Google Professional Machine Learning Engineer
QUESTIONS AND ANSWERS | VERIFIED AND WELL
DETAILED ANSWERS | PLUS RATIONALES |
GUARANTEED PASS | LATEST EXAM UPDATE | EXAM
PREP | STUDY GUIDE | PRACTICE TEST
,Domain 1: Architecting ML Solutions
Q1. A company wants to build an ML system that must meet a strict 99.99% availability SLA. They need low
operational overhead. Which architecture should they choose?
A) Self-managed ML on GCE with custom Kubernetes
B) Vertex AI endpoints with autoscaling and multi-zone deployment
C) Single TPU VM with a Flask server
D) Cloud Functions calling a local model file
Answer: B
Rationale: Vertex AI endpoints support autoscaling, regional/multi-zone deployment, and Google-managed
infrastructure, meeting high availability with low ops overhead. Self-managed options increase operational
burden and risk.
,Q2. A retail company needs real-time fraud detection with sub-100ms latency at high throughput. Which serving
option is MOST appropriate?
A) Vertex AI batch prediction
B) Vertex AI online prediction with a deployed endpoint
C) BigQuery ML
D) Dataflow streaming with no endpoint
Answer: B
Rationale: Online prediction endpoints are designed for low-latency, real-time inference. Batch prediction is for
offline; BigQuery ML is not ideal for sub-100ms per-request serving.
Q3. You must choose between training on TPUs vs GPUs for a large transformer model. Which factor MOST
strongly favors TPUs?
A) Small tabular datasets
, B) Very large matrix multiplications with high throughput needs
C) Need for CUDA-specific libraries
D) Single-node CPU-bound preprocessing
Answer: B
Rationale: TPUs excel at large-scale matrix operations common in transformers. CUDA dependency favors
GPUs; small tabular data doesn't need TPUs.
Q4. A team needs to share datasets and models across multiple projects with fine-grained IAM. What should
they use?
A) Local disks on a VM
B) Vertex AI Model Registry and Datasets with IAM
C) Cloud Storage buckets with public access
D) A shared spreadsheet
QUESTIONS AND ANSWERS | VERIFIED AND WELL
DETAILED ANSWERS | PLUS RATIONALES |
GUARANTEED PASS | LATEST EXAM UPDATE | EXAM
PREP | STUDY GUIDE | PRACTICE TEST
,Domain 1: Architecting ML Solutions
Q1. A company wants to build an ML system that must meet a strict 99.99% availability SLA. They need low
operational overhead. Which architecture should they choose?
A) Self-managed ML on GCE with custom Kubernetes
B) Vertex AI endpoints with autoscaling and multi-zone deployment
C) Single TPU VM with a Flask server
D) Cloud Functions calling a local model file
Answer: B
Rationale: Vertex AI endpoints support autoscaling, regional/multi-zone deployment, and Google-managed
infrastructure, meeting high availability with low ops overhead. Self-managed options increase operational
burden and risk.
,Q2. A retail company needs real-time fraud detection with sub-100ms latency at high throughput. Which serving
option is MOST appropriate?
A) Vertex AI batch prediction
B) Vertex AI online prediction with a deployed endpoint
C) BigQuery ML
D) Dataflow streaming with no endpoint
Answer: B
Rationale: Online prediction endpoints are designed for low-latency, real-time inference. Batch prediction is for
offline; BigQuery ML is not ideal for sub-100ms per-request serving.
Q3. You must choose between training on TPUs vs GPUs for a large transformer model. Which factor MOST
strongly favors TPUs?
A) Small tabular datasets
, B) Very large matrix multiplications with high throughput needs
C) Need for CUDA-specific libraries
D) Single-node CPU-bound preprocessing
Answer: B
Rationale: TPUs excel at large-scale matrix operations common in transformers. CUDA dependency favors
GPUs; small tabular data doesn't need TPUs.
Q4. A team needs to share datasets and models across multiple projects with fine-grained IAM. What should
they use?
A) Local disks on a VM
B) Vertex AI Model Registry and Datasets with IAM
C) Cloud Storage buckets with public access
D) A shared spreadsheet
