AWS Certified Big Data Specialty AWS Certified Big Data Specialty Free Practice Test — 30 Questions

Question 1

A data engineering team responsible for managing a complex data ingestion and transformation process feeding into an Amazon EMR cluster is experiencing significant operational friction. Pipelines are becoming increasingly intricate, with frequent, undocumented changes originating from various stakeholder groups. The team lacks a clear framework for prioritizing urgent fixes versus planned enhancements, leading to missed deadlines and a decline in data quality. Ownership for specific pipeline segments is often ambiguous, resulting in delays when issues arise as the responsible party is not immediately identifiable. The team’s current problem-solving approach is largely reactive, and there’s a palpable sense of frustration due to the constant firefighting. Which of the following strategic shifts would best address the team\'s challenges related to adaptability, collaboration, and effective problem-solving in this evolving AWS big data environment?

Accepted Answer

Implement a Kanban-style workflow with clearly defined swimlanes for pipeline ownership, coupled with mandatory cross-functional "design thinking" workshops to foster collaborative solution ideation and establish robust post-incident review processes.

Question 2

A data engineering team, initially tasked with building a batch-oriented data processing pipeline using Amazon S3 for data storage and Amazon EMR for transformations, is now facing a directive to incorporate near real-time analytics for a new set of high-velocity IoT sensor data. The business requires insights within minutes of data generation, a significant shift from the current daily batch processing. The team must demonstrate adaptability by integrating this new streaming capability into their existing data lake architecture with minimal disruption to ongoing batch workloads, while also preparing for future analytical needs that might involve more complex event processing. Which combination of AWS services best addresses these evolving requirements and demonstrates a proactive, flexible approach to architectural changes?

Accepted Answer

Amazon Kinesis Data Streams for ingestion, Amazon Kinesis Data Analytics for Apache Flink for real-time processing and transformation, and Amazon Redshift for storing processed streaming data to be queried alongside existing batch data.

Question 3

A seasoned data architect is leading a critical migration of a petabyte-scale, on-premises Hadoop data lake to Amazon S3, leveraging AWS Glue for ETL processes and Amazon EMR for analytical workloads. The team comprises individuals with varying levels of AWS expertise. Midway through the migration, a regulatory audit reveals a new, stringent data residency requirement for a significant portion of the data, necessitating a re-architecture of data storage and access patterns. Simultaneously, a key team member responsible for EMR cluster optimization resigns unexpectedly. The data architect must now reassess the project\'s timeline, resource allocation, and technical approach to meet the new compliance mandates while mitigating the impact of the team\'s reduced capacity. Which of the following behavioral competencies is MOST critical for the data architect to effectively navigate this multifaceted challenge?

Accepted Answer

Adaptability and Flexibility, coupled with strong Problem-Solving Abilities and Leadership Potential

Question 4

A data engineering team is orchestrating a complex migration of a petabyte-scale, on-premises relational data warehouse to AWS. The objective is to enhance analytical capabilities and operational efficiency. During the initial phase, which involves lifting and shifting historical data to Amazon S3 and setting up AWS Glue for ETL jobs, a significant shift in the interpretation of data privacy regulations impacts the handling of personally identifiable information (PII) within the datasets. This necessitates a substantial re-evaluation of the data transformation and access control strategies. Which of the following approaches best demonstrates the team\'s adaptability and flexibility in maintaining effectiveness and pivoting their strategy to address this unexpected compliance challenge while ensuring the migration remains on track?

Accepted Answer

Immediately halt all migration activities, convene an emergency cross-functional meeting to re-architect the entire data pipeline using AWS Lake Formation for granular access control and AWS KMS for enhanced encryption of PII, and then resume the migration based on the new architecture.

Question 5

A data engineering team, accustomed to a decade of on-premises ETL batch processing, is tasked with migrating a critical data warehouse to AWS. Despite the availability of services like AWS Glue, Amazon EMR with Spark, and Amazon Kinesis, the team exhibits significant resistance to adopting these cloud-native paradigms, preferring to replicate their existing batch-oriented workflows. They express concerns about the complexity of new tools and a perceived lack of control compared to their familiar environment. The team lead recognizes this as a significant impediment to realizing the full benefits of the cloud migration. Which behavioral competency, when effectively cultivated, would most directly enable the team to overcome this inertia and embrace the new AWS data processing methodologies?

