Certification Stack
3 certifications. Maximum ROI. Aligned to consulting, product building, and partnerships.
🥇 Must-Have · Primary
Google Cloud Professional ML Engineer
Strongest enterprise AI signal. Covers full ML lifecycle on GCP — the platform deeply embedded in banking and financial services. Opens doors to Google partnership programs and senior consulting engagements.
🥇 Must-Have · Secondary
AWS ML Specialty
AWS dominates enterprise banking cloud. Two-cloud ML fluency positions you as a vendor-agnostic architect — rare and highly valued. Starts Month 3 after GCP is complete.
🥈 High-Value · Parallel
Salesforce AI Specialist
Zero-friction leverage on your existing 4+ Salesforce certifications. Fast win that stacks directly on what you already know. Run parallel to GCP at 10 min/day in Month 1–2.
5-Month Execution Timeline
Parallel execution rule: Never more than 2 certifications at once. 70/30 focus split.
Month 1–2
GCP (Primary) + Salesforce AI Specialist (Parallel)
- 20 min/day → GCP ML Engineer — Phases 1–4
- 10 min/day → Salesforce AI Specialist — All 4 phases
- Target: Salesforce AI Specialist exam complete by end of Month 2
Month 3
GCP Full Focus + Exam
- 30 min/day → GCP Phases 5–6, MLOps, Mock exams
- Weekly mock exams starting Week 9
- Book exam when readiness ≥ 80%
Month 3–4
GCP Complete → Begin AWS ML Specialty
- Immediately start AWS after GCP exam
- 30 min/day → AWS Phases 1–3
- GCP knowledge transfers — SageMaker mirrors Vertex AI
Month 5
AWS Full Focus + Exam
- 30 min/day → AWS Phase 4, Mock exams
- Book exam when readiness ≥ 80%
- You are now a certified dual-cloud ML engineer
Why This Stack Works: GCP + AWS covers 70%+ of enterprise cloud environments. Salesforce AI Specialist leverages your existing edge. Together they signal: enterprise AI architect, not just a generalist. Three certifications that tell one coherent story.
GCP Professional ML Engineer
Primary certification. 6 phases. 8–12 weeks at 20–30 min/day. Exam: $200 USD.
Exam Focus: The GCP ML Engineer exam is heavily scenario-based. It tests your ability to choose the RIGHT GCP service for a given problem — not just know what each service does. Think: "When do I use AutoML vs Custom Training? When is Dataflow better than BigQuery?" That's the mindset.
PHASE 01
GCP Architecture & ML Fundamentals
Week 1–2
▼
Goal: Understand how GCP is structured as a 4-layer ML platform and when to use each layer. This is the mental model everything else builds on.
GCP 4-Layer ML Architecture
The foundation. GCP organizes ML into 4 layers: AI APIs (pre-built), AutoML (low-code), Vertex AI (custom training), and infrastructure (TPUs/GPUs). Every exam question maps to one of these layers. Know when to use which.
ML Problem Types & Framing
The exam tests whether you can frame a business problem as an ML problem. Classification vs Regression vs Clustering vs Recommendation vs Time Series. You must identify the right problem type before selecting a solution.
Build vs Buy Decision Framework
Critical for consulting AND the exam. When do you use a pre-built API (Vision AI, NLP API) vs build a custom model? Rule: Buy when the problem is generic. Build when you have unique data or need domain-specific accuracy.
Batch vs Real-Time Inference
Heavily tested. Batch = process large datasets offline (cheaper, slower). Real-time = low latency predictions on demand (more expensive). Banking use cases: Batch = end-of-day credit scoring. Real-time = fraud detection at transaction time.
PHASE 02
Data Engineering for ML (BigQuery, Dataflow, Pub/Sub)
Week 2–3
▼
Pattern to memorize: Pub/Sub → Dataflow → BigQuery = the canonical GCP streaming data pipeline. This shows up constantly in exam scenarios.
BigQuery & BigQuery ML
GCP's serverless data warehouse. BigQuery ML lets you train ML models directly in SQL — no Python needed. Exam tests: When to use BigQuery ML vs Vertex AI? Answer: BigQuery ML for structured tabular data where you want simplicity and speed. Vertex AI when you need custom architectures or computer vision.
Dataflow (Apache Beam)
Fully managed stream and batch data processing. Built on Apache Beam. Key insight: Dataflow processes data IN TRANSIT. BigQuery stores data AT REST. Exam often presents streaming scenarios — Dataflow is almost always the answer for real-time data transformation.
Pub/Sub (Event Streaming)
GCP's managed message queue. Think of it as the entry point for real-time data. Pattern: Source events → Pub/Sub (ingest) → Dataflow (process) → BigQuery (store) → Vertex AI (train/predict). Pub/Sub decouples producers and consumers — critical for enterprise banking event architectures.
