The AWS Certified Data Engineer – Associate (DEA-C01) is one of the most relevant AWS certifications for professionals and students who want to build modern data platforms in the cloud. Unlike broader AWS architecture certifications, DEA-C01 focuses specifically on the day-to-day responsibilities of a data engineer: ingesting data, transforming it, selecting suitable storage technologies, building reliable pipelines, monitoring workloads, and protecting sensitive information.
For students and engineers who are relatively new to AWS, this certification can initially appear challenging because the exam covers a wide range of services. Amazon S3, AWS Glue, Amazon Redshift, Amazon Athena, Amazon Kinesis, Amazon EMR, AWS Lake Formation, AWS Lambda, IAM, AWS KMS, and several orchestration and monitoring services may all appear within the same scenario.
The important point is that DEA-C01 is not simply a test of how many AWS service names you can remember. It evaluates whether you understand why one service, architecture, storage format, or processing method is more appropriate than another.
If you are preparing for the certification, it is useful to combine the concepts in this guide with focused
DEA-C01 exam preparation
so that you can identify both your strong areas and the topics that still require additional study.
This guide explains the current DEA-C01 exam structure, the technologies you should understand, the value of the certification, and a practical study approach for candidates who do not want to memorize the entire AWS documentation library.
What Is the AWS Certified Data Engineer – Associate?
AWS Certified Data Engineer – Associate is an Associate-level certification designed around the responsibilities of a cloud data engineer.
Candidates are expected to understand how to implement data pipelines and how to monitor, troubleshoot, and optimize those pipelines for performance and cost. The certification also covers data stores, data modeling, data quality, governance, security, and operational support.
In practical terms, a DEA-C01 candidate should be able to look at a requirement such as:
- Millions of events arrive continuously from applications.
- Raw data must be retained for several years.
- Analytics teams require SQL access to processed datasets.
- Sensitive information must be encrypted and access controlled.
- The pipeline should recover automatically when processing fails.
and translate those requirements into an appropriate AWS architecture.
For example, that architecture might involve Amazon Kinesis Data Streams for ingestion, Amazon S3 for durable storage, AWS Glue for transformation and cataloging, Amazon Athena for serverless SQL queries, and Amazon CloudWatch for monitoring. Another workload might be better served by Amazon MSK, Amazon EMR, Amazon Redshift, or Amazon DynamoDB.
Understanding these architectural trade-offs is much more important than memorizing isolated AWS service definitions.
DEA-C01 Exam Overview
| Exam Detail | Information |
|---|---|
| Certification | AWS Certified Data Engineer – Associate |
| Exam Code | DEA-C01 |
| Level | Associate |
| Exam Duration | 130 minutes |
| Number of Questions | 65 |
| Question Types | Multiple choice and multiple response |
| Passing Score | 720 on a scale of 100–1,000 |
| Exam Price | USD $150, subject to regional pricing and taxes |
| Testing Options | Pearson VUE testing center or online proctored exam |
| Certification Validity | 3 years |
The exam contains 65 questions. AWS uses both scored and unscored questions, and candidates are not told which questions are unscored during the test.
For this reason, you should treat every question as though it contributes to your final result.
There is also no penalty for guessing, so make sure you provide an answer to every question before ending the exam.
Who Should Take the DEA-C01 Exam?
The target audience includes data engineers who already have experience working with data pipelines and AWS services. However, that does not mean students or junior engineers should avoid the certification.
For a beginner, DEA-C01 can provide a structured roadmap for learning cloud data engineering. The main difference is that beginners will need considerably more hands-on practice than someone who already works with AWS data services every day.
Before serious DEA-C01 preparation, you should ideally understand:
- SQL and relational database fundamentals
- ETL and ELT concepts
- Batch processing versus streaming processing
- Structured, semi-structured, and unstructured data
- Data lakes and data warehouses
- Basic Python or another programming language
- Networking, compute, and storage fundamentals
- IAM and cloud security fundamentals
- Git and basic software development workflows
Candidates coming from database administration, business intelligence, Hadoop, Spark, networking, system administration, or application development can also use the certification as a pathway into cloud data engineering.
