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Fully managed batch computing at any scale — without managing servers or schedulers
AWS Batch is a fully managed service that dynamically provisions the optimal quantity and type of compute resources (EC2, Spot, or Fargate) based on the volume and requirements of submitted batch jobs. It eliminates the need to install and manage batch computing software or server clusters, handling job queuing, scheduling, dependency management, and compute provisioning automatically. AWS Batch is purpose-built for workloads that run to completion — such as scientific simulations, ML training preprocessing, ETL pipelines, rendering, and financial risk modeling.
Run large-scale, compute-intensive batch workloads without managing infrastructure, schedulers, or job queues — paying only for the underlying EC2 or Fargate resources consumed.
Use When
Avoid When
Managed Compute Environments (EC2)
AWS Batch provisions, scales, and terminates EC2 instances automatically based on job demand
Fargate Compute Environments
Serverless containers — no EC2 instance management; ideal for jobs not requiring GPU or custom AMI
Spot Instance Integration
Supports EC2 Spot and Fargate Spot for up to 90% cost reduction; automatic retry on interruption
Array Jobs
Run thousands of parallel child jobs from a single job submission; each child gets a unique index
Job Dependencies
Define sequential or N-to-1 job dependency chains within a queue
Multi-Node Parallel (MNP) Jobs
Distributed computing across multiple EC2 instances for tightly coupled HPC workloads (e.g., MPI)
GPU Support
Supports GPU instance types (p3, p4, g4dn) for ML training and rendering — requires EC2 managed environment, NOT Fargate
Custom AMI Support
Bring your own AMI for EC2 compute environments; not available with Fargate
Job Queues with Priority
Multiple queues with numeric priority; higher priority queues are scheduled first
EventBridge Integration
Trigger Batch jobs from EventBridge rules (scheduled or event-driven)
Step Functions Integration
Orchestrate Batch jobs as steps in complex workflows
CloudWatch Logs Integration
Job stdout/stderr streamed to CloudWatch Logs automatically
IAM Roles for Jobs
Each job can assume a specific IAM role (jobRoleArn) for fine-grained permissions
EFS and S3 Integration
Mount EFS volumes or access S3 for shared data between jobs
Secrets Manager / SSM Parameter Store
Inject secrets as environment variables into job containers
Fair Share Scheduling
Distribute compute fairly across users/teams sharing a compute environment
Scheduling Policies
Control job prioritization with fair-share scheduling policies
Event-Driven Batch Trigger
high freqS3 object upload triggers a Lambda function (or EventBridge rule) that submits an AWS Batch job to process the uploaded file. Lambda handles the trigger/orchestration; Batch handles the long-running compute. This solves the Lambda 15-minute timeout limitation for heavy processing.
Orchestrated Multi-Stage Batch Pipeline
high freqStep Functions orchestrates complex workflows where Batch jobs are individual steps with dependencies, branching, error handling, and retries. Step Functions waits for Batch job completion using the .sync integration pattern. Ideal for ETL pipelines, ML preprocessing → training → evaluation chains.
Preprocessing → Training Handoff
high freqAWS Batch handles large-scale data preprocessing and feature engineering (writing outputs to S3), then SageMaker Training Jobs consume the preprocessed data. Batch excels at custom preprocessing logic; SageMaker excels at managed ML training with built-in algorithms and experiment tracking.
Batch vs. ECS Decision Point
high freqBoth use Docker containers, but ECS is for long-running services and real-time tasks while Batch is for queued, finite-duration jobs with automatic scaling to zero. ECS requires you to manage task scheduling; Batch manages the scheduler. On exams, 'batch jobs that run to completion' = AWS Batch.
Serverless Batch Compute
high freqFargate compute environments in AWS Batch provide fully serverless execution — no EC2 instance management, AMI patching, or cluster sizing. Ideal for variable, unpredictable batch workloads without GPU requirements. Fargate Spot adds cost optimization for fault-tolerant jobs.
Batch vs. EMR for Big Data
medium freqEMR is optimized for distributed big data frameworks (Spark, Hive, Hadoop) with managed cluster lifecycle. AWS Batch is better for containerized jobs that don't require Spark/Hadoop ecosystems. For ML on Spark at scale, EMR wins; for custom containerized batch processing, AWS Batch wins.
