AWS Data Services Questions

Specialized knowledge of Amazon Web Services targeted at data storage, processing, analytics, and streaming. This covers object storage and data lake design with Simple Storage Service including storage classes, lifecycle and partitioning strategies; analytics and warehousing with Redshift including columnar storage, distribution styles, compression, query optimization and concurrency considerations; big data processing with Elastic MapReduce for managed Spark and Hadoop clusters and associated tuning; serverless extract transform and load using Glue and data catalog concepts, schema management and job orchestration; and real time data ingestion and processing with Kinesis including producers, shards, retention, consumers, and stream processing patterns. Candidates should understand when to choose batch versus streaming architectures, how to integrate services into end to end data pipelines, trade offs around scalability, latency, consistency, security, data governance and cost optimization, and monitoring and debugging techniques for data workloads.

HardTechnical

0 practiced

Explain design patterns to achieve exactly-once processing semantics when using Kinesis Data Streams with Spark Structured Streaming on EMR or Glue. Cover checkpointing, offsets, idempotent sinks, deduplication windows, and practical approaches for sinks like S3 and Redshift.

EasyTechnical

0 practiced

Define partitioning strategies for S3-based analytics datasets (for example event data). Suggest good partition keys and explain pitfalls such as too-fine-grained partitions, many tiny files, and hot partitions. Provide guidelines for choosing partition granularity.

HardTechnical

0 practiced

An EMR Spark job is failing with frequent GC overhead and shuffle spill messages. Describe step-by-step diagnostics you would perform using the Spark UI, YARN logs and CloudWatch, and list specific tuning actions (executor memory, cores, shuffle partitions, serialization, broadcast join) to resolve the problem.

EasyTechnical

0 practiced

Explain Amazon Kinesis Data Streams core concepts: shard, producer, consumer, retention, and how shard count maps to throughput and parallelism. Describe typical reasons to reshard and basic cost considerations.

MediumTechnical

0 practiced

Describe how you would implement data quality checks in an AWS data pipeline. Provide examples of checks (row counts, null rates, value ranges, referential integrity), where to perform them (ingest vs transform), and how to report and alert on failures using native AWS services.

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