{"id":45913908,"url":"https://github.com/francomano/aws-exams-notes","last_synced_at":"2026-02-28T07:02:41.436Z","repository":{"id":338823986,"uuid":"1146152375","full_name":"francomano/AWS-Exams-Notes","owner":"francomano","description":"Personal AWS exam notes (CERTIFIED) — ML Associate \u0026 SOA‑C03.","archived":false,"fork":false,"pushed_at":"2026-02-16T09:49:00.000Z","size":54,"stargazers_count":1,"open_issues_count":0,"forks_count":0,"subscribers_count":0,"default_branch":"main","last_synced_at":"2026-02-16T23:36:09.155Z","etag":null,"topics":["aws","certification","cloudops-engineer","exam","machine-learning","mla-c02","notes","soa-c03"],"latest_commit_sha":null,"homepage":"","language":null,"has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"cc0-1.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/francomano.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null,"notice":null,"maintainers":null,"copyright":null,"agents":null,"dco":null,"cla":null}},"created_at":"2026-01-30T17:34:19.000Z","updated_at":"2026-02-16T09:49:03.000Z","dependencies_parsed_at":null,"dependency_job_id":null,"html_url":"https://github.com/francomano/AWS-Exams-Notes","commit_stats":null,"previous_names":["francomano/aws-exams-notes"],"tags_count":null,"template":false,"template_full_name":null,"purl":"pkg:github/francomano/AWS-Exams-Notes","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/francomano%2FAWS-Exams-Notes","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/francomano%2FAWS-Exams-Notes/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/francomano%2FAWS-Exams-Notes/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/francomano%2FAWS-Exams-Notes/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/francomano","download_url":"https://codeload.github.com/francomano/AWS-Exams-Notes/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/francomano%2FAWS-Exams-Notes/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":29927188,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-02-27T19:37:42.220Z","status":"online","status_checked_at":"2026-02-28T02:00:07.010Z","response_time":90,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["aws","certification","cloudops-engineer","exam","machine-learning","mla-c02","notes","soa-c03"],"created_at":"2026-02-28T07:02:36.926Z","updated_at":"2026-02-28T07:02:41.431Z","avatar_url":"https://github.com/francomano.png","language":null,"readme":"# AWS Exams Notes (CERTIFIED) ✅\n\n**Personal study notes for AWS exams I've passed.** This README focuses only on the `.txt` files and a short summary of their contents.\n\n---\n\n## 📄 Notes (.txt files)\n\n- `mla-c01/appunti-ML-Associate.txt` — Machine Learning (Associate) notes:\n  - Data ingestion \u0026 storage: structured / unstructured / semi-structured data, data warehouses (schema-on-write) vs data lakes (schema-on-read), lakehouse concepts, and partitioning best practices (Parquet/ORC).\n  - Pipelines \u0026 streaming: ETL/ELT patterns, AWS Glue (crawlers/catalog), Step Functions, EventBridge, Kinesis vs Amazon MSK, JDBC/ODBC, Avro/Parquet formats.\n  - Transformation \u0026 feature engineering: EMR and Spark (EMR Serverless / EMR on EKS), Spark MLlib, PCA, TF-IDF, imputation strategies, handling imbalance (SMOTE), scaling/encoding, binning, and shuffling.\n  - SageMaker ecosystem \u0026 managed services: training jobs, endpoints/batch transform, Data Wrangler, Ground Truth, Model Monitor, Feature Store, Canvas, and no-code options for quick experiments.\n  - Analysis \u0026 serverless tools: Athena, QuickSight, Glue DataBrew, Glue Data Quality; practical tips (columnar formats, small number of large files, MSCK REPAIR TABLE) and exam-style reminders for cost/operational overhead.\n  - Short exam-style reminders and quick formulas\n\n- `soa-c03/appunti-cloudops.txt` — Cloud operations / Solutions-focused notes:\n  - Compute \u0026 EC2: instance types, placement groups, purchasing options (On‑Demand/Reserved/Savings Plans/Spot/Capacity Reservation), instance store vs EBS, AMIs, status checks, hibernation, and SSH/troubleshooting tips.\n  - Storage \u0026 S3: EBS / EFS / FSx differences, snapshots \u0026 lifecycle, S3 versioning \u0026 Object Lock (WORM), presigned URLs, transfer acceleration, and replication best practices.\n  - Networking \u0026 CDN: VPC fundamentals (subnets, IGW, NAT, NACL vs Security Groups), VPC endpoints, Direct Connect, Site‑to‑Site VPN, Route 53 (routing policies \u0026 health checks), CloudFront caching/headers/cookie forwarding and origin concepts.\n  - Load balancing \u0026 autoscaling: ALB/NLB/GWLB differences, sticky sessions, health checks, cross‑zone, connection draining, Auto Scaling groups and scaling policies (simple/step/target/predictive).\n  - Databases \u0026 caches: RDS multi‑AZ vs read replicas, Aurora concepts, RDS Proxy, ElastiCache (Redis/Memcached) and caching patterns.\n  - Management \u0026 automation: CloudFormation (ChangeSets, StackSets, CreationPolicy/cfn-signal), Systems Manager (Run Command, Session Manager, Parameter Store, Patch Manager), and Lambda limits.\n  - Observability \u0026 ops: CloudWatch metrics/logs/agent/Insights, CloudTrail, EventBridge, alarms/anomaly detection, and log exports/subscriptions.\n  - Security \u0026 compliance: WAF, Shield, GuardDuty, Inspector, Macie, Security Hub, KMS (CMK types \u0026 cross‑account), Secrets Manager rotation, and audit patterns.\n  - DR, backup \u0026 cost: AWS Backup, DataSync, Snow family, Cost Explorer, Budgets, and practical exam tips for high availability, DR and cost optimisation.\n\n**Badge:** [SOA-C03 Credential — View on Credly](https://www.credly.com/badges/d5d3f2ca-399b-43ec-bdf5-029a8ea2466d/public_url)  \n\n**Badge:** [MLA-C01 Credential — View on Credly](https://www.credly.com/badges/4f58abc5-8efa-440d-8a71-e631d5851c83/public_url)\n---\n\n## ⚙️ How to use\n\n- Open the `.txt` files for quick review; they are written as concise study prompts and checklists.\n- Use search (grep/CTRL+F) to find topics quickly while revising.\n\n---\n\n\u003e These are my personal notes and not official AWS documentation. Use them as a supplement to AWS whitepapers and official study guides.\n\n---\n\nIf you'd like, I can expand any file's summary with extracted key points from the text — tell me which one you'd like expanded. ✍️\n","funding_links":[],"categories":[],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ffrancomano%2Faws-exams-notes","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Ffrancomano%2Faws-exams-notes","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ffrancomano%2Faws-exams-notes/lists"}