AWS AI Practitioner — Day 8: Batch vs Real-time Patterns
Learn: when to choose batch (bulk, cost-sensitive) vs real-time (low-latency).
Hands-on: run a Batch Transform job on a small dataset and compare runtime and cost to real-time endpoint.
Practice question:
Q1: Which scenario is best for Batch Transform?
A) Serving interactive UI predictions with <200ms latency B) Periodic scoring of a large historical dataset C) High-frequency trading requiring microsecond latency D) Live chat response generation where latency matters
Answer: B — batch transform fits periodic scoring of large datasets.
Daily Practice Questions (new)
Q1: Which metrics are key when comparing batch vs real-time approaches?
A) Cost per record and latency B) DNS queries and SSH sessions C) IAM password age D) S3 bucket name
Answer: A — cost-per-record and latency drive the decision.
Q2: How do you run a Batch Transform in SageMaker?
A) Create a TransformJob with S3 input/output B) Use Lambda only C) Use Route 53 D) Update CloudWatch logs
Answer: A — Batch Transform jobs ingest S3 input and write S3 output.
Q3: When should you choose real-time inference?
A) When latency requirements are strict B) When cost per record must be minimal C) For archival storage D) For long-term backups
Answer: A — real-time is for low-latency needs.
Q4: When is batch inference preferable?
A) When throughput and cost-per-record matter B) When immediate response is required C) For interactive UIs D) For DNS routing
Answer: A — batch fits high-throughput cost-optimized scenarios.
Q5: How to estimate the cost of a batch job?
A) Multiply instance-hour cost by job duration B) Count number of files only C) Use IAM policies D) Check Route 53 pricing
Answer: A — cost ≈ instance cost × runtime, plus storage/transfer.
Q6: What is data serialization overhead?
A) Time and cost for format conversion and I/O B) A security control C) A logging level D) An S3 lifecycle policy
Answer: A — serialization adds latency and processing costs.
Q7: How to optimize batch jobs?
A) Use appropriate instance sizes and parallelism B) Disable monitoring C) Store logs forever D) Use only small instances always
Answer: A — tune instance types and parallelism for efficiency.
Q8: What affects real-time latency?
A) Model size and container cold starts B) IAM policy names C) Route 53 TTL D) CloudWatch retention
Answer: A — large models and cold starts increase latency.
Q9: How to measure endpoint latency?
A) Histograms of response times in CloudWatch B) S3 bucket listings C) IAM policy simulator D) Route 53 logs
Answer: A — use metrics/histograms to analyze latency distribution.
Q10: What is a hybrid approach to inference?
A) Use both batch and real-time depending on use case B) Use only batch always C) Use only real-time always D) Avoid monitoring
Answer: A — hybrids combine both to match differing needs.
Review Questions (previous lessons)
Q1: What is Batch Transform best suited for?
A) Periodic bulk scoring of datasets B) Real-time chat responses C) DNS resolution D) Cost reporting
Answer: A — Batch Transform handles bulk offline inference.
Q2: When do you use a real-time endpoint?
A) For immediate low-latency predictions B) For batch jobs only C) For backups D) For IAM role creation
Answer: A — real-time endpoints serve instantaneous requests.
Q3: Which S3 storage class reduces cost for infrequent reads?
A) Standard-IA B) S3 Standard C) EC2 Instance Store D) Lambda
Answer: A — Standard-IA is optimized for infrequent access.
Q4: Why record run metadata for experiments?
A) For reproducibility and traceability B) To increase costs C) To slow down training D) To hide details
Answer: A — metadata supports reproducing and analyzing runs.
Q5: How can you reduce endpoint cold starts?
A) Use provisioned concurrency or keep containers warm B) Disable monitoring C) Use only tiny models D) Delete logs
Answer: A — provisioning or warming reduces initial latency.
Q6: What is a canary deployment?
A) A limited rollout used to validate changes before full release B) Immediate global rollout C) Deleting previous versions D) Static website hosting
Answer: A — canaries let you validate changes safely.
Q7: Why monitor costs during experiments?
A) To prevent surprises and optimize resource usage B) To avoid testing C) To delete artifacts automatically D) To disable alerts
Answer: A — monitoring costs prevents unexpected bills.
Q8: How to validate outputs from a batch job?
A) Use sample checks and unit/integration tests B) Ignore results C) Only rely on logs D) Delete outputs
Answer: A — verify outputs with sampling and tests.
Q9: What is model drift?
A) Decline in model performance over time due to data changes B) A deployment method C) A storage class D) An IAM policy
Answer: A — drift happens when data distribution changes.
Q10: How should you choose an instance size for serving?
A) Based on model resource needs and budget testing B) Always choose the largest available C) Ignore resource metrics D) Use only spot instances
Answer: A — pick instances by resource needs and cost tradeoffs.