Scheduled Batch Jobs
medium freqEventBridge Scheduler (cron/rate expressions) triggers AWS Batch job submissions on a schedule — replacing traditional cron servers. This is the modern, serverless replacement for cron-based batch scheduling without managing EC2 instances running cron daemons.
Managed EC2 Scaling
medium freqAWS Batch managed compute environments handle EC2 Auto Scaling automatically based on job queue depth — you do NOT need to configure Auto Scaling groups manually. AWS Batch acts as its own scheduler and scaler. This is a key differentiator from unmanaged compute environments.
AWS Batch has NO job duration limit — jobs can run for hours or days. Lambda has a 15-minute hard limit. Any scenario describing long-running compute (>15 min) that needs to 'run to completion' should immediately point you to AWS Batch, not Lambda.
AWS Batch is FREE — you pay only for the underlying EC2/Fargate resources. When a question asks about cost optimization for batch workloads, the answer often involves Spot instances within AWS Batch (up to 90% savings) + setting minvCpus=0 to scale to zero when idle.
GPU workloads (ML training, rendering) require EC2 managed compute environments in AWS Batch — Fargate does NOT support GPU instances. If a scenario mentions GPU-based batch processing, the answer is Batch with EC2 (not Fargate).
For ML migration scenarios, prefer SageMaker over raw AWS Batch for model training and hyperparameter tuning. AWS Batch is appropriate for custom preprocessing pipelines, but SageMaker reduces operational overhead with managed training, experiment tracking, and built-in algorithms. The exam tests this 'right tool for ML' judgment.
Any scenario with jobs running longer than 15 minutes = AWS Batch, NOT Lambda. Lambda's hard 15-minute limit is the #1 disqualifier for long-running compute workloads.
For ML workloads, SageMaker beats AWS Batch when 'reducing operational overhead' is the goal. Batch gives flexibility; SageMaker gives managed training, HPO, and experiment tracking with less ops burden.
GPU batch jobs require EC2 managed compute environments — Fargate cannot run GPU workloads. Always check for GPU requirements before recommending Fargate for Batch.
Array Jobs (2–10,000 child jobs) are the AWS Batch answer for 'embarrassingly parallel' workloads — simulations, parameter sweeps, image processing at scale. Each child receives AWS_BATCH_JOB_ARRAY_INDEX to know which subset of data to process.
AWS Batch uses Docker containers for ALL jobs — jobs are defined as container images in a job definition. This means your code must be containerized. If a question says 'containerized batch workloads,' AWS Batch is the primary candidate.
Multi-Node Parallel (MNP) jobs in AWS Batch support tightly coupled HPC workloads using MPI across multiple EC2 instances. This is the AWS Batch answer for HPC/distributed computing scenarios — not to be confused with Array Jobs (which are loosely coupled/independent).
Job Queues have numeric priority — when multiple queues share a compute environment, higher priority queues get resources first. This is how you implement SLA tiers (e.g., urgent jobs vs. background jobs) in AWS Batch without separate compute environments.
Step Functions + AWS Batch (.sync integration) is the canonical answer for orchestrating multi-step batch pipelines with error handling, retries, and conditional branching. EventBridge is for triggering Batch jobs on a schedule or event; Step Functions is for complex workflow orchestration.
AWS Batch vs. ECS: Both use containers, but ECS is for long-running services; Batch is for finite jobs that run to completion with automatic queue management. If the scenario says 'process jobs from a queue' or 'run until complete,' choose Batch. If it says 'always-on service,' choose ECS.
Common Mistake
Lambda can replace AWS Batch for any compute workload if you just chain multiple Lambda functions together
Correct
Lambda has a hard 15-minute execution limit per invocation and is not designed for sustained, compute-intensive processing. Chaining Lambdas adds complexity, state management overhead, and still hits per-invocation limits. AWS Batch is purpose-built for long-running jobs that exceed Lambda's timeout — use Batch for jobs requiring more than 15 minutes of continuous compute.
