ELK Test

Evaluates understanding of Elasticsearch’s distributed search and analytics engine. Covers core data structures — indices, shards, documents, mappings, analyzers, and tokenizers — and their impact on query performance. Tests practical proficiency with Query DSL, filters, aggregations, full-text vs. keyword fields, and scoring relevance. Medium and hard questions assess real-world use: optimizing search speed, designing efficient mappings, working with nested and geo fields, and tuning queries for scale.

Focuses on Logstash as a data processing and transformation layer within ELK. Assesses pipeline design (input-filter-output), plugin usage (Beats, Kafka, JDBC, Elasticsearch), and advanced filter logic using Grok, Dissect, Mutate, JSON, and GeoIP. Explores performance-oriented ingestion design—multi-pipeline setups, persistent queues, back-pressure management, and fault tolerance. Hard questions test architect-level understanding of pipeline scaling, data enrichment, and error handling under large-scale data volumes.

Tests ability to transform data into actionable insights through Kibana dashboards and visualizations. Covers Discover, Lens, Canvas, and TSVB (Time Series Visual Builder). Evaluates management of saved searches, space-based access, drill-down dashboards, and role-specific visualization design. Explores the creation of alerts and anomaly detection workflows using Watcher, Elastic Alerting Framework, and integration with email/webhook/SIEM systems. Hard questions emphasize visualization optimization and scaling for enterprise observability.

Evaluates proficiency in managing Elasticsearch clusters — from node configuration and role assignment to shard replication, recovery, and fault detection. Tests familiarity with cluster APIs (_cat, _cluster/health, _nodes/stats), monitoring tools, and scaling strategies. Medium and hard questions address real-world operations like preventing split-brain, shard balancing, reindexing strategies, and cluster state recovery. Advanced coverage includes diagnosing bottlenecks, optimizing heap, and managing rolling upgrades and hot-warm architectures.

Focuses on lifecycle and retention management of indices to ensure performance, scalability, and cost efficiency. Covers index templates, rollover aliases, snapshot/restore operations, and automated ILM phases (hot, warm, cold, delete). Evaluates ability to build time-series management solutions for observability data, implement custom ILM policies, and automate data archival using repositories (S3, Azure Blob, GCS). Hard questions include ILM debugging, optimizing shard sizing, and designing retention strategies for high-ingest workloads.

Tests ability to secure the ELK stack at all layers. Covers TLS/SSL configuration, role-based access control (RBAC), API key usage, and native realm authentication. Medium-level questions include integration with enterprise identity providers (LDAP, SAML, OpenID Connect), audit logging, and encrypted communication channels. Hard questions test designing multi-tenant security models, enforcing compliance (GDPR, HIPAA, SOC2), field/document-level security, and end-to-end encryption for regulated environments.

Evaluates ability to automate ELK deployments and management through infrastructure-as-code (IaC) tools. Covers provisioning clusters using Ansible, Terraform, Docker, Helm, and Elastic Cloud APIs. Medium and hard questions include CI/CD integration with Jenkins/GitHub Actions, zero-downtime upgrades, configuration versioning, and drift management. Tests knowledge of REST API automation for template creation, user provisioning, and ILM policy deployment at scale. Hard-level items address multi-environment orchestration and ELK deployment in Kubernetes.

Focuses on integrating ELK into enterprise observability ecosystems. Covers ingestion via Beats and APM agents, collecting metrics, traces, and logs, and correlating telemetry data across systems. Medium-level questions examine integrations with Prometheus, Grafana, CloudWatch, and OpenTelemetry. Hard questions evaluate designing federated observability architectures, centralizing telemetry for microservices, implementing anomaly detection, and balancing ingestion across hybrid or multi-cloud environments for operational intelligence.

Tests deep diagnostic and optimization expertise for production-grade ELK environments. Covers JVM tuning, heap memory management, query profiling, caching mechanisms, and threadpool optimization. Medium and hard questions assess advanced topics like shard sizing, reindexing strategy, query performance bottlenecks, and cluster hot-spot management. Expert-level items test multi-cluster federation, cross-cluster search and replication, geo-distributed designs, high-availability recovery strategies, and disaster recovery optimization.