Reinforce DEA-C01 concepts with active-recall study cards covering all 4 blueprint domains. Each card shows the question on the front and the correct answer with a full explanation on the back.
Flashcards work through active recall — the process of retrieving information from memory rather than passively re-reading it. Research consistently shows that active recall produces stronger, longer-lasting memory than re-reading study guides. For DEA-C01 preparation, this means flashcards are one of the highest-return study tools available.
Attempt recall first
Read the DEA-C01 question on each card, pause, and attempt to formulate the answer in your own words before revealing. This retrieval attempt — even if wrong — dramatically strengthens memory compared to immediately reading the answer.
Review wrong cards again
When you get a card wrong, note it and add it back to your review pile. Spaced repetition — seeing difficult cards more frequently — is the mechanism that makes flashcard study far more efficient than linear reading.
Study by domain
Group your DEA-C01 flashcard sessions by domain for the first 3–4 weeks. Master one domain before moving to the next. In the final week, shuffle all cards together to test cross-domain recall — which is what the real DEA-C01 exam requires.
Short sessions beat marathon reviews
20–30 flashcard cards per session, done daily, produces better retention than a single 200-card marathon session. Five short daily sessions per week over 4 weeks gives you over 400 total card reviews — enough to reliably pass DEA-C01.
Sample cards from the DEA-C01 flashcard bank. Read the question, think of the answer, then read the explanation below.
A data engineer needs to ingest streaming data from an IoT fleet into Amazon S3 for near-real-time analytics. The data volume is approximately 5 GB per hour, and each event is less than 1 KB. Which AWS service should be used as the ingestion endpoint?
AWS IoT Core
AWS IoT Core is purpose-built for ingesting data from IoT devices, supporting MQTT, HTTP, and WebSocket protocols. It can handle millions of devices and high-throughput, small-message payloads (each event <1 KB) and integrates directly with Amazon S3 via IoT Core rules, making it the ideal ingestion endpoint for near-real-time analytics on streaming IoT data.
A company uses AWS Glue ETL jobs to transform data from Amazon S3 to Amazon Redshift. The job reads JSON files, applies schema mapping, and writes to a Redshift table. Recently, the job started failing with memory errors. The data volume has increased tenfold. Which approach should a data engineer take to resolve this issue with minimal code changes?
Increase the number of DPUs allocated to the Glue job.
Increasing the number of DPUs (Data Processing Units) allocated to the AWS Glue job directly addresses the memory constraint caused by a tenfold increase in data volume. Glue ETL jobs run on Apache Spark, which distributes data processing across executors; more DPUs provide more memory and compute capacity, allowing the job to handle larger datasets without code changes.
A financial services company processes real-time stock trade data. They use Amazon Kinesis Data Streams with a shard count of 5, each shard receiving about 500 records per second. The consumer application uses the Kinesis Client Library (KCL) with DynamoDB for checkpointing. Lately, some records are being processed multiple times. What is the most likely cause?
The consumer application is crashing and restarting, causing re-processing of records.
The Kinesis Client Library (KCL) uses DynamoDB to track checkpoint progress for each shard. If the consumer application crashes and restarts, the KCL will resume processing from the last committed checkpoint, which may be behind the actual processing point. This causes records that were already processed (but not yet checkpointed) to be re-processed, leading to duplicate processing.
A data engineering team needs to transform CSV files stored in Amazon S3 into Parquet format using AWS Glue. The files are partitioned by date and are updated hourly. Which AWS Glue feature should be used to automatically detect the schema and partition structure?
AWS Glue Crawler
AWS Glue Crawler is the correct choice because it automatically scans data in S3, infers the schema (including data types), and detects the partition structure (e.g., date-based partitions like year/month/day) by examining the folder hierarchy. It then populates the AWS Glue Data Catalog with metadata, enabling ETL jobs to read the data without manual schema definition.
An e-commerce company ingests clickstream data from their website into Amazon S3. The data is in JSON format, and each file is about 10 MB. They need to transform the data into a columnar format for analytics and load it into Amazon Redshift nightly. The transformation should be cost-effective and require minimal operational overhead. Which approach meets these requirements?
Use AWS Glue ETL job to convert to Parquet and load into Redshift.
AWS Glue ETL is the correct choice because it is a serverless, managed service that can efficiently convert JSON to Parquet (a columnar format optimized for Redshift) and load the data into Redshift with minimal operational overhead. The nightly batch processing of 10 MB files is well-suited for Glue's pay-per-use pricing, making it cost-effective without requiring infrastructure management.