DEA-C01 Exam Domains and Weightings
The DEA-C01 exam is organized into four major domains:
| Domain | Weight |
|---|---|
| Domain 1: Data Ingestion and Transformation | 34% |
| Domain 2: Data Store Management | 26% |
| Domain 3: Data Operations and Support | 22% |
| Domain 4: Data Security and Governance | 18% |
The weighting is worth paying attention to. Data ingestion, transformation, and storage together account for a substantial part of the exam. These areas should therefore receive a large share of your study time.
Domain 1: Data Ingestion and Transformation – 34%
This is the largest DEA-C01 domain and one of the most important areas to master.
You need to understand how data enters AWS, how it is transformed, and how individual processing stages can be orchestrated into a reliable data pipeline.
Batch vs. Streaming Data
A recurring DEA-C01 concept is knowing when a workload is batch-oriented and when it requires streaming.
For batch workloads, important services include:
- Amazon S3
- AWS Glue
- Amazon EMR
- AWS Database Migration Service (AWS DMS)
- Amazon Redshift
- AWS Lambda
- Amazon AppFlow
For streaming workloads, important technologies include:
- Amazon Kinesis Data Streams
- Amazon Data Firehose
- Amazon Managed Streaming for Apache Kafka (Amazon MSK)
- Amazon Managed Service for Apache Flink
- DynamoDB Streams
Do not study these technologies as isolated products. Compare them against one another.
Ask questions such as:
- Does the application require real-time processing?
- Does the consumer need to replay records?
- How much data will arrive?
- How many consumers need to process the stream?
- Does the organization already use Apache Kafka?
- Is operational simplicity more important than infrastructure control?
Questions based on these trade-offs are much closer to real AWS certification scenarios than questions that simply ask you to define a product.
Data Transformation
Candidates should understand how to select an appropriate transformation service according to data volume, latency, complexity, cost, and operational requirements.
AWS Glue deserves particular attention because it combines serverless ETL capabilities with crawlers, jobs, workflows, and the AWS Glue Data Catalog.
You should also understand when Amazon EMR is more appropriate for large Spark or Hadoop workloads, when Lambda is suitable for lightweight event-driven transformations, and when data transformations can be performed directly inside Amazon Redshift.
File formats also matter.
For analytical workloads, converting CSV or JSON into a columnar format such as Apache Parquet can reduce the amount of data scanned and improve query efficiency. Appropriate partitioning can further improve performance and control cost.
Pipeline Orchestration
A production data pipeline normally involves several AWS services. DEA-C01 therefore expects you to understand orchestration technologies such as:
- AWS Step Functions
- Amazon EventBridge
- Amazon Managed Workflows for Apache Airflow (Amazon MWAA)
- AWS Glue workflows
- AWS Lambda
You should know how pipelines react to events, how scheduled processing works, what happens after a failure, and how messages or notifications can be handled through services such as Amazon SNS and Amazon SQS.
Programming and Infrastructure as Code
DEA-C01 does not require candidates to memorize advanced programming syntax, but you should understand common programming and software engineering concepts.
Python and SQL are particularly useful during preparation.
Candidates should also understand version control, testing, logging, monitoring, CI/CD, and Infrastructure as Code. AWS CloudFormation, AWS CDK, and AWS SAM are useful technologies to recognize.
LLMs in Data Processing
Modern versions of the DEA-C01 exam scope also reflect the growing relationship between data engineering and generative AI.
This does not make DEA-C01 a generative AI certification. Instead, candidates should recognize how modern data architectures may interact with services such as Amazon Bedrock and how unstructured information, vectorization, embeddings, and AI-assisted processing can fit into a data workflow.
Domain 2: Data Store Management – 26%
A good data engineer does not automatically place every dataset into the same database.
DEA-C01 expects candidates to select storage technologies based on access patterns, scalability, performance, cost, durability, and data structure.
Amazon S3 and the Data Lake
Amazon S3 is one of the most important technologies in AWS data engineering.
You should understand:
- S3 storage classes
- S3 Lifecycle rules
- Object versioning
- Partitioned datasets
- Encryption
- Access control
- Data lake architectures
- Integration with Glue, Athena, EMR, and Redshift
Lifecycle management is particularly important. A dataset may begin in frequently accessed storage and later transition into lower-cost archival storage before eventually being deleted according to business or regulatory requirements.
Amazon Redshift
For DEA-C01, Amazon Redshift should be understood as more than simply “AWS’s data warehouse.”
