In today’s hyper-connected world, organizations generate massive volumes of structured and unstructured data. Harnessing this data to derive actionable insights is no longer optional—it is imperative for innovation and competitiveness. This is where AWS and its Big Data ecosystem step in. The AWS Certified Big Data – Specialty certification validates the expertise of data professionals who work with complex data analytics systems, helping them stand out in a crowded field of cloud engineers, data architects, and machine learning practitioners.
AWS provides an arsenal of tools and services designed for collecting, storing, processing, and visualizing large-scale data. From managed services like Amazon EMR and Redshift to serverless solutions like Kinesis and Glue, AWS empowers professionals to design end-to-end data pipelines capable of handling terabytes and even petabytes of information.
This first part of the series serves as your foundational cheat sheet, demystifying the certification, clarifying the exam’s scope, and detailing the core AWS services you need to master before attempting the test.
What is the AWS Certified Big Data – Specialty Exam?
The AWS Certified Big Data – Specialty (BDS-C01) is one of AWS’s advanced-level certifications. It validates a candidate’s technical skills and experience in designing and implementing AWS services to derive value from data. Unlike foundational or associate-level certifications, this exam demands not only knowledge of AWS services but also practical expertise in data lifecycle management, performance optimization, and security compliance in the cloud.
Target Audience
This certification is specifically designed for individuals who:
- Design and maintain Big Data architectures
- Are responsible for implementing data analytics pipelines using AWS
- Work in data engineering, data science, or advanced analytics roles
- Have at least two years of hands-on experience using AWS data services
Exam Format and Structure
Understanding the format helps candidates mentally prepare for the test and focus their study efforts. Here’s what to expect:
- Duration: 170 minutes
- Number of Questions: 65
- Format: Multiple choice and multiple response
- Cost: $300 USD
- Delivery Method: Pearson VUE or PSI, testing center or online proctoring
Exam Domains and Weightings
The BDS-C01 exam is divided into several domains, each contributing to your overall score:
- Collection: 17%
- Storage: 17%
- Processing: 17%
- Analysis and Visualization: 15%
- Security: 20%
- Data Lifecycle and Automation: 14%
Each domain encompasses specific AWS services and architectural strategies that align with real-world big data implementations.
Domain 1: Collection
Efficient and secure data ingestion is the starting point for any Big Data project. Candidates must understand how to capture real-time and batch data from various sources.
Core Services in This Domain
Amazon Kinesis Data Streams
Kinesis allows you to capture, process, and analyze streaming data in real time. It’s ideal for telemetry, log aggregation, and IoT use cases.
Amazon Kinesis Firehose
This fully managed service delivers real-time streaming data to destinations such as Amazon S3, Redshift, or Elasticsearch, with minimal effort required to maintain infrastructure.
AWS IoT Core
For IoT data ingestion, AWS IoT Core provides a gateway between devices and the cloud. Candidates should be aware of MQTT protocols, rules engine, and message brokers.
AWS Snowball and Snowmobile
For petabyte-scale data migration, these offline tools are used to transfer bulk data into AWS.
AWS DataSync
A network-based service that helps migrate and replicate on-premises data into AWS efficiently.
Exam Tip
Understand the differences between Firehose and Kinesis Streams in terms of latency, transformation capability, and delivery targets. Real-time use cases generally favor Streams, while near real-time or buffered delivery is better suited to Firehose.
Domain 2: Storage
After ingestion, data must be stored securely and efficiently to ensure rapid access and scalability. The certification tests your ability to select the right storage service for various data types and access patterns.
Core Storage Services
Amazon S3
The go-to service for object storage in AWS. Candidates should be fluent in understanding storage classes like S3 Standard, Intelligent-Tiering, and Glacier for cost optimization. Knowledge of lifecycle policies, versioning, and encryption is also crucial.
Amazon Redshift
AWS’s fully managed data warehouse supports analytical queries on massive datasets. It’s optimized for complex joins and aggregations using columnar storage.
Amazon DynamoDB
A NoSQL key-value and document database. Understand its read/write throughput models, global tables, and integration with AWS DAX for caching.
Amazon RDS and Aurora
Relational database services that support structured datasets. Aurora provides better performance and scaling, and integrates with analytics tools via Aurora Machine Learning and Aurora Serverless.
