customer-facing analytics dashboard. The existing streaming ingestion pipelines into BigQuery are beginning to show signs of strain, with increased latency and a growing backlog of unprocessed events, threatening data freshness and accuracy. The team needs to implement an immediate, robust solution that can handle this surge while also laying the groundwork for future resilience. Which architectural approach would best address this immediate crisis and demonstrate strong adaptability to fluctuating data volumes?

Accepted Answer

Implement a Pub/Sub topic to buffer the incoming data, coupled with a Dataflow streaming job configured for auto-scaling to process and ingest the data into BigQuery, while simultaneously initiating an investigation into the root cause of the surge and its predictability.

Question 6

A critical data pipeline, recently migrated to a new streaming ingestion framework on Google Cloud Platform, has unexpectedly failed during peak processing hours. This failure has halted the delivery of vital data to downstream analytical dashboards, jeopardizing adherence to strict business SLAs for data freshness. Initial investigation reveals no obvious configuration errors in the new framework\'s core components, such as Pub/Sub subscriptions or Dataflow job templates. The team is under immense pressure to restore service rapidly, but also needs to ensure data integrity and prevent future incidents. Which of the following approaches best balances immediate recovery needs with long-term system stability and demonstrates effective problem-solving and adaptability in a high-pressure, ambiguous situation?

Accepted Answer

Immediately revert the entire pipeline to the previous, stable architecture while simultaneously initiating a deep dive into the new framework's logs and documentation to identify and rectify the root cause, communicating progress transparently to stakeholders.

Question 7

An industry-wide regulatory update mandates enhanced data anonymization and access control for all financial transaction data processed within the next quarter. Your data engineering team has built a robust ETL pipeline on Google Cloud Platform utilizing Dataproc for large-scale data transformations and BigQuery for storage. The new regulation introduces stringent requirements for de-identifying Personally Identifiable Information (PII) and implementing role-based access controls at a granular level, impacting data lineage and auditability. Considering the need to rapidly adapt, which of the following approaches best demonstrates the required behavioral competencies and technical proficiency for this situation?

Accepted Answer

Proactively research and implement Cloud DLP transformations within the Dataproc jobs to automatically de-identify PII before data lands in BigQuery, concurrently redesigning BigQuery IAM roles to enforce granular access based on the new regulatory classifications, and communicating these technical changes and their business implications clearly to stakeholders.

Question 8

A global retail giant experiences a catastrophic failure in its real-time inventory update pipeline during the Black Friday sales blitz. The core processing engine, a Dataproc cluster, has stalled, halting the ingestion of critical sales data into BigQuery and consequently preventing accurate stock level displays for customers. The operations team is reporting a surge in customer complaints related to incorrect product availability. As the lead data engineer responsible for this critical system, what is the most immediate and effective course of action to mitigate the escalating crisis and restore service?

Accepted Answer

Initiate an immediate restart of the affected Dataproc cluster, coupled with a review of its recent job logs to identify the specific error causing the halt, while concurrently alerting the platform engineering team to potential downstream BigQuery ingestion issues.

Question 9

A data engineering team is undertaking a significant migration of a legacy on-premises data warehouse to Google Cloud Platform, aiming to leverage BigQuery for analytics and Cloud Data Fusion for ETL processes. The project is encountering substantial scope creep due to newly identified business intelligence needs and a lack of precisely defined success metrics for the migration\'s outcome. Simultaneously, the team is struggling with integrating data from several disparate, legacy operational systems, leading to data quality issues and integration bottlenecks. Business stakeholders express concern about the project\'s timeline and tangible benefits, indicating a communication gap regarding the technical complexities and progress. Which strategic approach best equips the data engineering lead to manage these evolving challenges, ensure project success, and maintain stakeholder confidence?

Accepted Answer

Establish a formal change control process with a dedicated review board for new requirements, define quantifiable Key Performance Indicators (KPIs) and Service Level Agreements (SLAs) for data availability and query performance, and adopt an iterative development methodology with frequent stakeholder demos and feedback loops.

Question 10

A data engineering team, tasked with adhering to a newly implemented, stringent data governance framework requiring comprehensive data lineage tracking and robust anonymization of personal identifiable information (PII), is evaluating its migration strategy to Google Cloud Platform. The team currently operates a legacy, on-premises data warehouse with rudimentary lineage capabilities and manual PII handling processes. The team lead, tasked with guiding this transition, must select a GCP-centric approach that not only meets regulatory mandates but also fosters agility and scalability. Considering the inherent ambiguity in the new framework\'s implementation specifics for cloud environments and the imperative to maintain operational continuity, which of the following strategic directions would best position the team for success on GCP?