Study concepts such as:
- Distribution strategies
- Sort keys
- Compression
- Redshift Spectrum
- Federated queries
- Materialized views
- COPY and UNLOAD operations
- Workload performance optimization
You should be able to recognize how poor distribution, inefficient queries, unsuitable data layouts, or excessive data movement can affect performance.
Relational, NoSQL, Graph, and Specialized Data Stores
The exam can require you to choose among several database technologies.
Important examples include:
- Amazon RDS
- Amazon Aurora
- Amazon DynamoDB
- Amazon DocumentDB
- Amazon Neptune
- Amazon MemoryDB
- Amazon Keyspaces
The most important factor is the workload’s access pattern.
A relational transactional workload is different from a massive key-value workload. A graph relationship problem is different from an analytical warehouse. Low-latency caching is different from long-term object storage.
AWS Glue Data Catalog and Metadata
Data engineering is not only about storing files. Data requires metadata so systems and users can discover and understand what exists.
You should know how AWS Glue crawlers discover schemas, how the AWS Glue Data Catalog stores metadata, how partitions are maintained, and how catalog information can be consumed by Athena, EMR, and other analytics technologies.
Apache Iceberg and Modern Data Lakes
Apache Iceberg has become increasingly relevant to modern AWS data architectures.
Open table formats allow data engineers to introduce more advanced table management capabilities over data stored in object storage. Candidates should understand the basic purpose of Iceberg and why it can be valuable when managing large analytical datasets.
Amazon S3 Tables is also worth reviewing when preparing for the current exam scope.
Vector Data and Vector Indexes
Another area worth understanding is vector data.
Modern AI workloads often represent content as numerical embeddings. Efficient similarity search requires specialized indexing approaches, including concepts such as:
- HNSW – Hierarchical Navigable Small Worlds
- IVF – Inverted File Index
You do not need to become a machine learning researcher for DEA-C01, but you should understand why vectors are used and how they relate to modern retrieval and generative AI architectures.
Domain 3: Data Operations and Support – 22%
Building a pipeline is only half the job. A production data platform must continue operating reliably after deployment.
This domain focuses on automation, monitoring, troubleshooting, analytics, and data quality.
Monitoring and Troubleshooting
You should be comfortable with technologies including:
- Amazon CloudWatch
- CloudWatch Logs
- AWS CloudTrail
- CloudWatch Logs Insights
- Amazon OpenSearch Service
A typical scenario might describe a Glue job that suddenly takes twice as long to complete, a streaming application that is falling behind, or a pipeline that occasionally fails.
The exam may then ask which metrics, logs, or architectural changes are most suitable for investigating the problem.
Amazon Athena
Amazon Athena is another high-value service for DEA-C01 preparation.
Athena allows SQL queries to run directly against data stored in Amazon S3 without requiring a traditional database server.
Understand how partitioning, compression, and columnar file formats can reduce the amount of data scanned. These design decisions can improve both performance and cost efficiency.
Data Quality
A pipeline is not useful if the resulting data cannot be trusted.
DEA-C01 includes concepts such as:
- Checking missing or empty fields
- Defining data quality rules
- Investigating data consistency
- Data sampling
- Handling data skew
When preparing, think beyond whether a job technically succeeds. A pipeline can finish successfully while still producing incomplete, duplicated, inconsistent, or incorrect data.
Domain 4: Data Security and Governance – 18%
Security represents a smaller percentage of the exam than ingestion or storage, but 18% is still significant. Security principles can also appear indirectly in questions from other domains.
IAM and Least Privilege
You should understand:
- IAM users, groups, and roles
- Identity-based policies
- Resource-based policies
- Custom IAM policies
- Least-privilege access
- Role-based access control
- Tag-based access
- Attribute-based access control
For example, a Lambda function processing objects from Amazon S3 should normally receive an IAM role containing only the permissions it actually requires rather than broad administrative permissions.
AWS Lake Formation
Lake Formation is particularly relevant when organizations require centralized governance over data lake resources.
Understand its relationship with Amazon S3, AWS Glue Data Catalog, Athena, EMR, and Redshift, especially when permissions must be managed at a more granular level.
Encryption
Candidates should understand the difference between encryption at rest and encryption in transit, together with the role of AWS Key Management Service (AWS KMS).
Cross-account access and encryption scenarios are also important because a technically correct data-sharing architecture may still fail when a user or application lacks permission to use the required encryption key.
Secrets and Credentials
Credentials should not be embedded directly in application source code.
