AI Deployment Basics MCQ Questions with Answers (Latest 2026)
Practice AI Deployment Basics MCQ questions with detailed explanations and clear answer validation. These MCQs help you revise core concepts, compare close options, and improve accuracy for interviews, certification exams, and technical screening rounds. Use this updated 2026 set to strengthen fundamentals and confidence.
Answer: Running inference behind an API for clients.
Here, Running inference behind an API for clients. is the right choice. Connects models to apps. It aligns directly with what the question asks about which option best describes model serving. A quick elimination of partially true options helps confirm it.
Q2. What is the primary purpose of model serving?
Select an answer to check.
Answer: Running inference behind an API for clients.
In this case, Running inference behind an API for clients. is correct. Connects models to apps. It aligns directly with what the question asks about what is the primary purpose of model serving. A quick elimination of partially true options helps confirm it.
Q3. Which statement about model serving is most accurate?
Select an answer to check.
Answer: Running inference behind an API for clients.
The best option here is Running inference behind an API for clients.. Connects models to apps. It aligns directly with what the question asks about which statement about model serving is most accurate. A quick elimination of partially true options helps confirm it.
Q4. How is model serving best characterized?
Select an answer to check.
Answer: Running inference behind an API for clients.
For this question, Running inference behind an API for clients. is correct. Connects models to apps. It aligns directly with what the question asks about how is model serving best characterized. A quick elimination of partially true options helps confirm it.
Q5. Which option best describes a REST API for ML?
Select an answer to check.
Answer: HTTP endpoints exposing model predictions.
HTTP endpoints exposing model predictions. is the correct answer here. Common but not always optimal. It aligns directly with what the question asks about which option best describes a rest api for. A quick elimination of partially true options helps confirm it.
Q6. What is the primary purpose of a REST API for ML?
Select an answer to check.
Answer: HTTP endpoints exposing model predictions.
Here, HTTP endpoints exposing model predictions. is the right choice. Common but not always optimal. This matches the core idea being tested around what is the primary purpose of a rest. A quick elimination of partially true options helps confirm it.
Q7. Which statement about a REST API for ML is most accurate?
Select an answer to check.
Answer: HTTP endpoints exposing model predictions.
In this case, HTTP endpoints exposing model predictions. is correct. Common but not always optimal. This matches the core idea being tested around which statement about a rest api for ml. A quick elimination of partially true options helps confirm it.
Q8. How is a REST API for ML best characterized?
Select an answer to check.
Answer: HTTP endpoints exposing model predictions.
The best option here is HTTP endpoints exposing model predictions.. Common but not always optimal. This matches the core idea being tested around how is a rest api for ml best. A quick elimination of partially true options helps confirm it.
Q9. Which option best describes gRPC for ML?
Select an answer to check.
Answer: Binary RPC framework with schemas (good for low latency).
For this question, Binary RPC framework with schemas (good for low latency). is correct. Lower overhead and typed. This matches the core idea being tested around which option best describes grpc for ml. A quick elimination of partially true options helps confirm it.
Q10. What is the primary purpose of gRPC for ML?
Select an answer to check.
Answer: Binary RPC framework with schemas (good for low latency).
Binary RPC framework with schemas (good for low latency). is the correct answer here. Lower overhead and typed. This matches the core idea being tested around what is the primary purpose of grpc for. A quick elimination of partially true options helps confirm it.
Q11. Which statement about gRPC for ML is most accurate?
Select an answer to check.
Answer: Binary RPC framework with schemas (good for low latency).
Here, Binary RPC framework with schemas (good for low latency). is the right choice. Lower overhead and typed. That is exactly the concept behind which statement about grpc for ml is most in this context. A quick elimination of partially true options helps confirm it.
Q12. How is gRPC for ML best characterized?
Select an answer to check.
Answer: Binary RPC framework with schemas (good for low latency).
In this case, Binary RPC framework with schemas (good for low latency). is correct. Lower overhead and typed. That is exactly the concept behind how is grpc for ml best characterized in this context. A quick elimination of partially true options helps confirm it.