Accepted Answer

Implement a cloud-native data architecture on GCP, leveraging services like Data Loss Prevention (DLP) API for anonymization, Cloud Data Catalog for lineage, and Cloud Dataflow for data transformation, while progressively migrating workloads.

Question 11

A multinational corporation\'s data engineering team is responsible for a real-time analytics pipeline on Google Cloud Platform. This pipeline ingests data from various global sources, processes it using Dataflow, and stores it in a central BigQuery dataset. A critical downstream consumer is a Looker dashboard that provides sales performance metrics. Recently, one of the primary data sources, a legacy CRM system, has undergone an unscheduled update, introducing new fields related to customer engagement scores and regional performance indicators. This schema change has caused Dataflow jobs to fail upon attempting to write to the BigQuery table, disrupting the Looker dashboard\'s data feed. The team needs a solution that accommodates these new fields without compromising data integrity or the operational continuity of the analytics pipeline.

Accepted Answer

Enable schema auto-update in the BigQuery destination table, allowing new fields to be added dynamically as they appear in the incoming data stream from Dataflow.

Question 12

Anya, a lead data engineer on Google Cloud Platform, is alerted to a critical incident: a newly deployed batch processing pipeline, designed to ingest and transform sensitive financial transaction data, is exhibiting erratic behavior, leading to data inconsistencies and potential breaches of the General Data Protection Regulation (GDPR). The incident response protocol mandates immediate action. Anya must decide on the most prudent initial course of action to mitigate risks, considering both technical stability and legal obligations.

Accepted Answer

Initiate a comprehensive root cause analysis of the pipeline's architecture and code, concurrently engaging the legal and compliance departments to assess GDPR implications and potential breach notification requirements.

Question 13

Anya, a seasoned data engineering lead, is orchestrating a critical migration of a substantial legacy data warehouse to Google Cloud Platform. Her team, comprised of individuals with deep expertise in the existing on-premises infrastructure, is exhibiting significant apprehension towards adopting new cloud-native services like BigQuery and Dataflow. This apprehension is manifesting as a reluctance to embrace modern ETL/ELT patterns and a general skepticism regarding the efficacy of the new platform. Anya recognizes that the success of this migration hinges not only on technical execution but also on fostering a culture of adaptability and encouraging her team to embrace new methodologies and navigate the inherent ambiguities of such a significant technological shift. Which of the following strategies would most effectively address the team\'s resistance and promote a smooth transition, aligning with the principles of adaptability, flexibility, and collaborative problem-solving?

Accepted Answer

Facilitating hands-on workshops with curated GCP sandbox environments and establishing a mentorship program pairing experienced cloud engineers with legacy system experts.

Question 14

A large financial institution is undertaking a significant modernization effort to migrate its core customer data warehouse from an on-premises SQL Server instance to Google Cloud Platform. The existing data warehouse is a monolithic structure, updated via nightly batch ETL processes managed by custom scripts and an outdated scheduling tool. The target architecture on GCP will utilize BigQuery for analytics, Dataflow for processing both batch and streaming data, Cloud Storage for staging raw data, and Dataproc for legacy Spark workloads. The primary concerns are minimizing downtime for customer-facing applications that rely on the data warehouse and ensuring absolute data integrity and transactional consistency throughout the migration process. Considering the critical nature of financial data and regulatory compliance requirements (e.g., SOX, GDPR for customer data privacy), which of the following strategies is the most paramount for maintaining operational continuity and data integrity during the transition?

Accepted Answer

Implement a robust Change Data Capture (CDC) mechanism coupled with a reliable data pipeline for incremental updates and continuous validation of data consistency between the legacy system and BigQuery.

Question 15

Anya, a lead data engineer on GCP, is alerted to a critical production incident: a real-time data pipeline ingesting telemetry from a vast network of industrial sensors into BigQuery via Pub/Sub and Dataflow is experiencing significant latency and intermittent data loss. Monitoring dashboards reveal a rapidly increasing Pub/Sub subscription lag and a rise in unacknowledged message counts. The team suspects the Dataflow job, processing millions of events per minute, is struggling to keep up with the ingestion rate, possibly due to an unexpected surge in message complexity or a transient resource contention. To restore service quickly and prevent further data degradation, which dual approach would be most effective for immediate mitigation and subsequent analysis?