Understand when AWS Secrets Manager or AWS Systems Manager Parameter Store can be used to manage credentials and other sensitive configuration values.
Privacy, PII, and Governance
The exam can also include governance concepts such as:
- Personally identifiable information (PII)
- Amazon Macie
- Data sovereignty
- Regional data restrictions
- Audit logging
- AWS Config
- Data sharing
The important lesson is that data engineering does not end when information reaches a storage bucket. You must also understand who can access the data, where it can be stored, how activity is recorded, and how retention requirements are enforced.
Important AWS Services for DEA-C01
The DEA-C01 syllabus covers many AWS technologies. For exam preparation, I would give particular attention to the following:
| Area | Important AWS Services and Technologies |
|---|---|
| Object Storage / Data Lake | Amazon S3, S3 Glacier, S3 Tables, AWS Lake Formation |
| ETL and Data Integration | AWS Glue, AWS Glue DataBrew, Amazon AppFlow |
| Data Warehouse | Amazon Redshift |
| Serverless Query | Amazon Athena |
| Big Data Processing | Amazon EMR |
| Streaming | Kinesis Data Streams, Amazon Data Firehose, Amazon MSK, Managed Service for Apache Flink |
| Databases | Amazon RDS, Aurora, DynamoDB, DocumentDB, Neptune, MemoryDB |
| Migration | AWS DMS, AWS DataSync, AWS Transfer Family |
| Orchestration | AWS Step Functions, Amazon MWAA, EventBridge, Glue workflows |
| Serverless Processing | AWS Lambda |
| Monitoring | Amazon CloudWatch, CloudWatch Logs, AWS CloudTrail |
| Security | IAM, AWS KMS, Secrets Manager, Amazon Macie |
| Infrastructure as Code | AWS CloudFormation, AWS CDK, AWS SAM |
| Modern Data / AI | Amazon Bedrock, SageMaker, vector indexing concepts, Apache Iceberg |
Do not interpret this table as a replacement for the official exam guide. Use it as a roadmap for deciding where to spend your study time.
How Difficult Is the DEA-C01 Exam?
I would describe DEA-C01 as a moderately difficult Associate-level AWS certification, particularly for candidates who have not previously worked with real data pipelines.
The challenge is usually not one deeply technical topic. Instead, the difficulty comes from having to understand many connected AWS services and select the best architecture under a specific set of constraints.
A question may provide several solutions that could technically work. Your task is often to identify which solution is:
- Most operationally efficient
- Most scalable
- Most cost-effective
- Most secure
- Most fault tolerant
- Best aligned with the stated requirement
This is why memorizing service definitions alone is rarely enough.
After studying a topic, working through
DEA-C01 practice questions
can help you become more familiar with the way multiple AWS services are compared within a single scenario.
Hands-On Skills You Should Practice
If possible, build a small AWS data platform instead of studying entirely from slides or notes.
A useful practice project could involve the following steps:
- Create an S3 bucket for raw data.
- Upload CSV or JSON datasets.
- Create an AWS Glue crawler.
- Populate the AWS Glue Data Catalog.
- Query the dataset with Amazon Athena.
- Create a Glue ETL job that converts the data to Parquet.
- Partition the processed dataset.
- Run Athena queries against the optimized data.
- Create CloudWatch monitoring for pipeline activity.
- Configure IAM roles using least-privilege permissions.
- Enable encryption using AWS KMS where appropriate.
After completing this foundation, extend the lab with Kinesis, Lambda, Step Functions, Redshift, or Lake Formation.
A small working project often teaches more than several hours of passive reading because it exposes the relationships between IAM, storage, schemas, permissions, logs, and data processing.
A Practical DEA-C01 Study Strategy
Phase 1: Learn the Core Data Architecture
Start with Amazon S3, AWS Glue, Athena, Redshift, RDS, DynamoDB, Lambda, and IAM.
These technologies provide a foundation for understanding most AWS data engineering architectures.
Phase 2: Learn Streaming and Large-Scale Processing
Move on to Kinesis, Amazon MSK, Amazon EMR, and Managed Service for Apache Flink.
Focus on the differences between streaming and batch workloads. Understand how throughput, latency, replay requirements, consumer patterns, and operational overhead influence the correct service choice.
Phase 3: Learn Orchestration and Operations
Study Step Functions, MWAA, EventBridge, CloudWatch, CloudTrail, SNS, and SQS.