Amazon EFS and FSx
Used for file-based storage, ideal for lift-and-shift scenarios or analytics workflows that depend on POSIX-compliant access.
Exam Tip
Expect scenario-based questions where you must determine the best storage solution based on access frequency, cost constraints, and latency requirements. Be prepared to justify why one storage tier is more appropriate than another.
Domain 3: Processing
This domain covers how to transform raw data into structured, meaningful formats suitable for analysis. Candidates should understand distributed processing frameworks, serverless compute, and pipeline orchestration.
Core Processing Services
Amazon EMR (Elastic MapReduce)
A managed Hadoop framework that supports tools like Hive, Spark, Presto, and HBase. You’ll need to understand EMR clusters, instance types, autoscaling, and integration with S3 and DynamoDB.
AWS Glue
A serverless ETL (extract, transform, load) service that automatically discovers and catalogs data using Glue Crawlers. It uses Apache Spark under the hood.
AWS Lambda
Used to run serverless functions, often as part of event-driven data pipelines. Knowledge of limits, triggers, and integration with S3, DynamoDB, and Kinesis is essential.
AWS Batch
Efficient for running large-scale parallel jobs. Candidates should grasp how it schedules jobs using ECS or EC2 resources.
Amazon Data Pipeline
Used for orchestrating ETL jobs and data movement between different services. It’s particularly useful in legacy environments but is increasingly replaced by Glue workflows or Step Functions.
Exam Tip
Focus on use cases. For example, when would you choose Glue over EMR? Know how to reduce costs in Spark clusters using Spot Instances or transient EMR clusters.
Domain 4: Analysis and Visualization
Once data is processed, it must be explored and visualized to derive actionable insights. This domain emphasizes data warehousing, business intelligence, and real-time analytics.
Core Services
Amazon Redshift
Critical for querying large volumes of structured data. Understand columnar storage, distribution styles (key, all, even), sort keys, and compression encodings.
Amazon Athena
A serverless query service that uses Presto under the hood. It reads directly from S3 using standard SQL. Best for ad hoc analysis on semi-structured data.
Amazon QuickSight
AWS’s BI service for creating interactive dashboards. Know about SPICE (Super-fast, Parallel, In-memory Calculation Engine), embedding dashboards, and access control.
AWS OpenSearch Service
Used for full-text search and log analytics. Based on the open-source Elasticsearch engine.
Amazon CloudWatch and CloudTrail
Useful for operational analytics, monitoring metrics, and identifying anomalous behavior through logs and events.
Exam Tip
Master the differences between Redshift and Athena. Understand cost structures, performance trade-offs, and scalability. Visualization questions often touch on QuickSight’s integration with RDS, S3, or Athena.
Domain 5: Security
Given the sensitive nature of data, securing it throughout its lifecycle is critical. This domain evaluates your grasp of encryption, compliance, data access control, and auditing.
Core Concepts
Encryption
Master server-side (SSE-S3, SSE-KMS, SSE-C) and client-side encryption. Know how to enforce encryption using bucket policies.
AWS Key Management Service (KMS)
Enables creation and control of encryption keys. Candidates should understand key policies, automatic key rotation, and integration with services like S3, Redshift, and DynamoDB.
Identity and Access Management (IAM)
Understand policies, roles, least privilege access, and resource-based policies.
VPC Security
Data services often reside in a Virtual Private Cloud. Learn about subnets, NACLs, security groups, and endpoint services.
AWS Lake Formation
Used to simplify security management in data lakes. Familiarize yourself with data lake permissions, table-level access control, and fine-grained permissions.
Exam Tip
Expect multiple questions on data encryption and security policies. Be ready to evaluate access strategies that comply with GDPR, HIPAA, or other regulatory standards.
Domain 6: Data Lifecycle and Automation
Finally, understanding the full lifecycle of data—collection to deletion—and automating this pipeline is vital for operational efficiency.
Key Services and Practices
Data Lifecycle Management
Use S3 lifecycle rules to transition objects across storage tiers. Know how to delete stale datasets automatically.
Workflow Automation
Glue Workflows and AWS Step Functions are instrumental in orchestrating complex data tasks. CloudWatch Events and EventBridge can be used to trigger workflows.
Versioning and Audit Trails
Leverage versioning in S3, DynamoDB streams, and CloudTrail to monitor changes and maintain traceability.