Accepted Answer

Dynamically scale up the Dataflow job's worker count and configure a Pub/Sub dead-letter topic for messages that fail repeated processing attempts.

Question 16

A rapidly growing e-commerce firm, previously reliant on an on-premises data warehouse, has recently pivoted its strategy to a cloud-native, data-driven operational model. As the lead data engineer, you are tasked with migrating a complex, multi-terabyte customer transaction dataset to Google Cloud Platform (GCP) while ensuring near real-time availability for critical business analytics dashboards. The project timeline is aggressive, and initial requirements regarding specific data transformation logic for new analytical models are somewhat ambiguous due to ongoing research by the business intelligence team. You must also contend with potential legacy system constraints and ensure robust data governance throughout the migration. Which of the following approaches best exemplifies the required adaptability, problem-solving, and strategic thinking to navigate this situation effectively?

Accepted Answer

Implement a phased migration using Cloud Data Fusion for ETL, BigQuery for data warehousing, and Cloud Dataflow for both batch and streaming processing, while establishing frequent, iterative feedback sessions with the business intelligence team to refine transformation logic and proactively address ambiguities.

Question 17

A seasoned data engineering team is undertaking a significant migration of a critical on-premises data warehouse to Google Cloud Platform, leveraging BigQuery as the central data repository. Upon completing the initial data transfer and establishing ETL pipelines, the team observes a substantial increase in query latency and unexpected cost escalations for routine analytical workloads. Stakeholders are expressing concern over the diminished performance and the growing expenditure. Initial investigations reveal that the schema was largely replicated from the legacy system, with minimal adjustments made to accommodate BigQuery\'s unique architectural characteristics. The team’s primary analytical queries frequently involve filtering by date ranges, segmenting data by customer tier, and aggregating metrics based on product categories.

Which of the following strategic adjustments to the BigQuery schema design and data loading process would most effectively address the observed performance degradation and cost inefficiencies?

Accepted Answer

Implement time-unit partitioning on a date column for temporal queries and cluster tables by frequently filtered columns such as customer tier and product category.

Question 18

A data engineering team operating on Google Cloud Platform is tasked with updating an existing analytics pipeline that ingests customer data from various sources into BigQuery. A recently enacted industry-specific regulation mandates that all personally identifiable information (PII) must be anonymized or pseudonymized prior to its inclusion in any analytical dataset, with stringent requirements for consent management and data handling. The current pipeline uses Cloud Storage for staging raw data and BigQuery for downstream analytics. The team needs a solution that allows for the systematic transformation and anonymization of PII, supports version control for the transformation logic, and can be reliably orchestrated with the existing data ingestion processes. Which of the following approaches would best address these requirements, ensuring compliance and maintainability?

Accepted Answer

Utilize Dataform to define and manage SQL-based data transformations, including the anonymization logic, and orchestrate these transformations using Cloud Composer to integrate with the existing data ingestion workflows.

Question 19

QuantumLeap Analytics, a rapidly growing fintech startup, is migrating its on-premises data warehouse to Google Cloud Platform. The project lead, Anya, is managing a diverse team of data engineers, some of whom are new to cloud technologies. The migration timeline is aggressive, and the precise data transformation requirements are still being refined by the product team, leading to significant ambiguity. During a critical sprint review, a disagreement emerges between two senior engineers regarding the optimal partitioning strategy for BigQuery tables, impacting downstream analytics performance and potentially delaying the next phase. Anya needs to resolve this conflict swiftly and decisively while ensuring team morale and project progress are maintained. Which of the following actions best exemplifies Anya\'s ability to navigate this situation, demonstrating leadership potential and adaptability?

Accepted Answer

Facilitate a focused working session with the disagreeing engineers and key stakeholders to collaboratively define and document the optimal partitioning strategy, leveraging their collective expertise to adapt the plan based on emerging data characteristics and performance benchmarks, while clearly communicating the rationale and next steps to the broader team.

Question 20

Anya, a lead data engineer on Google Cloud, is tasked with migrating a critical customer analytics pipeline from a legacy batch system to a real-time streaming architecture using Dataflow and Pub/Sub. The initial project brief outlines the general goal but lacks specific details regarding data transformation logic for the new streaming format and error handling protocols. The product owner has indicated that priorities might shift based on early user feedback once a prototype is available. Anya needs to ensure her team remains effective and adaptable during this transition, which involves integrating with new GCP services and potentially adopting different data modeling techniques. Which course of action best demonstrates Anya\'s adaptability and leadership potential in this ambiguous and evolving situation?