Accepted Answer

Learning Agility

Question 6

Aethelred Analytics, a financial services firm operating under strict data residency and privacy regulations similar to GDPR, initially architected a data lake on Amazon S3, with AWS Glue orchestrating batch ETL processes for historical financial transactions. Recently, they need to incorporate real-time market sentiment data from external feeds and ensure that all Personally Identifiable Information (PII) is masked *before* it is made available for analytical queries, regardless of whether the data originates from batch or streaming sources. The existing data pipeline must be adapted to accommodate these new requirements while maintaining compliance and centralized governance. Which approach best addresses Aethelred Analytics\' evolving needs for real-time data ingestion, pre-analytical PII masking, and robust data governance within their AWS data lake?

Accepted Answer

Implement Amazon Kinesis Data Streams for real-time ingestion, configure AWS Glue Streaming ETL jobs to apply PII masking transformations before landing data in S3, and utilize AWS Lake Formation to manage granular access controls to the masked data.

Question 7

A global financial services firm is experiencing rapid growth and needs to expand its real-time analytics capabilities to incorporate new market data feeds and comply with recently introduced stringent data privacy regulations that mandate the filtering of personally identifiable information (PII) at the earliest ingestion point, along with encryption using customer-managed keys for all data at rest and in transit. The current architecture utilizes Amazon Kinesis Data Streams for ingesting market data, AWS Lambda for stateless transformations, Amazon S3 for data lake storage, and an Amazon EMR cluster for batch analytics. The firm must adapt its ingestion and processing layers to accommodate these new requirements with minimal disruption to the existing batch analytics workflow and ensure a comprehensive audit trail for data handling.

Which architectural modification best addresses these evolving needs while maintaining efficiency and compliance?

Accepted Answer

Introduce Amazon Kinesis Data Firehose to ingest data from new sources, reconfigure existing Kinesis Data Streams to deliver to Firehose, and utilize Firehose's integrated Lambda transformation capabilities for PII filtering and encryption with customer-managed keys, delivering processed data to S3 with appropriate logging enabled for audit trails.

Question 8

A multinational financial services firm is constructing a data lake on AWS to consolidate customer transaction data from various global regions. The data is initially ingested into Amazon S3. A dedicated data engineering team utilizes AWS Glue ETL jobs to cleanse, transform, and aggregate this raw data into curated datasets, which are then cataloged using the AWS Glue Data Catalog. Analysts in different departments require access to these curated datasets for reporting and ad-hoc analysis using Amazon Athena. Critically, due to regulatory compliance mandates (e.g., GDPR, CCPA), access must be strictly controlled at a granular level, allowing specific users to view only certain columns (e.g., excluding personally identifiable information) and rows (e.g., based on their regional responsibilities). Furthermore, a separate analytics team operating in a different AWS account needs to query a subset of this curated data without data duplication. Which AWS service and approach would best satisfy these requirements for centralized, fine-grained access control and secure cross-account data sharing within the data lake ecosystem?

Accepted Answer

Implement AWS Lake Formation to manage permissions for data registered in the AWS Glue Data Catalog, enabling column-level and row-level security, and utilize Lake Formation's cross-account sharing capabilities.

Question 9

A seasoned data engineering lead is tasked with modernizing a critical, yet poorly documented, on-premises data warehouse to a cloud-native AWS architecture. The project timeline is aggressive, with business units demanding faster access to analytics. The existing ETL jobs are complex and their interdependencies are not fully mapped out. During the initial discovery phase, significant discrepancies are found between the perceived functionality of the data warehouse and its actual behavior, leading to frequent, unforeseen roadblocks. The lead must balance the need for a robust, scalable solution with the immediate pressure to deliver value and maintain operational stability. Which of the following approaches best demonstrates the required behavioral competencies to navigate this complex migration?

Accepted Answer

Adopt an iterative migration strategy, focusing on migrating core data domains incrementally, with frequent validation cycles involving business stakeholders, and maintaining a flexible architectural blueprint that allows for adjustments based on ongoing discoveries and feedback.

