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Bites 291
Differentiate informational, navigational, and transactional intent and classify keywords programmatically
Define three intents by user goal; regex-match modifiers like what/how vs buy/near; check SERP features.
Design a secure templating engine for user notifications
Tests balancing creator flexibility with defense-in-depth security and i18n. Strong answers cover context-aware auto-escaping, a restricted AST grammar, ICU MessageFormat for pluralization, and sandboxed execution.
Build a competitor sitemap scraper for H1 and word count
Tests architecture of a polite, resilient scraper with clear legal guardrails. Strong answers cover throttled async fetches, robots.txt checks, HTML parsing, and copyright review. Red flag: ignoring rate limits, retry logic, or legal risk entirely.
Explain the difference between IaaS, PaaS, and SaaS with examples
This tests your grasp of cloud abstraction layers and shared responsibility. A strong answer maps IaaS to raw infrastructure like EC2, PaaS to managed runtimes like Heroku, and SaaS to end-user apps like Gmail.
CloudEvents: The Event Data Standard
CloudEvents provides a common envelope for event data, enabling routing without custom parsers. Use it when events cross clouds, SaaS tools, or internal services. It standardizes wrappers, not payloads, so producers and consumers still need aligned schemas.
AWS SDK: Code That Operates Your Cloud
The AWS SDK turns AWS API calls into native code so your app can command S3 or DynamoDB directly. It handles auth, retries, and formatting automatically. The footgun: forgetting region or credentials causes silent failures that look like network errors.
Explain the concept of a sidecar container in Kubernetes
Tests Pod multi-container patterns. A strong answer defines sidecars as co-located helpers sharing network and storage, cites service mesh or log forwarding, and argues for reuse and separate lifecycles. Red flag: calling it another Pod or legacy workaround.
How would you block merges when PR coverage drops 2%?
Upload coverage to Codecov, set a project status threshold of 2, and require the check in branch protection.
Explain dependency management and diamond conflicts in automated builds
Tests transitive dependency resolution and conflict strategies in build pipelines. Strong answers mention nearest-wins eviction, strict versioning, shading, or classloader isolation. Red flag: manual jar swaps or pinning without understanding ABI breakage.
Platform Orchestrator: Infrastructure's Travel Agent
A platform orchestrator is infrastructure's travel agent: describe an app and it provisions compute, databases, and caches across clouds automatically. It shines once teams outgrow per-service Terraform. Adopt it too early and it becomes a bottleneck.
Container Image Security Scanning
A container image is a frozen filesystem; scanning unpacks layers to flag known CVEs before deployment. Run it in CI/CD after every build and continuously in production registries. Scanning only at build time misses new CVEs in running images.
How would you implement data lineage for microservices analytics?
Tests end-to-end provenance tracking, not just docs. Strong answers cover automated metadata capture at service boundaries, a central catalog such as DataHub or OpenLineage, and column-level tracing.
Propose a technical architecture for a centralized Metrics Layer.
This tests your grasp of data governance and semantic layers. A great answer outlines a system with a central definition store (e.g., YAML in Git), a query engine, and an API, ensuring all teams get consistent metric results.
Describe the role of dbt in a modern analytics stack
Tests your grasp of modern ELT patterns. A good answer explains how dbt uses SQL for in-warehouse transformations, enabling software engineering practices like version control and testing. A red flag is confusing dbt with a full ETL tool or an orchestrator.
How do you create a 'golden record' from fragmented data?
Tests your ability to design a data reconciliation system. A great answer outlines a process: profiling sources, defining survivorship rules, implementing a matching engine, and creating a stewardship process. A red flag is suggesting a simple one-off script.
How would you handle 10% null values in a key column?
This tests your understanding of data cleaning trade-offs. First, investigate the cause of nulls. Then, discuss simple imputation (mean/median) vs. discarding rows, weighing pros and cons. A red flag is jumping to a solution without asking about the data.
Data Warehouse vs. Data Lake vs. Lakehouse
Tests your grasp of modern data architectures. A great answer defines warehouses (structured, schema-on-write) and lakes (raw, schema-on-read), then explains how a lakehouse adds ACID transactions and governance on top of a lake.
Build a pipeline to load a daily CSV into a database
This tests your ability to connect basic cloud services (storage, compute, database) into a simple, event-driven data pipeline. A good answer mentions an event trigger (S3), a serverless function (Lambda), and a database (RDS), plus error handling.
Design a Centralized Metrics Layer
This tests your grasp of data governance and creating a single source of truth. A good answer defines a semantic layer between the data warehouse and BI tools, centralizing metric definitions in code.
Describe dbt's role in a modern analytics stack
Tests your grasp of the ELT paradigm and applying software engineering principles to data. A good answer defines dbt as the 'T' in ELT, contrasts its in-warehouse SQL approach with traditional ETL, and clarifies its relationship with orchestrators like…