Cloud

Google Vertex Interview Questions

4.9 (2095)11,295 learners20 lessons40m

Curriculum

Topic

01 - What is Vertex AI, and what problem does a unified ML platform solve compared to stitching together separate t1:59
02 - What are the core building blocks of Vertex AI (datasets, training, Model Registry, Endpoints, Pipelines, Feat1:53
03 - What is the difference between AutoML and custom training in Vertex AI, and when would you choose each1:56
04 - How does Vertex AI relate to BigQuery and BigQuery ML, and when would you use each for training1:57
05 - What types of dataproblems does Vertex AI AutoML support (tabular, image, text, video)2:01
06 - As of 2026, Vertex AI has been rebranded under Gemini Enterprise Agent Platform. What changed, and what stayed2:07
07 - What is a Vertex AI managed dataset, and what are the benefits of using one2:06
08 - Walk through the steps to train a model with AutoML on a tabular dataset1:52
09 - What is custom training, and what are training containers (prebuilt vs custom) in Vertex AI2:06
10 - What is hyperparameter tuning in Vertex AI, and how does the service help you do it2:05
11 - How would you use a GPU or TPU for a custom training job, and when is each appropriate1:54
12 - What is distributed training, and when would you need it on Vertex AI2:21
13 - What is a Vertex AI Endpoint, and what's the difference between online (real-time) and batch prediction1:58
14 - How do you deploy a trained model to an endpoint, and what is traffic splitting used for1:57
15 - What is the Model Registry, and why is model versioning important1:50
16 - How would you do a safe rollout of a new model version (e.g., canary traffic splitting A-B)2:02
17 - What factors affect prediction latency and cost on an endpoint, and how would you optimize them1:55
18 - What are Vertex AI Pipelines, and what framework are they based on (Kubeflow TFX)2:09
19 - Why use pipelines instead of running training scripts manually — what do you gain2:02
20 - What is the Vertex AI Feature Store, and what problem does it solve (e.g., training-serving skew)1:59