Question 10

Anya, a lead data engineer, oversees a critical project migrating a financial analytics platform from an on-premises batch processing system to a cloud-native, near real-time streaming architecture on AWS. The team, accustomed to established ETL workflows and tools, is expressing apprehension about adopting new technologies like Amazon Kinesis Data Streams and AWS Lambda for event processing. Project timelines are tight, and the exact integration points with legacy systems are still being refined, creating a degree of ambiguity. Anya needs to ensure the project\'s success while maintaining team morale and fostering a collaborative problem-solving environment. Which of the following actions would best position Anya to successfully navigate this transition and demonstrate strong leadership and adaptability?

Accepted Answer

Proactively identify specific skill gaps related to streaming data processing and cloud-native services within the team, then facilitate targeted training sessions and provide access to relevant AWS documentation and sandbox environments to encourage hands-on experimentation and knowledge sharing.

Question 11

A financial analytics firm is experiencing a significant surge in transactional data volume and velocity. Concurrently, they need to incorporate unstructured customer feedback from various channels into their existing data lake and downstream analytical models. The current architecture relies on Amazon EMR for batch processing of structured data and Amazon Kinesis Data Firehose for ingesting streaming data into Amazon S3. The team\'s immediate response is to scale up the EMR cluster and configure Firehose to directly append the unstructured feedback to the S3 data lake. Which strategic adjustment best demonstrates adaptability and effective problem-solving in this evolving big data landscape, considering the need for specialized processing of unstructured data and potential future growth?

Accepted Answer

Implement AWS Glue DataBrew for interactive data preparation and transformation of the unstructured feedback, then use AWS Glue ETL jobs to integrate the processed data into the data lake, potentially augmenting the existing EMR workflows for structured data.

Question 12

A data engineering team, responsible for a critical customer-facing analytics platform on AWS, is tasked with integrating a novel real-time stream processing framework to enhance data freshness. The chosen framework, while promising significant performance gains, is still maturing, leading to some documentation gaps and unexpected integration challenges. Management has also shifted the project\'s primary success metric from latency reduction to the breadth of data sources ingested within the first quarter. The team lead must guide the group through this evolving landscape, ensuring continued progress and morale despite the inherent uncertainty and the need to re-evaluate their technical strategy. Which behavioral competency is most critical for the team lead to demonstrate in this situation?

Accepted Answer

Adaptability and Flexibility

Question 13

A global e-commerce company is migrating its customer analytics platform to AWS. They must comply with strict data privacy regulations, including the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA), which mandate that personally identifiable information (PII) be protected. The analytics team needs to perform complex aggregations and machine learning tasks on customer behavioral data, which inherently contains PII such as email addresses and purchase histories. The company wants to avoid direct exposure of raw PII to the broader analytics team while still enabling them to derive meaningful insights. What is the most effective approach to govern access and ensure compliance for this scenario?

Accepted Answer

Utilize AWS Lake Formation to create data filters that mask or tokenize PII columns, and grant granular access to these filtered views to specific analytics roles, while using AWS Glue DataBrew for the initial PII transformation.

Question 14

A data engineering team responsible for delivering near real-time insights to a global e-commerce platform is struggling to meet escalating demands for new data sources and altered reporting frequencies. Their current architecture, a hybrid of legacy on-premises infrastructure and a fragmented set of AWS services, lacks standardized deployment pipelines and robust monitoring. This has resulted in extended lead times for feature delivery and frequent rework due to misaligned expectations. The team lead observes a general reluctance to adopt new processing frameworks and a tendency to revert to familiar, albeit less efficient, methods when faced with project ambiguity. Which behavioral competency, if fostered, would most directly enable the team to overcome these systemic challenges and improve their overall delivery cadence and responsiveness?

Accepted Answer

Adaptability and Flexibility

Question 15

A global manufacturing firm is implementing a new system to monitor critical operational parameters from thousands of networked sensors across its worldwide facilities. The system must ingest high-velocity, high-volume time-series data in real time. The primary objectives are to detect anomalous readings that could indicate equipment malfunction or safety hazards with minimal latency, log all raw and processed data for historical analysis and regulatory audits, and enable data scientists to perform complex ad-hoc queries on years of historical sensor data to identify long-term performance trends and optimization opportunities. The firm prioritizes a serverless and highly scalable architecture that minimizes operational overhead. Which combination of AWS services best addresses these requirements?

Accepted Answer

Amazon Kinesis Data Streams for ingestion, Amazon Kinesis Data Analytics for Apache Flink for real-time anomaly detection, Amazon S3 for data lake storage, Amazon Athena for historical querying, and AWS Step Functions for workflow orchestration.

