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13.6.1: Review Questions

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    Review Questions

    Multiple Choice

    1. Which cloud analytics service is serverless, charges per query based on data scanned, and runs SQL directly on S3?

      • a) Amazon Redshift b) Amazon Athena c) Azure Synapse d) Amazon QuickSight

    2. HSBC's cloud migration to Google Cloud for risk modeling achieved approximately:

      • a) 2x faster analysis b) 5x faster analysis c) 16x faster analysis d) 100x faster analysis

    3. Which cost optimization strategy involves using spare cloud capacity at steep discounts for fault-tolerant batch jobs?

      • a) Reserved instances b) On-demand pricing c) Spot/preemptible instances d) Storage tiering

    Short Answer

    1. Compare AWS Redshift, Google BigQuery, and Azure Synapse. How does each approach compute and storage? Which uses a truly serverless model?

    2. Describe the four-layer serverless analytics architecture (ingest → store → process → serve). Give specific AWS services for each layer.

    3. Explain three cost optimization strategies for cloud analytics. For each, describe the trade-off (e.g., cost savings vs. flexibility or risk).

    4. What is vendor lock-in in cloud analytics, and how can organizations mitigate it? Describe two specific strategies.

    True/False

    1. True or False: Amazon Athena requires you to provision and manage server clusters before running queries.

    2. True or False: A manufacturing company using Azure IoT + ML for predictive maintenance achieved a 40% reduction in unplanned downtime.

    3. True or False: Using open-source frameworks like Apache Beam helps reduce vendor lock-in by enabling portable data pipelines.

    Answer Key:

    1-b, 2-c, 3-c, 4. Redshift: provisioned clusters (or serverless option), columnar MPP warehouse. BigQuery: fully serverless, separates compute/storage, pay per query data scanned — most serverless. Synapse: hybrid — offers both dedicated SQL pools (provisioned) and serverless on-demand query. BigQuery is the most purely serverless. 5. Ingest: Kinesis/Firehose (streaming), Glue (batch ETL). Store: S3 data lake (raw/clean/curated zones). Process: Glue/Athena/EMR for transformation and analysis. Serve: QuickSight dashboards, Redshift for heavy queries, APIs for applications. 6. (1) Reserved instances: 30-72% savings but requires 1-3yr commitment — less flexibility. (2) Spot instances: 70-90% savings but can be terminated — only for fault-tolerant jobs. (3) Storage tiering: moves cold data to cheaper tiers (Glacier) — saves 68-95% but retrieval is slower. 7. Lock-in = dependence on proprietary services makes switching costly. Strategies: (1) Use open-source tools (Spark, Beam, Kubernetes) for portability across clouds. (2) Store data in vendor-neutral formats (Parquet, CSV) and avoid deeply proprietary features where possible. 8. False, 9. True, 10. True.

    This page titled 13.6.1: Review Questions was last modified on Thu, 17 Sep 2026 03:18:32 GMT and is shared under a CC BY 4.0 license and was authored, remixed, and/or curated by Felix Amoruwa.

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