Covers patterns, approaches, and technologies for moving data from source systems into downstream storage and processing platforms. Candidates should understand pull based and push based ingestion models including periodic polling of application interfaces, event driven webhooks, log collection, file based batch uploads, database replication using change data capture, and streaming ingestion. Evaluate trade offs for latency, throughput, ordering, delivery semantics such as at least once and exactly once, backpressure and flow control, idempotency, fault tolerance, and cost. Be familiar with common ingestion technologies and platforms such as Apache Kafka, Amazon Kinesis, Google PubSub, and Apache NiFi as well as managed cloud ingestion and extract transform load services. Topics also include schema management and evolution, data formats such as JavaScript Object Notation and columnar file formats, data validation and cleansing at ingress, security and authentication for ingestion pipelines, monitoring and observability, and operational concerns for scaling and recovery.
EasyTechnical
0 practiced
Compare batch and streaming ingestion for ML workloads. For each mode describe how it affects: freshness of training data, complexity of features (online vs offline features), cost model, operational complexity, and where "nearline" or "micro-batch" fits in. Give concrete recommendations for (a) nightly retrains on large data and (b) online feature updates for personalization models.
HardTechnical
0 practiced
Technical: Create a comprehensive test plan for ingestion pipelines including unit tests, integration tests, property-based tests, synthetic workload tests, chaos tests (e.g., broker failure, network partitions), and data-quality tests. For each test type describe what it verifies, how to automate it in CI/CD, and how often to run heavier tests (e.g., chaos tests).
MediumTechnical
0 practiced
Compare Apache Kafka, Amazon Kinesis, and Google Pub/Sub for AI ingestion use-cases. Evaluate them on throughput, latency, ordering guarantees, regional/global replication, ecosystem integrations (connectors, managed services), operational burden, and cost considerations for both training data collection and real-time feature streams.
EasyTechnical
0 practiced
Define and compare delivery semantics in ingestion systems: "at least once", "at most once", and "exactly once". Explain the practical implications of each for AI model training and inference—for example, how duplicate training examples or dropped feature updates affect model quality and downstream metrics. Provide simple examples of when "at least once" is acceptable versus when "exactly once" is essential.
EasyTechnical
0 practiced
Explain the core components of Apache Kafka and how they relate to ingestion: topics, partitions, brokers, consumer groups, offsets, retention, and compaction. Then describe how partitioning affects ordering and parallelism and why partition-key choice matters for both throughput and correctness of real-time features used by models.
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