CI/CD for Data Pipelines
Integrate CodePipeline and CodeBuild to automate the deployment of analytical code, data transformations, and schema changes.
Exam Tip
Know how to design a data pipeline that self-manages, scales on demand, and recovers from failure gracefully. Lifecycle automation questions test both technical know-how and architectural intuition.
From Theory to Real-World Application
In this series, we laid the foundation by unpacking the essential domains and core services tied to the AWS Certified Big Data – Specialty exam. But technical fluency alone isn’t enough. The true hallmark of a Big Data specialist lies in their ability to apply that knowledge to real-world challenges with architectural foresight and strategic discernment.
Part 2 dives into practical implementations, decision-making processes, and use case-based analysis—skills imperative not only for passing the certification but for thriving as a data architect in dynamic cloud environments.
Real-World Use Cases in AWS Big Data Architectures
A recurring pattern in the Big Data Specialty exam is the use of real-world scenarios. These case studies challenge your ability to weigh service capabilities against business and technical requirements. Let’s dissect some of the most common ones to sharpen your situational awareness.
Handling Real-Time Log Analytics
Consider a scenario where an organization needs to ingest and analyze millions of log events per minute coming from a fleet of microservices. Speed and minimal latency are critical. In such cases, Amazon Kinesis Data Streams becomes an ideal ingestion point due to its ability to capture high-throughput data in real time. Once ingested, processing can be handled using AWS Lambda functions or Kinesis Data Analytics to enrich, transform, or filter the logs.
The transformed data can then be pushed into Amazon OpenSearch Service for querying and visual dashboards. To preserve historical records and reduce storage costs, the same stream can also write raw logs to Amazon S3.
This setup ensures the business gets immediate visibility through dashboards and retains logs for compliance and deeper offline analysis. The exam will often challenge you to determine whether Firehose or Kinesis Streams better suits a scenario; choosing between simplicity and control becomes vital.
Executing Petabyte-Scale ETL Operations
Let’s explore a case involving the ingestion of vast volumes of structured data from multiple on-premise databases and third-party platforms. A typical requirement includes nightly ETL jobs that refresh a data lake. To handle such scale efficiently, AWS DataSync or Database Migration Service (DMS) can facilitate source ingestion.
Storage and processing converge in Amazon S3 and AWS Glue. Glue Crawlers can auto-discover schemas and register metadata into the AWS Glue Data Catalog. Glue Jobs can transform the ingested data, optionally converting it into columnar formats like Parquet or ORC. The refined datasets can then be queried on-demand using Amazon Athena or accessed via Redshift Spectrum if integrated into a Redshift data warehouse.
In this scenario, it’s important to evaluate cost, latency, and scalability. You should know when Glue might hit performance bottlenecks and when a more customizable EMR-based Spark cluster becomes the better fit. The exam will probe your ability to make such distinctions.
Enabling Ad-Hoc Data Analysis for Business Analysts
In many organizations, business analysts need access to sales, customer, or marketing data to explore trends and correlations. However, they typically do not manage infrastructure or handle backend data processes.
For these cases, a clean solution involves storing semi-structured or structured data in Amazon S3 and leveraging Amazon Athena for serverless querying. To improve performance and minimize costs, the data should be stored in partitioned Parquet format. QuickSight can be used as a lightweight business intelligence tool to visualize the results.
This pattern requires no provisioning, scales automatically, and allows data exploration via SQL. The exam may frame a scenario where analysts need frequent data access, and your recommendation must emphasize ease of use, low overhead, and cost-efficiency.
Implementing Secure Interdepartmental Data Sharing
Data governance is often a paramount concern, especially when datasets are shared across multiple departments with varying access requirements. A robust solution entails using AWS Lake Formation, which can restrict access down to the table and even column level.
By defining data access policies in Lake Formation and integrating them with Glue Data Catalog, you can manage permissions centrally. KMS can handle encryption, while IAM roles define who can access which datasets. CloudTrail can monitor and log all access events to provide an auditable history of data consumption.
In an exam scenario where compliance and privacy are central, opting for Lake Formation over traditional IAM or bucket policies will often be the correct path. Knowing how to enforce least-privilege access principles across services is essential.