Q13. Which option best describes batch inference?
Select an answer to check.
Answer: Predicting on large groups of records offline.
The best option here is Predicting on large groups of records offline.. Cheap; high throughput. That is exactly the concept behind which option best describes batch inference in this context. A quick elimination of partially true options helps confirm it.
Q14. What is the primary purpose of batch inference?
Select an answer to check.
Answer: Predicting on large groups of records offline.
For this question, Predicting on large groups of records offline. is correct. Cheap; high throughput. That is exactly the concept behind what is the primary purpose of batch inference in this context. A quick elimination of partially true options helps confirm it.
Q15. Which statement about batch inference is most accurate?
Select an answer to check.
Answer: Predicting on large groups of records offline.
Predicting on large groups of records offline. is the correct answer here. Cheap; high throughput. That is exactly the concept behind which statement about batch inference is most accurate in this context. A quick elimination of partially true options helps confirm it.
Q16. How is batch inference best characterized?
Select an answer to check.
Answer: Predicting on large groups of records offline.
Here, Predicting on large groups of records offline. is the right choice. Cheap; high throughput. It fits the requirement in the prompt about how is batch inference best characterized. A quick elimination of partially true options helps confirm it.
Q17. Which option best describes online inference?
Select an answer to check.
Answer: Real-time per-request prediction.
In this case, Real-time per-request prediction. is correct. Lower throughput, lower latency. It fits the requirement in the prompt about which option best describes online inference. A quick elimination of partially true options helps confirm it.
Q18. What is the primary purpose of online inference?
Select an answer to check.
Answer: Real-time per-request prediction.
The best option here is Real-time per-request prediction.. Lower throughput, lower latency. It fits the requirement in the prompt about what is the primary purpose of online inference. A quick elimination of partially true options helps confirm it.
Q19. Which statement about online inference is most accurate?
Select an answer to check.
Answer: Real-time per-request prediction.
For this question, Real-time per-request prediction. is correct. Lower throughput, lower latency. It fits the requirement in the prompt about which statement about online inference is most accurate. A quick elimination of partially true options helps confirm it.
Q20. How is online inference best characterized?
Select an answer to check.
Answer: Real-time per-request prediction.
Real-time per-request prediction. is the correct answer here. Lower throughput, lower latency. It fits the requirement in the prompt about how is online inference best characterized. A quick elimination of partially true options helps confirm it.
Q21. Which option best describes autoscaling?
Select an answer to check.
Answer: Scaling replicas with load.
Here, Scaling replicas with load. is the right choice. Common with K8s/HPA. This is the most accurate statement for which option best describes autoscaling. A quick elimination of partially true options helps confirm it.
Q22. What is the primary purpose of autoscaling?
Select an answer to check.
Answer: Scaling replicas with load.
In this case, Scaling replicas with load. is correct. Common with K8s/HPA. This is the most accurate statement for what is the primary purpose of autoscaling. A quick elimination of partially true options helps confirm it.
Q23. Which statement about autoscaling is most accurate?
Select an answer to check.
Answer: Scaling replicas with load.
The best option here is Scaling replicas with load.. Common with K8s/HPA. This is the most accurate statement for which statement about autoscaling is most accurate. A quick elimination of partially true options helps confirm it.
Q24. How is autoscaling best characterized?
Select an answer to check.
Answer: Scaling replicas with load.
For this question, Scaling replicas with load. is correct. Common with K8s/HPA. This is the most accurate statement for how is autoscaling best characterized. A quick elimination of partially true options helps confirm it.
Q25. Which option best describes a GPU?
Select an answer to check.
Answer: Accelerator for parallel matrix workloads.
Accelerator for parallel matrix workloads. is the correct answer here. Common for DL inference and training. This is the most accurate statement for which option best describes a gpu. A quick elimination of partially true options helps confirm it.
Q26. What is the primary purpose of a GPU?
