Applied AI cert-prep add-on

Google Cloud Professional ML Engineer Exam Prep Add-On

Convert AI Engineering Mastery into a Google Cloud Professional ML Engineer ramp aligned to Vertex AI and the GCP ML stack.

$147 add-on60h additional studyTarget exam: Google Cloud Professional Machine Learning EngineerExam fee: $200

What the add-on ships

++Six domain reviews mapped to the official Google Cloud Professional ML Engineer exam guide
++Three full-length 50 to 60 question mock exams in Google's scenario format
++Vertex AI scenarios: pipelines, model registry, endpoints, feature store, model monitoring
++Vertex AI Search and Conversation, Gemini API, and AI Platform integration patterns
++Direct link to the official Google Cloud exam page as the canonical reference

Built on the parent course

++AI Engineering Mastery 18-module curriculum covering RAG, agents, evals, observability, MLOps
++Production AI engineering patterns from the parent course that map directly to Vertex AI scenarios

Parent course: ai engineering mastery

Best for

++ML Engineers consolidating Google Cloud ML experience into a senior credential
++Data scientists moving into engineering roles on GCP infrastructure
++Solutions architects with a Google-Cloud-heavy customer portfolio

Practice surface

++Three Google-format mock exams in the scenario style the exam ships
++Vertex AI worked scenarios (pipelines, endpoints, model registry, monitoring)
++Targeted weak-area question drilling per Google ML Engineer exam objective

Buy the add-on

$147 on top of the ai engineering mastery parent course. Lifetime access to the practice materials, mock exams, and exam-day worksheets.

See the parent course

Exam summary

Google Cloud Professional Machine Learning Engineer covers building ML solutions on Google Cloud. Six domains: architecting low-code ML solutions, collaborating with stakeholders, scaling prototypes, serving and scaling models, automating and orchestrating ML pipelines, monitoring AI solutions. 50 to 60 questions in 2 hours.

Domain reviews

Architecting Low-Code ML Solutions

BigQuery ML, AutoML, Vertex AI Workbench.

++BigQuery ML: in-warehouse model training and inference
++Vertex AI AutoML for tabular, image, text, video
++When to use low-code vs custom training

Primary sources:

Google Cloud Professional ML Engineer exam guide

Scaling Prototypes into ML Models

Vertex AI Training, custom containers, hyperparameter tuning.

++Vertex AI custom training with Python packages or custom containers
++Vertex AI Vizier for hyperparameter tuning
++Distributed training: data parallel, model parallel

Primary sources:

Vertex AI documentation

Serving and Scaling Models

Vertex AI Prediction endpoints, online and batch.

++Online prediction endpoints with autoscaling
++Batch prediction jobs
++Private endpoints, traffic splitting for A/B testing
++GPU and TPU acceleration

Primary sources:

Vertex AI Prediction docs

Automating and Orchestrating ML Pipelines

Vertex AI Pipelines on Kubeflow, CI/CD for ML.

++Vertex AI Pipelines with KFP SDK
++Pipeline components, parameters, artifacts
++Vertex AI Model Registry and aliasing
++Cloud Build for CI/CD

Primary sources:

Vertex AI Pipelines

Monitoring AI Solutions

Vertex AI Model Monitoring, drift detection, performance metrics.

++Feature attribution drift, training-serving skew
++Model performance monitoring
++Alerting via Cloud Monitoring
++Explainable AI: Vertex AI feature attribution

Primary sources:

Vertex AI Model Monitoring

Practice scenarios (5)

Practice scenarios are scenario-based learning, not exam-question mimicry. Each scenario maps to a specific exam domain and includes a worked explanation plus a primary-source citation. Reproducing actual exam items would violate the cert body's NDA; the format here exercises the same underlying concepts under different surface phrasing.

Preview scenario 1 of 5 · Architecting Low-Code ML Solutionsfree preview

A data analyst with strong SQL but limited Python experience needs to train a model directly against a 5TB BigQuery dataset without moving data to a separate ML environment. Which approach fits?

A. BigQuery ML using CREATE MODEL SQL syntax
B. Export the dataset to Cloud Storage and train with custom Vertex AI training
C. Manual scripts on a local laptop
D. Cloud Functions HTTP triggers

Answer: A

BigQuery ML lets data analysts train models against BigQuery datasets using SQL CREATE MODEL syntax. The data does not leave BigQuery. Exporting to Cloud Storage and training in Vertex AI moves the data unnecessarily. Local scripts and Cloud Functions are anti-patterns at this scale.