Making Strategic Service Selections Without Confusion
AWS offers multiple services with overlapping functionalities, especially in the data space. To choose the right one, focus on the use case rather than the technology. For real-time ingestion with custom data transformations, Kinesis Data Streams provides granular control. When simplicity is preferred, such as streaming data directly to S3 or Redshift with built-in compression and transformation, Kinesis Firehose is more appropriate.
Glue is optimal for managed ETL jobs that benefit from automatic schema inference and minimal infrastructure management. However, if the job requires heavy data lifting, custom transformations, or external libraries, then EMR’s flexibility and compatibility with frameworks like Spark and Hadoop are better suited.
When building a warehouse, Redshift offers columnar storage and rapid aggregation. On the other hand, if the task only requires querying S3-based datasets without persistent infrastructure, Athena shines with its serverless model.
AWS S3 continues to be the backbone for unstructured and semi-structured data, with the ability to serve both as a data lake and a cost-efficient archival solution. Its durability, event notification capabilities, and storage class tiers make it indispensable for Big Data workflows.
Enhancing Performance through Architectural Choices
Maximizing performance is not about throwing more resources at the problem—it’s about making intelligent decisions.
One such decision involves data formats. Columnar formats like Parquet or ORC should be the default choice for large-scale analytical queries. These formats allow selective reading of columns, drastically reducing I/O and scan costs.
Partitioning your S3 datasets by fields such as date or region enables services like Athena or Redshift Spectrum to skip irrelevant files, improving query response times. Bucketing, while more often used in Hive and EMR, can optimize join operations by reducing data shuffling.
Using services like QuickSight SPICE for in-memory data caching, Redshift materialized views for query reuse, and DynamoDB with DAX for low-latency retrieval can further accelerate analytics.
Implementing Robust Monitoring and Troubleshooting Mechanisms
Operational observability is essential to maintaining performance, availability, and data integrity. The exam often introduces scenarios involving failed ETL jobs, missing records, or slow queries.
Amazon CloudWatch should be used to set alarms for Glue job failures, Kinesis lag, or Redshift CPU spikes. Logs from Glue, EMR, or Lambda can be directed to CloudWatch Logs for in-depth troubleshooting.
CloudTrail is indispensable when you need to audit access patterns or determine if a user or service modified or deleted resources. For compliance-sensitive environments, pairing CloudTrail with AWS Config provides both change history and resource compliance snapshots.
Strengthening Data Security and Governance in AWS
Security is not a discrete domain in AWS—it’s a continuous layer across all components.
Encryption should always be applied both in transit and at rest. For instance, Amazon S3 supports multiple encryption mechanisms, including server-side encryption with S3-managed keys, KMS-managed keys, and client-side encryption. Redshift supports encryption of both data blocks and connections.
When sharing data with fine-grained permissions, IAM alone may not suffice. In these scenarios, use Lake Formation to define access at the database, table, or column level. Redshift and Athena can integrate directly with these permissions to enforce access controls.
Always employ the least-privilege model when defining IAM roles. Combine ABAC (attribute-based access control) with resource tags to allow dynamic and scalable permissioning.
For data residency and compliance requirements, such as HIPAA or GDPR, ensure proper key rotation policies, lifecycle management, and encryption standards are enforced. Data classification tools can also help label sensitive data for policy enforcement.
Controlling Costs Without Compromising Value
Cost optimization is often a blind spot for exam candidates but is deeply embedded in the Big Data Specialty exam. You must constantly balance performance with price.
Using Parquet instead of JSON can reduce Athena query costs by 80 percent or more. Setting up Glue job bookmarking ensures you process only new data, saving on compute charges. Applying S3 lifecycle policies can automatically move stale data to Glacier or Deep Archive, drastically cutting storage expenses.
Redshift can be made more economical through Reserved Instances or usage of concurrency scaling to handle peak loads. With EMR, selecting spot instances for worker nodes and autoscaling the cluster can significantly reduce compute overhead.
Lambda functions, because they are charged by execution time and memory used, provide a predictable cost model ideal for small, event-triggered jobs. Combining these strategies ensures a lean yet robust data platform.
Practicing Exam Scenarios and Decision Logic
In the exam, you’ll encounter complex scenarios with multiple correct options. Your ability to discern the most cost-effective, secure, and high-performing architecture will be tested.
Take a case where mobile logs need to be analyzed without infrastructure. The correct solution would be storing logs in S3 using Parquet format, querying with Athena, and visualizing using QuickSight. This model is simple, serverless, and cost-effective.