Select an answer to check.
Answer: Accelerator for parallel matrix workloads.
Here, Accelerator for parallel matrix workloads. is the right choice. Common for DL inference and training. It aligns directly with what the question asks about what is the primary purpose of a gpu. The other options are either incomplete or contextually incorrect.
Q27. Which statement about a GPU is most accurate?
Select an answer to check.
Answer: Accelerator for parallel matrix workloads.
In this case, Accelerator for parallel matrix workloads. is correct. Common for DL inference and training. It aligns directly with what the question asks about which statement about a gpu is most accurate. The other options are either incomplete or contextually incorrect.
Q28. How is a GPU best characterized?
Select an answer to check.
Answer: Accelerator for parallel matrix workloads.
The best option here is Accelerator for parallel matrix workloads.. Common for DL inference and training. It aligns directly with what the question asks about how is a gpu best characterized. The other options are either incomplete or contextually incorrect.
Q29. Which option best describes model quantization?
Select an answer to check.
Answer: Reduce precision (e.g., FP16, INT8) for speed/memory.
For this question, Reduce precision (e.g., FP16, INT8) for speed/memory. is correct. Trade-off accuracy vs speed. It aligns directly with what the question asks about which option best describes model quantization. The other options are either incomplete or contextually incorrect.
Q30. What is the primary purpose of model quantization?
Select an answer to check.
Answer: Reduce precision (e.g., FP16, INT8) for speed/memory.
Reduce precision (e.g., FP16, INT8) for speed/memory. is the correct answer here. Trade-off accuracy vs speed. It aligns directly with what the question asks about what is the primary purpose of model quantization. The other options are either incomplete or contextually incorrect.
Q31. Which statement about model quantization is most accurate?
Select an answer to check.
Answer: Reduce precision (e.g., FP16, INT8) for speed/memory.
Here, Reduce precision (e.g., FP16, INT8) for speed/memory. is the right choice. Trade-off accuracy vs speed. This matches the core idea being tested around which statement about model quantization is most accurate. The other options are either incomplete or contextually incorrect.
Q32. How is model quantization best characterized?
Select an answer to check.
Answer: Reduce precision (e.g., FP16, INT8) for speed/memory.
In this case, Reduce precision (e.g., FP16, INT8) for speed/memory. is correct. Trade-off accuracy vs speed. This matches the core idea being tested around how is model quantization best characterized. The other options are either incomplete or contextually incorrect.
Q33. Which option best describes model distillation?
Select an answer to check.
Answer: Train smaller model to mimic a larger one.
The best option here is Train smaller model to mimic a larger one.. Reduces inference cost. This matches the core idea being tested around which option best describes model distillation. The other options are either incomplete or contextually incorrect.
Q34. What is the primary purpose of model distillation?
Select an answer to check.
Answer: Train smaller model to mimic a larger one.
For this question, Train smaller model to mimic a larger one. is correct. Reduces inference cost. This matches the core idea being tested around what is the primary purpose of model distillation. The other options are either incomplete or contextually incorrect.
Q35. Which statement about model distillation is most accurate?
Select an answer to check.
Answer: Train smaller model to mimic a larger one.
Train smaller model to mimic a larger one. is the correct answer here. Reduces inference cost. This matches the core idea being tested around which statement about model distillation is most accurate. The other options are either incomplete or contextually incorrect.
Q36. How is model distillation best characterized?
Select an answer to check.
Answer: Train smaller model to mimic a larger one.
Here, Train smaller model to mimic a larger one. is the right choice. Reduces inference cost. That is exactly the concept behind how is model distillation best characterized in this context. The other options are either incomplete or contextually incorrect.
Q37. Which option best describes model pruning?
Select an answer to check.
Answer: Remove low-importance weights/heads.
In this case, Remove low-importance weights/heads. is correct. Sparsifies the model. That is exactly the concept behind which option best describes model pruning in this context. The other options are either incomplete or contextually incorrect.