A company uses AWS Database Migration Service (DMS) to continuously replicate data from an on-premises Oracle database to Amazon S3 in Parquet format. The replication is used for near-real-time analytics. Recently, the DMS task started failing with an error indicating insufficient memory. The source database is large (2 TB). What should a data engineer do to resolve this issue while minimizing changes to the existing architecture?
Increase the DMS replication instance size.
The error indicates the DMS replication instance is running out of memory during continuous replication of a 2 TB Oracle database to S3 in Parquet format. Increasing the replication instance size (Option D) directly addresses the memory constraint by providing more RAM and processing capacity, which is necessary for handling large volumes of Change Data Capture (CDC) data and Parquet conversion overhead. This solution requires minimal architectural changes, as it only involves modifying the instance class in the DMS task settings.
A company uses Amazon Kinesis Data Streams to ingest clickstream data. The data is consumed by a custom consumer application that writes to Amazon S3 every 5 minutes. The consumer is falling behind and processing lag is increasing. Which action is MOST effective to reduce the lag?
Increase the number of shards in the Kinesis stream
The consumer is falling behind because the stream's throughput capacity is insufficient for the incoming data volume. Increasing the number of shards in the Kinesis stream directly increases the total read capacity (each shard provides 2 MB/s read throughput and 5 transactions/second), allowing the consumer to process more data in parallel and reduce lag.
A data team runs a daily AWS Glue ETL job that processes data from an Amazon Redshift cluster and writes results to Amazon S3. The job completes successfully but takes 2 hours longer than expected. The job uses the JDBC connection to Redshift. The Redshift cluster is 4 dc2.large nodes. The Glue job has 10 workers of type G.1X. Which change would MOST likely reduce the job duration?
Use the S3 staging option in the Glue connection to unload data from Redshift to S3 first
The JDBC connection in AWS Glue reads data row-by-row from Redshift, which is slow for large datasets. By enabling the S3 staging option in the Glue connection, the job uses Redshift's UNLOAD command to export data to S3 in parallel, then Glue reads from S3. This bypasses the JDBC bottleneck and leverages Redshift's massively parallel processing (MPP) to export data much faster.
A data engineer runs a Spark job on Amazon EMR that reads data from Amazon S3 and writes results back to S3. The job fails with an 'S3AccessDenied' error. The engineer verifies that the IAM role attached to the EMR cluster has s3:GetObject and s3:PutObject permissions on the relevant buckets. What is the MOST likely cause of the error?
The IAM role does not have s3:ListBucket permission on the bucket.
The IAM role attached to the EMR cluster must have the s3:ListBucket permission on the bucket to allow the Spark job to enumerate objects when reading from S3. Without this permission, even with s3:GetObject and s3:PutObject, the job fails with an 'S3AccessDenied' error because the S3 list operation is required for directory listing and file discovery.
A company uses AWS Glue to process sensitive data stored in Amazon S3. The security team requires that all data in transit between AWS Glue and S3 be encrypted. Which configuration should be used to meet this requirement?
Use an S3 bucket policy that denies requests not using HTTPS.
Requiring HTTPS for all requests to the S3 bucket ensures that data in transit between AWS Glue and S3 is encrypted using TLS. By using an S3 bucket policy with a condition that denies requests where `aws:SecureTransport` is false, the company enforces encryption for all connections, including those from AWS Glue. This meets the security requirement without needing to modify Glue or S3 configurations beyond the bucket policy.
A company is using Amazon EMR to process data stored in Amazon S3. The S3 bucket is configured with a bucket policy that denies access unless the request includes a specific tag. The EMR cluster's IAM role has s3:GetObject permission. However, the EMR job fails to read data from S3. What is the most likely cause?
The IAM role does not have a condition that matches the required tag.
The bucket policy denies access unless the request includes a specific tag. Even though the EMR cluster's IAM role has s3:GetObject permission, the IAM role does not have a condition key (e.g., aws:RequestTag) that matches the required tag. Therefore, the request is denied by the bucket policy, causing the EMR job to fail.
A company uses AWS Lake Formation to manage data lake permissions. The data lake contains sensitive customer data in the 'customer' database. The security team wants to ensure that only users with a specific tag 'access_level=analyst' can query the 'customer' table. Which combination of steps should the data engineer take to enforce this?
In Lake Formation, create an LF-tag 'access_level' with values 'analyst' and 'admin'. Grant 'SELECT' permission on the 'customer' table to the tag value 'analyst'. Associate the LF-tag with the 'customer' table.