This is the #1 Lambda vs. Batch trap on SAA-C03. Questions will describe a workload that 'takes 2-3 hours to process' and offer Lambda as a distractor. The 15-minute Lambda limit is the immediate disqualifier.
Common Mistake
For ML model training migrations, AWS Batch is the best service because it's flexible and supports any container
Correct
While AWS Batch CAN run ML training containers, Amazon SageMaker is the purpose-built service for ML training with significantly lower operational overhead. SageMaker provides managed training infrastructure, built-in algorithms, automatic model tuning (hyperparameter optimization), experiment tracking, and model registry. The exam specifically tests whether you recognize that 'reducing operational overhead for ML workloads' points to SageMaker, not raw Batch.
SAP-C02 and DEA-C01 frequently present ML migration scenarios where candidates must choose between 'flexible but operationally heavy' (Batch) and 'managed but ML-specific' (SageMaker). The question will emphasize 'minimize operational overhead' as the deciding factor.
Common Mistake
AWS Batch and Amazon EMR are interchangeable for big data processing workloads
Correct
EMR is optimized for distributed big data frameworks (Apache Spark, Hive, HBase, Hadoop) with managed cluster lifecycle, native Spark optimizations, and EMRFS for S3 access. AWS Batch is for containerized jobs that don't require the Spark/Hadoop ecosystem. For 'process 10TB of data with Spark transformations,' use EMR. For 'run 500 parallel containerized processing jobs,' use AWS Batch.
Exam questions will describe a big data workload and offer both EMR and Batch as options. The key discriminator is whether the workload uses Spark/Hadoop (EMR) or is a custom containerized job (Batch).
Common Mistake
AWS Batch manages its own compute automatically, so you never need to think about EC2 instance types or sizing
Correct
While AWS Batch does manage provisioning and scaling, you MUST configure compute environments with appropriate instance types, maxvCpus, and Spot vs. On-Demand strategy. Misconfiguring maxvCpus causes job queuing; choosing wrong instance families causes GPU jobs to fail (Fargate doesn't support GPU). The managed aspect means Batch handles the Auto Scaling mechanics, not the architecture decisions.
Candidates assume 'fully managed' means zero configuration. On the exam, architecture questions require you to know WHEN to use EC2 vs. Fargate environments and how maxvCpus affects cost and performance.
Common Mistake
AWS Batch costs money as a service on top of the compute costs
Correct
AWS Batch has NO additional service charge. You pay only for the underlying EC2 instances, Fargate vCPU/memory-seconds, EBS volumes, and data transfer. This makes Batch extremely cost-effective compared to self-managed schedulers (which require EC2 instances running 24/7 for the scheduler itself).
Cost optimization questions may ask to compare self-managed batch schedulers (EC2 running Slurm/PBS) vs. AWS Batch. The correct answer highlights that Batch eliminates scheduler infrastructure costs AND enables Spot instance usage for job compute.
Common Mistake
Fargate compute environments in AWS Batch support all the same features as EC2 compute environments
Correct
Fargate compute environments do NOT support GPU instances, custom AMIs, or multi-node parallel (MNP) jobs. Fargate is ideal for CPU-based, containerized batch jobs where you want zero EC2 management. EC2 managed environments are required for GPU workloads, custom OS configurations, or tightly coupled HPC jobs using MPI.
GPU-based ML or rendering scenarios are a common exam trap — candidates choose Fargate for 'serverless simplicity' but Fargate cannot run GPU workloads. Always check for GPU requirements before selecting Fargate.
BATCH = 'Beyond A Timeout, Containers Handle everything' — AWS Batch is for jobs that go Beyond Lambda's timeout, using Containers, with AWS Handling the infrastructure
FREE + SPOT = Batch Cost Formula: Batch itself is FREE, add SPOT instances for maximum savings (up to 90%)
EC2 for GPU, Fargate for simplicity — if the job needs a Graphics card, it needs EC2; if it needs simplicity, use Fargate
Array = Parallel, MNP = Distributed: Array Jobs run INDEPENDENT parallel tasks; Multi-Node Parallel runs CONNECTED distributed tasks (like MPI)
CertAI Tutor · SAA-C03, SAP-C02, DEA-C01, CLF-C02 · 2026-02-21
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