Q38. What is the primary purpose of model pruning?
Select an answer to check.
Answer: Remove low-importance weights/heads.
The best option here is Remove low-importance weights/heads.. Sparsifies the model. That is exactly the concept behind what is the primary purpose of model pruning in this context. The other options are either incomplete or contextually incorrect.
Q39. Which statement about model pruning is most accurate?
Select an answer to check.
Answer: Remove low-importance weights/heads.
For this question, Remove low-importance weights/heads. is correct. Sparsifies the model. That is exactly the concept behind which statement about model pruning is most accurate in this context. The other options are either incomplete or contextually incorrect.
Q40. How is model pruning best characterized?
Select an answer to check.
Answer: Remove low-importance weights/heads.
Remove low-importance weights/heads. is the correct answer here. Sparsifies the model. That is exactly the concept behind how is model pruning best characterized in this context. The other options are either incomplete or contextually incorrect.
Q41. Which option best describes ONNX?
Select an answer to check.
Answer: Open exchange format for ML models.
Here, Open exchange format for ML models. is the right choice. Cross-framework portability. It fits the requirement in the prompt about which option best describes onnx. The other options are either incomplete or contextually incorrect.
Q42. What is the primary purpose of ONNX?
Select an answer to check.
Answer: Open exchange format for ML models.
In this case, Open exchange format for ML models. is correct. Cross-framework portability. It fits the requirement in the prompt about what is the primary purpose of onnx. The other options are either incomplete or contextually incorrect.
Q43. Which statement about ONNX is most accurate?
Select an answer to check.
Answer: Open exchange format for ML models.
The best option here is Open exchange format for ML models.. Cross-framework portability. It fits the requirement in the prompt about which statement about onnx is most accurate. The other options are either incomplete or contextually incorrect.
Q44. How is ONNX best characterized?
Select an answer to check.
Answer: Open exchange format for ML models.
For this question, Open exchange format for ML models. is correct. Cross-framework portability. It fits the requirement in the prompt about how is onnx best characterized. The other options are either incomplete or contextually incorrect.
Q45. Which option best describes TensorRT?
Select an answer to check.
Answer: NVIDIA inference optimizer/runtime.
NVIDIA inference optimizer/runtime. is the correct answer here. Speeds GPU inference. It fits the requirement in the prompt about which option best describes tensorrt. The other options are either incomplete or contextually incorrect.
Q46. What is the primary purpose of TensorRT?
Select an answer to check.
Answer: NVIDIA inference optimizer/runtime.
Here, NVIDIA inference optimizer/runtime. is the right choice. Speeds GPU inference. This is the most accurate statement for what is the primary purpose of tensorrt. The other options are either incomplete or contextually incorrect.
Q47. Which statement about TensorRT is most accurate?
Select an answer to check.
Answer: NVIDIA inference optimizer/runtime.
In this case, NVIDIA inference optimizer/runtime. is correct. Speeds GPU inference. This is the most accurate statement for which statement about tensorrt is most accurate. The other options are either incomplete or contextually incorrect.
Q48. How is TensorRT best characterized?
Select an answer to check.
Answer: NVIDIA inference optimizer/runtime.
The best option here is NVIDIA inference optimizer/runtime.. Speeds GPU inference. This is the most accurate statement for how is tensorrt best characterized. The other options are either incomplete or contextually incorrect.
Q49. Which option best describes Triton Inference Server?
Select an answer to check.
Answer: NVIDIA model server supporting many backends.
For this question, NVIDIA model server supporting many backends. is correct. Hosts diverse models behind one API. This is the most accurate statement for which option best describes triton inference server. The other options are either incomplete or contextually incorrect.
Q50. What is the primary purpose of Triton Inference Server?
Select an answer to check.
Answer: NVIDIA model server supporting many backends.
NVIDIA model server supporting many backends. is the correct answer here. Hosts diverse models behind one API. This is the most accurate statement for what is the primary purpose of triton inference. The other options are either incomplete or contextually incorrect.