Question 16

Anya, a lead data engineer, observes her team struggling to meet deadlines for a critical real-time analytics platform. Project requirements are frequently updated by product managers, and the team lacks a consistent method for incorporating these changes, leading to rework and frustration. During daily stand-ups, developers express confusion about the current priorities, and cross-functional communication regarding data schema evolution is often delayed or incomplete. Anya recognizes the need to adapt the team\'s workflow to manage this inherent ambiguity and maintain momentum. Which of the following actions would best demonstrate Anya\'s adaptability, leadership potential, and commitment to collaborative problem-solving in this scenario?

Accepted Answer

Implement a formal change request process for all data pipeline modifications, establish regular cross-functional review sessions for upcoming feature requirements, and mandate clear documentation for all schema updates, fostering a more predictable and collaborative development environment.

Question 17

A global e-commerce company, operating under increasingly strict data sovereignty laws in multiple jurisdictions, is migrating its petabyte-scale customer analytics platform from a traditional data warehouse to a cloud-native data lake architecture on AWS. The platform processes sensitive customer information, including purchase history and personal identifiers, and must comply with regulations that mandate data residency within specific geographic regions and restrict cross-border data transfer for certain data types. The existing processing jobs are built on Amazon EMR, leveraging Spark for transformations. The company needs to adapt its data governance and processing strategy to ensure continuous compliance without significantly impacting query performance or introducing substantial architectural complexity. Which AWS service combination, when implemented with a focus on dynamic access control and data classification, best addresses the immediate need for regulatory adherence while maintaining operational agility?

Accepted Answer

AWS Lake Formation for fine-grained access control and data cataloging, integrated with Amazon EMR for processing, configured to enforce data residency policies through attribute-based access control and data classification tags.

Question 18

A multinational logistics company is deploying a new fleet of smart sensors across its global network of warehouses. These sensors generate high-velocity, high-volume data streams containing metrics like temperature, humidity, equipment status, and movement patterns. The company needs to ingest this data, perform complex real-time transformations including anomaly detection (e.g., unusual equipment vibrations), enrich it with historical maintenance logs and weather data, and then make the processed data available for interactive querying by data scientists to identify operational inefficiencies and predict potential equipment failures. The solution must support near real-time analytics and ad-hoc exploration of the enriched data. Which AWS service is the most appropriate for the core real-time data processing and enrichment layer of this solution?

Accepted Answer

AWS Kinesis Data Analytics for Apache Flink

Question 19

A data engineering team at a financial services firm is developing a real-time analytics platform using AWS services like Kinesis, Lambda, and DynamoDB. Midway through the project, a significant change in data privacy regulations (e.g., GDPR-like requirements) necessitates a complete re-architecture of data ingestion and storage to ensure compliance. The team is experiencing uncertainty and some resistance to the sudden shift in direction. Which leadership approach would most effectively guide the team through this transition and ensure continued project momentum?

Accepted Answer

Proactively communicate the revised project scope and rationale, solicit team input on re-architecture strategies, and delegate specific compliance-related tasks to sub-teams, fostering a sense of shared ownership and clear direction.

Question 20

Quantum Leap Analytics, a financial services firm, is grappling with a data processing pipeline that exhibits escalating latency, frequent data quality degradations, and an inability to scale effectively with their burgeoning customer transaction volumes. The firm is also under increasing pressure to adhere to strict data privacy regulations like GDPR and CCPA, which demand granular control over data access, purpose limitation, and demonstrable consent management. The current architecture, predominantly batch-oriented, is proving inadequate for the new strategic imperative of real-time fraud detection. Which strategic adjustment would most effectively address these interwoven technical, operational, and compliance challenges?

Accepted Answer

Re-architect the data processing framework to a serverless, event-driven model utilizing services such as AWS Lambda for processing, Amazon Kinesis for real-time data ingestion and streaming, Amazon S3 for data storage, and integrating AWS Glue Data Catalog and AWS Lake Formation to enforce fine-grained access controls and audit trails for regulatory compliance.