Reference: BigQuery ML overview

Unlock the rest

4 more practice scenarios with full explanations and primary-source citations

The remaining scenarios cover every exam domain at the same depth as the preview above. Includes the exam-day strategy guide and additional study resources. $147 one-time, lifetime access.

Exam-day strategy

++Identify the Vertex AI sub-product first. Most questions resolve by mapping the use case to Pipelines, Endpoints, Training, Vizier, Model Monitoring, or Workbench.
++Watch for distractors that propose BigQuery ML when the use case clearly needs custom training, or vice versa.
++On serving questions, distinguish online prediction (low latency) from batch prediction (cost-efficient bulk) from streaming.
++Pipelines questions usually have one canonical KFP-based answer; eliminate options that recommend cron + scripts.
++Pace at roughly 2 minutes per question across a 120-minute exam. Flag and return on multi-service architecture questions.

Additional resources

Sources

Official Google Cloud Professional Machine Learning Engineer exam page (certifying body)

Exam fee and blueprint last verified 2026-05-22. Confirm current values with the certifying body before scheduling the exam.

What the add-on ships

++Six domain reviews mapped to the official Google Cloud Professional ML Engineer exam guide

++Three full-length 50 to 60 question mock exams in Google's scenario format

++Vertex AI scenarios: pipelines, model registry, endpoints, feature store, model monitoring

++Vertex AI Search and Conversation, Gemini API, and AI Platform integration patterns

++Direct link to the official Google Cloud exam page as the canonical reference

Exam summary

Domain reviews

Architecting Low-Code ML Solutions

BigQuery ML, AutoML, Vertex AI Workbench.

++BigQuery ML: in-warehouse model training and inference
++Vertex AI AutoML for tabular, image, text, video
++When to use low-code vs custom training

Primary sources:

Google Cloud Professional ML Engineer exam guide

Scaling Prototypes into ML Models

Vertex AI Training, custom containers, hyperparameter tuning.

++Vertex AI custom training with Python packages or custom containers
++Vertex AI Vizier for hyperparameter tuning
++Distributed training: data parallel, model parallel

Primary sources:

Vertex AI documentation

Serving and Scaling Models

Vertex AI Prediction endpoints, online and batch.

++Online prediction endpoints with autoscaling
++Batch prediction jobs
++Private endpoints, traffic splitting for A/B testing
++GPU and TPU acceleration

Primary sources:

Vertex AI Prediction docs

Automating and Orchestrating ML Pipelines

Vertex AI Pipelines on Kubeflow, CI/CD for ML.

++Vertex AI Pipelines with KFP SDK
++Pipeline components, parameters, artifacts
++Vertex AI Model Registry and aliasing
++Cloud Build for CI/CD

Primary sources:

Vertex AI Pipelines

Monitoring AI Solutions

Vertex AI Model Monitoring, drift detection, performance metrics.

++Feature attribution drift, training-serving skew
++Model performance monitoring
++Alerting via Cloud Monitoring
++Explainable AI: Vertex AI feature attribution

Primary sources:

Vertex AI Model Monitoring

Practice scenarios (5)

Preview scenario 1 of 5 · Architecting Low-Code ML Solutionsfree preview

A data analyst with strong SQL but limited Python experience needs to train a model directly against a 5TB BigQuery dataset without moving data to a separate ML environment. Which approach fits?

A. BigQuery ML using CREATE MODEL SQL syntax
B. Export the dataset to Cloud Storage and train with custom Vertex AI training
C. Manual scripts on a local laptop
D. Cloud Functions HTTP triggers

Answer: A

Reference: BigQuery ML overview

Unlock the rest

4 more practice scenarios with full explanations and primary-source citations

The remaining scenarios cover every exam domain at the same depth as the preview above. Includes the exam-day strategy guide and additional study resources. $147 one-time, lifetime access.

Exam-day strategy

++Identify the Vertex AI sub-product first. Most questions resolve by mapping the use case to Pipelines, Endpoints, Training, Vizier, Model Monitoring, or Workbench.

++Watch for distractors that propose BigQuery ML when the use case clearly needs custom training, or vice versa.

++On serving questions, distinguish online prediction (low latency) from batch prediction (cost-efficient bulk) from streaming.

++Pipelines questions usually have one canonical KFP-based answer; eliminate options that recommend cron + scripts.

++Pace at roughly 2 minutes per question across a 120-minute exam. Flag and return on multi-service architecture questions.