Accepted Answer

Proactively identify and document potential ambiguities in the new streaming requirements, then schedule a focused working session with the product owner to clarify them before proceeding with significant pipeline modifications.

Question 21

Anya, a lead data engineer on a critical project, observes that a newly deployed batch processing pipeline on Google Cloud Platform is intermittently failing during peak data ingestion periods. These failures are accompanied by reports of downstream data inconsistencies, suggesting potential schema drift or unexpected data volume surges. The team\'s Service Level Agreement (SLA) for data availability is at risk. Anya needs to quickly diagnose the root cause and adapt the pipeline\'s behavior to ensure reliability. Which of the following actions should Anya prioritize to effectively address this situation and demonstrate adaptability and problem-solving acumen?

Accepted Answer

Thoroughly analyze logs from Cloud Monitoring and Cloud Logging for Dataflow and BigQuery to identify specific error patterns and resource utilization during failure events.

Question 22

A critical data pipeline on Google Cloud Platform, responsible for aggregating and transforming sensitive financial transaction data for quarterly regulatory reporting, experiences a catastrophic, unrecoverable failure just hours before the submission deadline. The failure appears to be related to an unexpected data schema change introduced in a recent upstream data source update, which the pipeline\'s validation checks did not anticipate. The data engineer on call must act with extreme urgency to mitigate the impact and ensure compliance. Which of the following immediate actions best balances operational stability, regulatory adherence, and future risk mitigation?

Accepted Answer

Perform an immediate rollback of the affected data pipeline to its last known stable state, initiate a root cause analysis in a separate, isolated environment to identify the schema mismatch, and proactively communicate the situation and mitigation steps to compliance officers and relevant business unit leaders.

Question 23

A critical, unforeseen business imperative demands the immediate integration of real-time sensor data into an established BigQuery data warehouse, which currently relies on nightly batch ETL processes orchestrated by Cloud Composer. The existing data pipelines are robust and fulfill current reporting needs, but the new requirement necessitates near-instantaneous availability of the sensor readings for fraud detection analytics. The data engineering team has been tasked with implementing this change with minimal disruption to existing operations and a clear plan for future scalability. Which of the following approaches best balances immediate needs with long-term maintainability and operational stability on Google Cloud?

Accepted Answer

Implement a parallel data ingestion and processing path using Pub/Sub for message queuing and Dataflow in streaming mode to process the sensor data, writing it to a new BigQuery dataset. Subsequently, develop a strategy to reconcile or merge this real-time data with the existing batch data as needed for analytics.

Question 24

Anya, a lead data engineer at a fintech firm, is tasked with migrating a critical customer data processing pipeline from on-premises infrastructure to Google Cloud Platform. Midway through the migration, a new, stringent data privacy regulation is enacted, mandating specific data anonymization techniques and real-time consent management that were not part of the original project scope. Anya\'s team is already facing tight deadlines and has developed significant momentum on the existing architecture. How should Anya best navigate this situation to ensure both regulatory compliance and project success, demonstrating her adaptability and leadership?

Accepted Answer

Immediately halt the current migration, convene an emergency meeting with the compliance team to fully understand the new regulations, and then present a revised project plan with adjusted timelines and resource allocations to stakeholders, emphasizing the necessity of the pivot.

Question 25

A critical data pipeline responsible for generating compliance reports for the financial sector experiences an unexpected failure just hours before a mandated submission deadline. The failure\'s root cause is not immediately apparent, and the system is experiencing intermittent instability. Concurrently, a new, unannounced policy change from the regulatory authority is circulating internally, potentially impacting the report\'s format and required disclosures. As the lead data engineer, how would you navigate this complex situation to uphold both technical integrity and regulatory obligations?

Accepted Answer

Initiate immediate rollback to a previous stable version of the pipeline while simultaneously dispatching a detailed incident report to the regulatory authority, outlining the failure, the mitigation strategy, and requesting clarification on the new policy's applicability.

Question 26

A global financial institution is migrating its critical customer data processing to Google Cloud Platform. The data engineering team is tasked with building a new streaming pipeline using Dataflow and Pub/Sub to ingest and process customer transaction data. A recent stringent regulatory update mandates that all Personally Identifiable Information (PII) must be pseudonymized in transit before being stored in BigQuery. Crucially, an immutable audit log detailing each pseudonymization event, including the original value (before pseudonymization, for a limited internal audit window), the pseudonymized value, the timestamp of the operation, and the pipeline worker that performed it, must be maintained for seven years. This audit log must be protected against any form of modification or deletion. Which strategy best satisfies these stringent, immutable audit logging requirements within the specified retention period?