Question 21

A data engineering team is tasked with re-architecting a critical customer analytics pipeline. Recent legislative changes in data privacy necessitate significant modifications to data ingestion, transformation, and storage. Concurrently, the marketing department has introduced a new stream of high-velocity, semi-structured behavioral data from a novel customer engagement platform. The team is experiencing significant ambiguity regarding the precise interpretation of the new regulations and the optimal method for integrating the diverse data formats and processing requirements into the existing AWS ecosystem. Which of the following approaches best demonstrates the team\'s adaptability and flexibility in addressing these evolving priorities and inherent uncertainties?

Accepted Answer

Proactively engage with legal and compliance teams to clarify regulatory mandates, concurrently explore AWS services like AWS Glue and Amazon Kinesis Data Analytics for processing the new data streams, and conduct iterative proof-of-concept deployments to validate integration strategies and performance.

Question 22

A multinational financial services firm is undertaking a large-scale migration of its customer data platform to AWS. Midway through the project, a new, stringent data privacy regulation is enacted, requiring significant modifications to data anonymization and access control mechanisms that were already implemented. The project lead, Anya, must guide her cross-functional team, composed of data engineers, security analysts, and compliance officers, through this unexpected pivot without derailing the entire migration timeline or jeopardizing data integrity. Which of Anya\'s behavioral competencies would be most critical in successfully navigating this challenge?

Accepted Answer

Demonstrating a growth mindset by encouraging the team to research and adopt new anonymization techniques and access control patterns, facilitating necessary training, and empowering them to re-architect components flexibly.

Question 23

A data engineering team at a financial services firm, responsible for building real-time analytics pipelines on AWS, is experiencing significant disruption. Project scope frequently changes mid-sprint due to evolving regulatory compliance mandates and shifting business intelligence needs. Team members report feeling overwhelmed by the constant re-prioritization, leading to missed deadlines and a decline in code quality. Communication breakdowns are common, with different sub-teams working in silos and lacking a unified understanding of project goals. Morale is low, and there\'s a palpable resistance to adopting new tools or methodologies suggested by management, hindering innovation. Which strategic intervention would most effectively address the underlying behavioral and process challenges impacting the team\'s performance?

Accepted Answer

Implement a structured Agile framework, such as Scrum or Kanban, to foster iterative development, improve transparency, and enable rapid adaptation to changing requirements.

Question 24

A global e-commerce organization is architecting a new big data platform on AWS to ingest and analyze customer behavior, transaction history, and product reviews. The platform must accommodate diverse data formats, including structured, semi-structured, and unstructured data, originating from various sources. A critical requirement is to implement robust data governance, ensuring compliance with data residency mandates (e.g., GDPR, CCPA) across multiple AWS Regions and enabling granular access controls for different internal teams (marketing, analytics, fraud detection). The organization anticipates frequent changes in data schemas and analytical workloads. Which AWS service combination provides the most effective and adaptable solution for managing data access, governance, and residency in this complex, evolving data lake environment?

Accepted Answer

AWS Lake Formation, AWS Glue Data Catalog, and Amazon EMR

Question 25

A data engineering team is migrating a petabyte-scale historical data lake from an on-premises Hadoop ecosystem to AWS. During parallel processing of large historical datasets using Amazon EMR, they observe significant performance degradation and occasional data inconsistencies compared to the on-premises environment. Initial analysis suggests that the existing static partitioning strategy, based solely on ingestion date, is not effectively optimizing data access for various analytical queries that frequently filter by region and product category, leading to increased read amplification and scan times. The team needs to rapidly adjust their approach to ensure a successful and performant data lake on AWS, while also adhering to evolving data governance requirements that mandate stricter access controls and schema management.

Which of the following strategies would best address the observed performance issues and evolving governance needs, demonstrating adaptability and effective problem-solving in a complex migration scenario?

Accepted Answer

Implement AWS Glue Data Catalog for centralized metadata management, refine data partitioning in Amazon S3 to include region and product category alongside ingestion date, and leverage AWS Lake Formation for fine-grained access control and data lineage tracking.

Question 26

A cross-functional data analytics team, responsible for building a customer segmentation model for a global e-commerce platform, is experiencing significant delays. Two key members, one focusing on data ingestion and pipeline reliability, and the other on data privacy and anonymization techniques, are in constant disagreement. The former argues for rapid data integration to accelerate model training, even if it means temporarily retaining more granular customer identifiers. The latter insists on immediate and robust anonymization, citing stringent data protection regulations like the California Consumer Privacy Act (CCPA) and the General Data Protection Regulation (GDPR), and believes the current approach risks non-compliance. This impasse is halting progress on critical feature engineering and model validation phases. As the team lead, what is the most effective behavioral competency to address this situation and move the project forward while ensuring compliance?