Or consider a Redshift cluster showing signs of performance degradation. A nuanced response would involve reviewing distribution and sort keys, applying compression, and offloading cold data to Redshift Spectrum.
Another scenario may involve restricting access to financial datasets. Instead of using IAM alone, deploying Lake Formation ensures data governance down to the column level, aligning with regulatory requirements.
The Final Ascent
You’ve explored the foundational principles of AWS Big Data services and practiced applying them to nuanced, real-world scenarios. Now comes the final stretch—the strategic polishing required to cross the certification finish line with assurance. Part 3 delves into exam-day methodologies, psychological readiness, cognitive traps to avoid, and tactical study blueprints that fortify your preparation. This is where precision meets performance.
The Anatomy of the Exam
The AWS Certified Big Data – Specialty exam features 65 questions in a multiple-choice, multiple-answer format. You have 170 minutes to complete the exam, and a passing score is typically around 750 out of 1000, although AWS doesn’t explicitly disclose grading metrics.
Expect scenario-based questions that focus less on memorization and more on architectural soundness. Services are not evaluated in isolation but in combinations that solve complex business problems. Each question will test your ability to analyze needs, identify constraints, and select the most appropriate service or pattern.
Core Competencies to Master
To score confidently, you must develop expertise in the following key domains:
- Data Collection – Understanding ingestion patterns via Kinesis, Firehose, DMS, and IoT Core
- Storage Solutions – Designing efficient data lakes, optimizing S3 structure, and implementing Redshift
- Data Processing – Orchestrating workflows through Glue, EMR, Lambda, and step functions
- Analysis and Visualization – Crafting insight pathways with Athena, QuickSight, and Redshift Spectrum
- Security – Employing encryption, permission boundaries, auditing, and data classification
- Monitoring and Automation – Using CloudWatch, CloudTrail, and auto-scaling patterns in data pipelines
Each domain carries significant weight, but the exam consistently emphasizes processing and analysis—so allocate your time accordingly.
Question Dissection Strategies
When approaching a question, use a methodical four-step approach:
- Deconstruct the Scenario
Read the scenario slowly, identifying the core objective. What is the business goal? What constraints are imposed (cost, latency, compliance, etc.)? - Highlight Service Clues
Look for keyword cues like real-time, batch, encrypted, serverless, or highly available. These often hint at which AWS service or pattern is being tested. - Apply the Elimination Method
Immediately remove answers that are obviously incorrect, such as services not designed for the use case or ones that ignore constraints. - Validate the Remaining Options
Cross-reference the remaining answers with your mental checklist: does the solution scale? Is it cost-aware? Is it secure? Is it unnecessarily complex?
Avoid overengineering. The best answer is often the most practical, not the most technically elaborate.
Practice Question Breakdown
Let’s evaluate a few sample questions to develop an exam-ready mindset.
Scenario: A company needs to ingest high-velocity streaming data from IoT devices into a storage solution for later analysis. The system must support real-time analytics and also store raw data for future batch processing. What solution is most appropriate?
Best Answer: Use Amazon Kinesis Data Streams to ingest the data, then use Kinesis Data Analytics for real-time processing, and deliver raw data to S3 via Firehose.
Analysis: This solution meets the dual need for real-time and long-term storage. Kinesis Streams is ideal for granular processing, and Firehose delivers raw payloads to S3 efficiently. Kinesis Analytics adds the transformation layer.
Scenario: Your organization needs to enable ad-hoc SQL querying of JSON logs stored in S3. The solution must be serverless and cost-effective.
Best Answer: Use Amazon Athena with the Glue Data Catalog.
Analysis: Athena is purpose-built for querying data directly from S3 using SQL. Glue provides the schema. There’s no infrastructure to manage, aligning with the serverless requirement.
Scenario: A financial firm needs to limit access to columns in sensitive datasets stored in S3. Teams require access to different parts of the data. What approach should you take?
Best Answer: Use Lake Formation to define column-level permissions and enforce policies with Glue and Athena.
Analysis: Lake Formation enables fine-grained access control. IAM policies alone cannot enforce column-level granularity. This question evaluates governance and compliance awareness.
Designing a Study Framework
A well-organized preparation plan is critical. A structured five-week strategy ensures you build confidence and retain depth.