Lake Formation LF-tags allow you to define metadata tags (key-value pairs) and grant permissions to those tags. By creating an LF-tag 'access_level' with values 'analyst' and 'admin', granting SELECT on the 'customer' table to the tag value 'analyst', and associating that LF-tag with the table, only principals who have the tag 'access_level=analyst' (or are granted via the tag) can query the table. This enforces tag-based access control at the Lake Formation permission layer, which is the intended mechanism for fine-grained, attribute-based access control in Lake Formation.
A company has an Amazon RDS for MySQL DB instance with read replicas. The primary DB instance fails. What is the correct procedure to promote a read replica to become the new primary?
Manually promote the read replica to a standalone DB instance.
When an Amazon RDS for MySQL primary DB instance fails, read replicas do not automatically become the new primary. The correct procedure is to manually promote the read replica using the AWS Management Console, CLI, or API, which converts it into a standalone DB instance. After promotion, you must update your application endpoints to point to the new primary, as RDS does not handle this automatically.
A company uses Amazon DynamoDB for a gaming application. They need to store player session data that expires after 24 hours. Which DynamoDB feature should they use to automatically delete expired items?
Time to Live (TTL)
DynamoDB Time to Live (TTL) is the correct feature because it allows you to define a per-item timestamp attribute (e.g., `expireAt`) that DynamoDB automatically deletes once that timestamp is reached. This is ideal for expiring session data after 24 hours without requiring custom code or scheduled jobs to scan and delete items, reducing cost and operational overhead.
An e-commerce application uses Amazon ElastiCache for Redis to cache product catalog data. The cache currently uses lazy loading. The team wants to ensure that frequently accessed product data is always fresh. Which caching strategy should they implement?
Write-through caching
Write-through caching ensures that data is written to the cache simultaneously with the database, guaranteeing that frequently accessed product data is always fresh. This strategy eliminates stale reads by synchronously updating the cache on every write, which directly addresses the requirement for freshness without relying on expiration or lazy population.
Refer to the exhibit. A data engineer has attached this bucket policy to an S3 bucket. What is the effect of this policy?
It allows the DataLakeRole to read and write objects, but only over HTTPS.
The bucket policy uses a condition key `aws:SecureTransport` set to `true`, which restricts access to HTTPS (TLS) connections only. The `Principal` is `DataLakeRole`, and the `Action` includes `s3:GetObject` and `s3:PutObject`, so the policy allows that role to read and write objects exclusively over HTTPS, enforcing encrypted data in transit.
The DEA-C01 flashcard bank covers all 4 official blueprint domains published by Amazon Web Services. Cards are distributed proportionally, so domains with higher exam weight have more cards.
Domain Coverage
Data Ingestion and Transformation
Data Operations and Support
Data Security and Governance
Data Store Management
Both flashcards and practice questions are evidence-based study tools. The difference is in what they train:
Flashcards — concept retention
Best for memorising definitions, acronyms, protocol behaviours, command syntax, and conceptual distinctions. Use flashcards to build the foundational vocabulary that DEA-C01 questions assume you know.
Best in: weeks 1–3
Practice tests — application
Best for applying concepts to realistic scenarios, eliminating distractors, and building exam stamina.DEA-C01 questions test scenario reasoning — not just recall — so practice tests are essential.
Best in: weeks 3–6
The most effective DEA-C01 study plan combines both: use flashcards for the first 2–3 weeks to build conceptual foundations, then shift to practice tests and mock exams in the final 2–3 weeks to apply and benchmark that knowledge. Most candidates who pass on their first attempt use both tools.
Yes. Courseiva provides free DEA-C01 flashcards across all official exam domains. Every card includes the correct answer and a full explanation of why it is right and why the distractors are wrong. The platform also includes topic-based practice, mock exams, and readiness tracking — no account required.
Courseiva has 1321+ original DEA-C01 flashcards across all 4 exam blueprint domains. New cards are added regularly as the question bank grows. All cards are checked against the official Amazon Web Services exam objectives, with editorial oversight from an experienced network and security engineer.
Courseiva flashcards are purpose-built for IT certification exams. Unlike generic flashcard platforms where content quality varies, every Courseiva card is mapped to the official DEA-C01 exam blueprint, written by engineers who hold the certification, and includes a full explanation of the correct answer and why the distractors are wrong. This explanation quality is what separates genuine learning from rote memorisation.
Courseiva is a web platform — an internet connection is required. For offline study, we recommend creating free Courseiva account, using the platform in your browser, and using your device's offline capabilities if your browser supports offline web apps.
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