Accepted Answer

Conflict Resolution Skills

Question 27

A global online retailer is experiencing significant growth, leading to a massive influx of customer interaction data stored across various AWS services. To comply with evolving data privacy regulations like GDPR, the company must ensure that customer Personally Identifiable Information (PII) is not unnecessarily exposed during exploratory data analysis by their data science team. The data science team needs to analyze customer purchasing patterns, website navigation, and product feedback to identify trends and improve user experience. However, they should only have access to aggregated, anonymized, or pseudonymized data that aligns with the principle of data minimization and purpose limitation. The current architecture utilizes Amazon S3 for raw data storage, with data ingested via Kinesis Data Firehose. The retailer needs a solution that allows data scientists to efficiently query and transform this data for analysis without direct access to raw PII, while also providing robust governance and auditability for compliance.

Which AWS services and strategy would best enable the data science team to conduct their analysis while strictly adhering to GDPR\'s data minimization and purpose limitation principles?

Accepted Answer

Implement AWS Lake Formation for centralized data governance and fine-grained access control, leverage Amazon EMR for scalable data processing, and utilize AWS Glue DataBrew for data preparation and transformation to create analysis-ready datasets.

Question 28

A global e-commerce platform has implemented an AWS data lake using Amazon S3, governed by AWS Lake Formation. The data lake contains customer transaction data, partitioned by `country` and `transaction_date`. A new initiative requires a data science team to build a predictive model for identifying fraudulent transactions. This team consists of senior data scientists who need access to all transaction details for a specific set of countries and all dates, and junior data scientists who require access only to anonymized transaction amounts and customer IDs for a broader range of countries, but only for the last fiscal quarter. Both groups need to operate within the strict data privacy regulations of the regions they serve. Which approach best balances the need for granular access control, regulatory compliance, and operational efficiency for the data science team\'s project?

Accepted Answer

Define distinct Lake Formation data access policies for each group of data scientists, granting `SELECT` permissions on specific columns and rows (filtered by `country` and `transaction_date`) for the required data subsets, and enable AWS CloudTrail for auditing all access events.

Question 29

A rapidly growing e-commerce platform is struggling with its real-time analytics pipeline. The current architecture utilizes Amazon Kinesis Data Firehose to ingest clickstream data and deliver it to Amazon S3. Downstream, Amazon EMR clusters are used for batch processing and analysis. Recently, the operations team has reported a significant increase in processing times for EMR jobs and a corresponding rise in AWS costs. Upon investigation, it\'s discovered that the Kinesis Data Firehose delivery stream is configured with a buffer interval of 60 seconds and a buffer size of 5 MB, using GZIP compression. This configuration results in a large number of small files being written to S3, which is negatively impacting EMR\'s read performance and increasing the overall cost of data processing due to inefficient file handling. The team needs to propose a solution that optimizes file sizes in S3 to improve EMR job performance and reduce costs, while maintaining near real-time data availability.

Which of the following adjustments to the Kinesis Data Firehose delivery stream configuration would most effectively address the described performance and cost issues?

Accepted Answer

Increase the buffer interval to 300 seconds, increase the buffer size to 15 MB, and switch the compression method to Snappy.

Question 30

A multinational e-commerce company is migrating its entire customer transaction history, spanning several petabytes, into an AWS data lake. This dataset contains sensitive Personally Identifiable Information (PII) such as email addresses, physical addresses, and partial payment details, subject to strict data privacy regulations. The analytics team requires broad access to transactional data for trend analysis, but the legal and compliance teams mandate that access to specific PII columns must be heavily restricted and auditable. The company is also anticipating future regulatory changes that may impose even stricter data access controls. Which AWS service configuration would best enable the company to implement a dynamic and compliant data access strategy for its data lake, allowing for granular control over sensitive data elements while facilitating efficient querying by the analytics team?

Accepted Answer

Configure AWS Lake Formation to enforce column-level security on sensitive PII fields within the AWS Glue Data Catalog, granting specific IAM roles to the analytics team with permissions to query the data via Amazon Athena.

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