Week 1: Service Familiarization
Dedicate time to understanding the core services: S3, Redshift, Glue, EMR, Athena, Kinesis, and QuickSight. Use the AWS documentation and free tier to experiment with configurations, create test datasets, and run simple queries.
Week 2: Deep Dives and Edge Cases
Move into advanced topics like Lake Formation, streaming analytics, and Redshift Spectrum. Study how services interact in multi-layered workflows. Focus on IAM intricacies, VPC endpoints, encryption options, and security layers.
Week 3: Practice Exams and Pattern Recognition
Take your first set of practice exams. Analyze each wrong answer. Focus not just on the correct service but why other options fail. Begin to see patterns and repeatable architectural themes. Start identifying traps such as over-complication or cost ignorance.
Week 4: Case Studies and Documentation Reviews
Study real-world architectures. Review AWS Big Data whitepapers, particularly those on data lakes and analytics. Read the FAQs and limitations for each core service. These documents often reveal corner cases that appear in exams.
Week 5: Refinement and Mental Rehearsal
Spend this week on mock exams, flashcards, and scenario challenges. Build architectural diagrams from memory. Simulate exam conditions. Eliminate time-sinks. Prioritize rest, hydration, and mental clarity.
Tools to Reinforce Learning
Several learning resources can enhance your preparation:
- AWS Skill Builder – Offers official training, labs, and quizzes tailored to certification paths.
- Tutorials Dojo and Whizlabs – Trusted for high-quality practice questions and scenario-based challenges.
- AWS Whitepapers – Essential for understanding architectural decisions and security best practices.
- YouTube and Cloud Academy – Useful for video-based learning and quick refreshers.
- GitHub Repositories – Some repositories include Big Data exam dumps, use-case summaries, and CloudFormation templates.
Avoid brain-dump websites offering exam questions without explanation. They might violate AWS’s exam policies and dilute your understanding.
Exam Day Strategy
Arrive at the test center or start your remote session with buffer time. Ensure a distraction-free space, government ID, and a stable internet connection if taking it online.
Use the mark for review feature liberally. If a question feels ambiguous, skip and return later. Many questions require contextual thinking that becomes clearer as you progress.
Focus your initial sweep on high-confidence questions. This builds momentum. On the second pass, dive into the complex ones, eliminating distractions and anchoring answers with clear reasoning.
Don’t second-guess unless you’re certain. Your first answer is often right when rooted in practiced decision-making.
Pitfalls That Derail Candidates
Numerous exam-takers fall prey to common missteps:
- Overemphasizing a Single Service – While mastering Redshift or Glue is important, ignoring lesser-known services like DataBrew or DataSync can cost points.
- Underestimating Governance Questions – Expect detailed queries about access control, encryption, and auditability.
- Forgetting Cost Optimization – Many answers appear correct but violate cost-efficiency. Watch for hidden expense traps.
- Ignoring Format Efficiency – Questions often hint at the wrong storage format. Choosing JSON over Parquet in analytics scenarios is a frequent error.
- Disregarding Automation – Preference should be given to scalable, serverless, and automated solutions unless otherwise specified.
Recognizing these traps ahead of time gives you an edge over reactive test-takers.
Post-Certification Advantage
Achieving the AWS Certified Big Data – Specialty credential isn’t just a badge—it signals mastery over modern data architectures. It positions you for specialized roles such as:
- Data Engineer
- Big Data Solutions Architect
- Cloud Analytics Consultant
- Machine Learning Pipeline Specialist
It also opens doors to AWS Partner Network roles and advanced certifications like the AWS Certified Data Analytics – Specialty.
Beyond career benefits, the discipline and knowledge gained elevate your competency in architecting end-to-end data systems that are secure, performant, and cost-effective.
Conclusion:
This final segment of the AWS Certified Big Data – Specialty cheat sheet journey completes your roadmap. You’ve traversed foundational theory, engaged with real-world application, and absorbed tactical strategies for exam day excellence.
This certification doesn’t merely test your technical chops—it examines your ability to see the forest and the trees. It rewards those who balance engineering expertise with business insight, scalability with cost, and innovation with governance.
Step into the exam room not as a hopeful candidate, but as a poised data strategist. You now carry the tools, patterns, and mindset to not only pass the AWS Big Data Specialty exam—but to architect data systems that transform raw information into enterprise intelligence.