Do not only learn how a pipeline starts. Learn what happens when it fails.
Phase 4: Learn Security and Governance
Review IAM, KMS, Secrets Manager, Lake Formation, Macie, AWS Config, encryption, auditing, data sovereignty, and least privilege.
Phase 5: Review Modern DEA-C01 Topics
If your training materials are older, make sure you separately review newer topics such as:
- Apache Iceberg
- Amazon S3 Tables
- Amazon Bedrock
- LLMs for data processing
- Vectorization
- HNSW and IVF vector indexes
- Amazon Aurora
- Modern AWS data catalog and governance concepts
Phase 6: Practice Scenario-Based Questions
When reviewing a question, do not stop after finding the correct answer.
Ask yourself why each incorrect option is wrong.
For example:
A company stores several terabytes of compressed application logs in Amazon S3 and wants analysts to run occasional SQL queries without maintaining servers.
Amazon Athena immediately deserves consideration because it can run serverless SQL queries directly against data in S3. Amazon Redshift could technically support analytics, but provisioning a warehouse may introduce unnecessary complexity for an occasional-query requirement.
This type of reasoning is central to DEA-C01.
A structured
DEA-C01 study guide and question set
can be useful during this phase because it helps expose topics where you understand the AWS service itself but still struggle to select it correctly in an exam scenario.
Common Mistakes When Preparing for DEA-C01
1. Memorizing Service Names
Knowing that Glue performs ETL or that Kinesis supports streaming is only the beginning. Learn each service’s purpose, limitations, integrations, operational model, and common use cases.
2. Ignoring Cost
AWS certification questions frequently include cost and operational efficiency as architectural requirements.
Understand serverless versus provisioned architectures, data scanning costs, storage tiers, compression, lifecycle rules, and data transfer.
3. Ignoring File Formats and Partitioning
Parquet, compression, partitioning, and schema design may appear less exciting than learning another AWS service, but they are fundamental data engineering concepts.
4. Studying Only Traditional ETL Topics
Modern data engineering has expanded beyond classic ETL. Candidates should also be familiar with newer topics involving open table formats, vectors, AI-assisted processing, and modern data governance.
5. Leaving Security Until the Final Day
Security and governance account for a meaningful part of the exam. IAM and KMS also appear indirectly in many questions from other domains.
Is AWS Certified Data Engineer – Associate Worth It?
For someone pursuing AWS data engineering, analytics engineering, cloud engineering, or data architecture roles, DEA-C01 is a useful certification because its syllabus maps well to technologies commonly used in real AWS data platforms.
The certification demonstrates familiarity with areas such as:
- Cloud data lakes
- Data warehouses
- ETL and ELT
- Streaming platforms
- SQL analytics
- Data governance
- Pipeline automation
- Monitoring and troubleshooting
- Cloud security
For students, preparing for the exam can provide a structured way to learn how different AWS services fit together.
At the same time, certification should not be treated as a substitute for hands-on ability.
Someone who can explain a Kinesis-to-S3-to-Glue-to-Redshift pipeline, troubleshoot it, identify where costs arise, and describe its IAM model will demonstrate much stronger engineering ability than someone who can only list AWS service definitions.
The best combination is:
certification knowledge + hands-on labs + SQL/programming skills + a small portfolio project.
DEA-C01 vs. Other AWS Certifications
Students sometimes struggle to decide whether DEA-C01 should be their first AWS Associate certification.
If your goal is specifically data engineering, the
AWS Certified Data Engineer – Associate DEA-C01 certification
is the most directly relevant AWS credential.
AWS Certified Solutions Architect – Associate provides broader architectural knowledge, while AWS Certified Developer – Associate focuses more heavily on application development. Machine learning certifications are more relevant when your responsibilities shift toward model development, deployment, and ML operations.
There is significant overlap between these roles, but DEA-C01 goes deeper into data ingestion, data stores, transformation pipelines, analytics, data quality, and governance.
How Long Should You Study for DEA-C01?
There is no universal preparation period.
An experienced AWS data engineer may need only focused exam preparation, while a student learning AWS data technologies for the first time may need several months.
For a beginner, a reasonable approach is to spend approximately 8–12 weeks combining theory, hands-on labs, documentation review, and question practice instead of attempting to compress the entire syllabus into a few days.
