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44 multiple-choice questions and 19 flashcards on Data Ingestion and Transformation, about 29% of the AWS Data Engineer Associate bank. Every one carries a written rationale.
Data Ingestion and Transformation is one of 4 chapters in CoStudy's AWS Data Engineer Associate bank, and it holds 44 of the bank's 150 multiple-choice questions — roughly 29% of the total. That proportion is not arbitrary: chapters follow the certifying body's published exam outline, and the number of questions in each is set by that domain's published weight, so the share of your practice time this chapter takes matches the share of the real exam it accounts for.
Studying by chapter is worth doing once you have a diagnostic score. A single overall percentage tells you whether you are close; it does not tell you which domain is dragging. Working a weak chapter in isolation, and re-testing it in isolation, is the fastest way to move a score that has stalled — and it is why the mock exams in CoStudy report by domain rather than as one number.
10 questions drawn from this chapter, with the full rationale shown — the controlling principle behind the right answer, and why each wrong option tempts and fails.
Which is TRUE about a Kinesis Data Streams shard?
Answer: C — It provides 1 MB/s ingest and 2 MB/s egress with a 1000 records/s write cap
A) The numbers are reversed — this is the direction-reversal trap. C) Correct: 1 MB/s in, 2 MB/s out, 1000 records/s write cap. Enhanced fan-out lifts per-consumer read limits. B) Those are inflated fabricated numbers. D) Streams scale by adding shards; they are not auto-unlimited.
Which is TRUE about Kinesis Data Streams shard iterator types?
Answer: B — AT_TIMESTAMP starts reading from records at or after a specified timestamp
B) AT_TIMESTAMP is the timestamp-based iterator. A) LATEST reads new records after subscription. C) TRIM_HORIZON reads from the OLDEST available record. D) AT_SEQUENCE_NUMBER is exact-sequence-based, not clock-based.
Which is the BEST way to ingest hundreds of small IoT MQTT messages per second?
Answer: A — AWS IoT Core routing to Kinesis Data Streams or Firehose
A) IoT Core natively handles MQTT at scale and routes to Kinesis, Firehose, or Lambda. B) Direct API Gateway per device does not scale device-side. C) Handing devices AWS credentials is a security anti-pattern. D) Per-device rules do not scale.
Which is the DEFAULT buffering behavior of Kinesis Data Firehose to S3?
Answer: B — 300 seconds or 5 MB, whichever comes first
A) Off-by-one trap — this is Firehose's minimum, not the default. B) The default is 300 seconds or 5 MB — configurable 60 s to 900 s and 1 MB to 128 MB. C) 15 min / 128 MB are the maximums. D) Sub-second buffering is not a Firehose option.
Which is the MAIN reason to use DMS Serverless over the classic DMS replication instance?
Answer: B — Automatic capacity scaling in DCUs eliminates instance sizing decisions
B) DMS Serverless auto-scales in DMS Capacity Units — no instance sizing. A) Classic DMS supports Oracle CDC too. C) Source coverage is nearly identical. D) Cross-region works in both.
Which service is BEST for a sub-15-minute S3-triggered JSON-to-Parquet transform on small files?
Answer: C — AWS Lambda triggered by the S3 ObjectCreated event
A) EMR is over-provisioned for tiny event-driven work. B) Glue jobs have longer cold-start than Lambda for tiny files. C) Lambda is the simplest, fastest, cheapest option for sub-15-minute event-driven transforms. D) COPY loads into Redshift; it does not produce Parquet on S3.
Which Kinesis Data Streams consumer type gives dedicated per-consumer throughput up to 2 MB/s?
Answer: C — An enhanced fan-out consumer registered with the stream via SubscribeToShard
A) Classic consumers share the shard's 2 MB/s across all attached readers. B) Standard Lambda event source shares shard throughput too. C) Enhanced fan-out gives each registered consumer a dedicated 2 MB/s per shard via HTTP/2 push. D) Firehose is a delivery destination, not a fan-out mechanism.
Which service is BEST for streaming CDC from an Aurora MySQL cluster into an S3 raw lake with schema evolution?
Answer: B — AWS DMS with Aurora as source and S3 as target using CDC-only mode
B) DMS supports Aurora MySQL CDC and can write versioned change files to S3 partitioned by table. A) Crawlers do not read CDC. C) RDS event notifications describe DB events, not row changes. D) Custom tailing is fragile and reinvents DMS.
When is Glue DataBrew a better fit than a Glue job?
Answer: D — Visual, code-free data preparation done by analysts with reusable recipes
A) That is Glue jobs on Spark. D) DataBrew provides a code-free UI with 250+ transformations and reusable recipes for analyst-friendly prep. C) DataBrew is not for streaming. B) That is SageMaker or Redshift ML.
A team ingests 10 MB/s of clickstream records that MUST preserve per-key ordering and be consumed by two custom Java KCL applications. Which service fits BEST?
Answer: A — Kinesis Data Streams with shards partitioned on the ordering key
B) Firehose is delivery-only — no custom KCL consumers can attach. A) Streams preserve order within a shard by partition key and support multiple KCL consumers with enhanced fan-out. C) SQS Standard is best-effort ordering, not per-key strict. D) MSK works but is heavier operationally and unnecessary when native Kinesis fits the throughput.
4 cards from the 19 in this chapter.
Schema Registry?
Glue component to manage Avro/JSON/Protobuf schemas with versioning + compatibility checks. Used by Kinesis, MSK, Lambda.
What does Glue DataBrew do?
Visual data preparation — explore datasets and create reusable transformation recipes without writing code; analyst-friendly.
Glue DPU?
Data Processing Unit — 4 vCPU + 16 GB RAM. Glue jobs are billed per DPU-hour.
When use Kinesis vs SQS vs MSK?
Kinesis: real-time analytics streams. SQS: decoupled microservice queue. MSK: managed Kafka for high-throughput streaming.
These are a sample. The full Data Ingestion and Transformation chapter runs 63 items with per-chapter progress tracking, on the web and in the iOS app.
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