Accepted Answer

Configure the Dataflow pipeline to write detailed transformation logs to a Cloud Storage bucket with object versioning and a lifecycle policy enforcing immutability for seven years.

Question 27

Anya, a senior data engineer leading a cross-functional team on Google Cloud Platform, is tasked with migrating a sensitive customer dataset to a new analytics platform. Shortly after the project\'s inception, a significant change in data privacy regulations, specifically concerning the permissible retention periods and anonymization standards for Personally Identifiable Information (PII), is announced. The existing data pipeline, designed for optimal query performance, now faces potential non-compliance. Anya must quickly adapt the team\'s strategy, which was focused on schema optimization and data warehousing efficiency, to incorporate robust data anonymization and adhere to the new regulatory framework. Considering Anya\'s responsibilities for leadership, adaptability, and technical problem-solving, what is the most crucial initial step she should take to effectively address this evolving compliance landscape?

Accepted Answer

Initiate a comprehensive impact assessment of the new regulations on the current data architecture and pipeline, identifying specific data elements requiring anonymization and developing a phased strategy for implementing appropriate privacy-preserving techniques and controls.

Question 28

Anya, a lead data engineer on Google Cloud Platform, is overseeing a critical project involving the ingestion of massive real-time data streams from global IoT devices. The team is divided between two primary ingestion strategies: one advocating for a highly customizable Dataflow pipeline with sophisticated windowing for complex event processing, and another preferring a simpler Pub/Sub to Cloud Functions integration for immediate transformations. A strict regulatory deadline for data sovereignty compliance is rapidly approaching, and the team\'s inability to agree on an approach is causing significant delays and team friction. Anya has attempted to delegate decision-making, but the deadlock persists. Considering Anya\'s role in navigating technical challenges, team dynamics, and external pressures, which action would most effectively resolve the situation while ensuring project success and compliance?

Accepted Answer

Facilitate a structured debate focusing on the trade-offs of each proposed solution against project requirements and regulatory compliance, then make a decisive, communicated recommendation.

Question 29

During the development of a new customer analytics platform on Google Cloud Platform, a sudden, unforeseen regulatory change mandates strict data residency requirements for all Personally Identifiable Information (PII). This necessitates a fundamental re-architecture of the existing data pipelines, storage solutions (including BigQuery and Cloud Storage), and data processing workflows (potentially involving Dataflow and Dataproc). The project deadline remains unchanged, and the team must deliver a compliant solution. Which of the following core competencies would be MOST critical for the data engineering team to successfully navigate this challenging situation and deliver the project on time and in compliance?

Accepted Answer

Adaptability and Flexibility

Question 30

A multinational financial services firm is undergoing a significant transformation of its fraud detection system, migrating from an on-premises batch processing architecture to a cloud-native, real-time streaming solution on Google Cloud Platform. The initial data engineering team successfully designed and implemented a robust batch data pipeline using Cloud Dataflow to process daily financial transaction logs, storing the transformed data in BigQuery for analytical reporting. However, a critical business requirement change mandates the detection of fraudulent activities within minutes of transaction occurrence. This necessitates an immediate pivot from the established batch processing paradigm to a real-time streaming ingestion and processing model. The team must adapt their existing architecture and workflows to incorporate services like Pub/Sub for message queuing and potentially reconfigure Dataflow jobs for streaming mode, all while ensuring data quality, low latency, and cost-efficiency. Considering the rapid evolution of requirements and the need for swift adaptation, which of the following strategic adjustments would best demonstrate the team\'s ability to navigate this significant technical and operational transition, aligning with the principles of adaptability, problem-solving, and strategic vision communication within a professional data engineering context?

Accepted Answer

Proactively redesign the entire data ingestion and processing layer to leverage Pub/Sub for streaming ingestion, reconfigure existing Dataflow jobs for streaming execution, and implement a dynamic schema evolution strategy in BigQuery to accommodate the real-time data characteristics, while initiating a cross-functional workshop to align stakeholders on the new architecture and revised delivery timelines.

Professional Data Engineer Professional Data Engineer on Google Cloud Platform Free Practice Test — 30 Questions

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