More important than the exact number of weeks is whether you can confidently explain architectural decisions.
Before scheduling the exam, you should be able to answer questions such as:
- When should I choose Kinesis instead of Amazon MSK?
- When should I choose AWS Glue instead of Amazon EMR?
- Why would I convert CSV data to Parquet?
- When is Athena preferable to Redshift?
- How can S3 partitioning reduce query cost?
- How should a data pipeline recover from failure?
- How can IAM provide least-privilege access?
- How do Lake Formation and Glue Data Catalog work together?
- How should sensitive information be encrypted?
- What problem does Apache Iceberg solve?
- Why are vector indexes used for similarity search?
If you can answer most of these without simply recalling a memorized definition, your preparation is moving in the right direction.
DEA-C01 Exam Day Tips
Read each question carefully and identify the requirement before evaluating the answer choices.
Words and phrases such as least operational overhead, lowest cost, real time, highly available, serverless, least privilege, or minimal code changes can completely change which solution is best.
If two answers appear technically correct, return to the exact requirement. AWS certification questions often test whether you can distinguish a workable solution from the solution that best satisfies both technical and business constraints.
Do not spend excessive time on one difficult question. Mark it for review and continue.
During your final revision, you can use
DEA-C01 AWS exam questions and preparation resources
to revisit weaker domains and become more comfortable with the wording and structure of scenario-based questions.
Frequently Asked Questions About DEA-C01
What does DEA-C01 stand for?
DEA-C01 is the exam code for the AWS Certified Data Engineer – Associate certification.
How many questions are on the DEA-C01 exam?
The exam contains 65 questions, including both scored and unscored items.
What is the DEA-C01 passing score?
The minimum passing scaled score is 720 out of 1,000.
How long is the AWS Data Engineer Associate exam?
The examination duration is 130 minutes.
How much does DEA-C01 cost?
The standard exam price is USD $150, although taxes, exchange rates, and regional pricing can affect the final cost.
Is coding required for DEA-C01?
You do not need to memorize advanced programming syntax. However, you should understand programming concepts and the role of languages such as Python and SQL in data engineering workflows.
Is SQL important for DEA-C01?
Yes. SQL is one of the most useful practical skills for this certification. You should be comfortable querying and transforming data and understand how SQL is used with technologies such as Amazon Athena and Amazon Redshift.
Does DEA-C01 include generative AI?
Modern DEA-C01 preparation should include awareness of concepts such as LLM-assisted data processing, vectorization, embeddings, vector indexes, and Amazon Bedrock. However, DEA-C01 remains primarily a data engineering certification rather than a dedicated generative AI exam.
Is DEA-C01 suitable for beginners?
A motivated beginner can prepare for DEA-C01, but beginners should spend additional time on hands-on AWS labs, SQL, ETL concepts, storage architecture, monitoring, and IAM.
How long is AWS Certified Data Engineer – Associate valid?
AWS certifications are generally valid for three years, after which candidates need to meet AWS recertification requirements to maintain an active credential.
Final Thoughts
The DEA-C01 AWS Certified Data Engineer – Associate is a valuable certification for anyone who wants to understand how modern cloud data pipelines are designed and operated on AWS.
Its real value comes from the breadth of engineering decisions it requires you to understand. You need to know how data enters AWS, how it is transformed, where it should be stored, how analysts consume it, how pipelines are monitored, and how security and governance are maintained throughout the data lifecycle.
For beginners, the number of AWS services can feel overwhelming at first. Do not attempt to learn every service to the same depth. Start with the architecture: S3, Glue, Athena, Redshift, Kinesis, EMR, Lambda, IAM, and CloudWatch. Once those relationships make sense, expand into Lake Formation, orchestration, migration tools, open table formats, vector workloads, and newer AI-related data technologies.
Most importantly, practice making architectural decisions rather than simply memorizing product descriptions.
If you understand not only what an AWS service does but also why you would choose it over another service, you will be much better prepared for both the DEA-C01 exam and real-world AWS data engineering work.
For candidates who want additional practice after reviewing the concepts above, the
DEA-C01 AWS Certified Data Engineer Associate course
can be used as an additional revision resource alongside hands-on AWS practice and the official AWS documentation.
Official References:
Note: AWS periodically updates certification exam guides, services, and exam policies. Always review the latest official AWS exam guide before scheduling your exam.
