AWS Certified Generative AI Practice Tests: Build Exam-Day Signal
A practical guide to AWS Certified AI Practitioner (AIF-C01) and Machine Learning Engineer – Associate (MLA-C01) practice tests—timing, domain weighting, review loops, and the question patterns that actually show up.
Start with the right exam
“AWS certified generative AI” usually maps to one of two official AWS exams:
**AWS Certified AI Practitioner (AIF-C01)** — broad, product-level generative AI knowledge on AWS.
**AWS Certified Machine Learning Engineer – Associate (MLA-C01)** — hands-on machine learning engineering with heavy generative AI coverage.
Read the official exam guide before buying a practice test. The AIF-C01 guide splits the exam into five domains: AI/ML fundamentals, generative AI fundamentals, foundation model applications, responsible AI, and security/compliance/governance. MLA-C01 focuses on data preparation, feature engineering, model training, evaluation, deployment, and monitoring.
Make practice tests a review loop
A practice test is only useful if it changes what you do next. At Sapior, we recommend this loop:
1. **Diagnostic first.** Take one untimed set to identify weak domains.
2. **Timed domain drills.** Spend the most time on Amazon Bedrock, prompt engineering, retrieval-augmented generation, model evaluation, and security controls.
3. **Review twice as long as you answer.** For each miss, classify it as a service choice, prompt/configuration error, evaluation metric confusion, or security gap.
4. **Re-test.** Take another full timed exam after targeted review.
High-signal patterns
Most AWS generative AI practice tests repeat a small set of patterns.
**Service selection:** RAG chatbot questions usually point to Amazon Bedrock Knowledge Bases; enterprise search questions often point to Amazon Kendra.
**Evaluation:** Classification metrics are precision, recall, F1, and AUC. Generation quality is measured with faithfulness, relevance, toxicity, and sometimes ROUGE/BERTScore.
**Security:** Guardrails, IAM, KMS, CloudWatch logging, and VPC endpoints show up in nearly every set.
**Cost/latency:** Prompt caching, provisioned throughput, batch inference, and endpoint autoscaling separate memorizers from engineers.
Try these sample questions
1. A company wants to build a RAG chatbot over internal PDFs in Amazon S3 with minimal operational overhead. Which AWS service should they use?
A. SageMaker JumpStart
B. Amazon Bedrock Knowledge Bases
C. Amazon Kendra
D. Amazon Comprehend
**Answer:** B. Bedrock Knowledge Bases natively connects S3 data sources to a RAG workflow.
2. Which evaluation metric is most appropriate for measuring whether a generated answer is grounded in the retrieved source text?
A. ROUGE
B. BLEU
C. Faithfulness/groundedness
D. Raw accuracy
**Answer:** C. Faithfulness/groundedness specifically measures source alignment.
3. A company needs to block harmful content and apply safety filters to a production Bedrock chatbot. Which control should be used?
A. IAM policy
B. Bedrock Guardrails
C. KMS key
D. VPC endpoint
**Answer:** B. Bedrock Guardrails apply content policies and blocked topics to foundation model inputs/outputs.
Official sources to calibrate
Use AWS Skill Builder official practice questions for AIF-C01 and MLA-C01 to calibrate your terminology. Then add a harder layer that forces explanation-based review. The official AWS exam guides are the only reliable map to the current domain weights.
Final prep checklist
[ ] Read the AIF-C01 or MLA-C01 exam guide.
[ ] Take a diagnostic under timed conditions.
[ ] Run at least three domain-focused drills.
[ ] Review every missed Bedrock, SageMaker, and security question in AWS documentation.
[ ] Re-take a full practice exam 72 hours before the real test.