Vertex AI Feature Store
Central repository for ML features. Solves training-serving skew: the same feature values are used in training AND serving. Exam: If a scenario mentions inconsistent model performance between training and production — Feature Store is the solution.
Cloud Storage & Data Formats
GCS = object storage. Training data lives here. Know when to use CSV vs TFRecord vs Avro vs Parquet. TFRecord = optimized for TensorFlow training. Parquet = columnar format, efficient for BigQuery. Avro = schema evolution, good for Pub/Sub.
PHASE 03
Vertex AI — Training, Deployment & Pre-built AI
Week 3–5
▼
Exam weight: Vertex AI is the highest-weighted domain on the exam. Spend the most time here. Know every major component and the scenarios that call for each one.
Vertex AI Training: AutoML vs Custom Training
AutoML = no code, managed, GCP picks the architecture. Custom Training = bring your own code (TF, PyTorch, sklearn). Exam pattern: If you have tabular/image/text data and want speed → AutoML. If you have a unique model architecture or specific framework → Custom Training. AutoML requires minimum data thresholds — exam tests this.
Online vs Batch Prediction Endpoints
Online Endpoints = real-time, low latency, REST API. Batch Prediction = asynchronous, large datasets, cheaper. Always ask: does the business need immediate predictions or can it wait? Fraud detection = Online. End-of-month churn scoring = Batch.
Vertex AI Model Registry
Central store for all trained models — versioning, lineage, metadata. Exam: Every production model MUST be registered before deployment. If a question asks about tracking model versions or promoting models to production — Model Registry is the answer.
Pre-built AI APIs (Vision AI, NLP API, Document AI)
These are plug-and-play AI APIs for common problems. Vision AI = image classification, object detection. NLP API = sentiment, entity extraction. Document AI = extract structured data from scanned documents. Banking use case: Document AI for loan application processing — extract data from PDFs automatically.
Vertex AI Workbench & Notebooks
Managed Jupyter notebooks on GCP. Used for exploration and experimentation. Exam: Workbench is the environment for data scientists to explore data and prototype models before training at scale.
PHASE 04
MLOps — Pipelines, CI/CD & Automation
Week 5–7
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Why this matters for consulting: MLOps is what separates toy AI projects from production-grade enterprise AI. This is where your PM background becomes a superpower — you already understand process, governance, and quality gates.
Vertex AI Pipelines (Kubeflow / TFX)
Orchestrate end-to-end ML workflows: data ingestion → preprocessing → training → evaluation → deployment. Built on Kubeflow Pipelines or TFX. Each step is a containerized component. Exam: If a scenario mentions repeatable, automated ML workflows — Vertex AI Pipelines is the answer.
CI/CD for ML with Cloud Build
Apply software engineering practices to ML. Triggered code changes → automated build → unit tests → model training → evaluation gate → deploy if metrics pass. Cloud Build = GCP's CI/CD service. Exam tests understanding of where quality gates go in the ML pipeline.
Cloud Composer (Apache Airflow)
Workflow orchestration for data pipelines. Important distinction: Cloud Composer (Airflow) orchestrates DATA workflows. Vertex AI Pipelines orchestrates ML workflows. Exam trap: Don't confuse these two. If the question is about scheduling data ETL → Composer. If it's about ML pipeline steps → Vertex AI Pipelines.
Container Registry & Artifact Registry
Store and manage Docker container images for ML training jobs. Artifact Registry = newer, recommended. Container Registry = legacy. Custom training jobs run in containers — your model code + dependencies are packaged as a Docker image and pushed here before training.
MLOps Maturity Levels (Level 0, 1, 2)
Google's framework for ML system maturity. Level 0 = manual, ad-hoc. Level 1 = automated training pipeline. Level 2 = fully automated CI/CD ML pipeline. Exam scenario: A bank is manually retraining models every quarter — which MLOps level are they at, and what do they need to advance?
PHASE 05
Model Monitoring, Governance & Explainability
Week 7–9
▼
Banking/Regulated Industry Angle: This phase is CRITICAL for your consulting work. Regulators require explainability and bias monitoring for AI in banking. Know this cold — it's both exam content AND client value.
Data Drift vs Concept Drift
Data drift = the distribution of input features changes over time (e.g., customer demographics shift). Concept drift = the relationship between inputs and outputs changes (e.g., what predicts default risk changes after an economic crisis). Both degrade model accuracy. Exam tests: Which type of drift is described in the scenario?
Vertex AI Model Monitoring
Automatically monitors deployed models for feature drift and prediction drift. Sends alerts when metrics exceed defined thresholds. Exam: If a question asks how to detect when a model's performance is degrading in production — Model Monitoring is the answer.
Explainable AI (SHAP / Feature Attributions)
Vertex AI Explainable AI provides feature attributions — which features contributed most to each prediction. Based on SHAP (Shapley values). Banking use case: Explaining to a loan officer WHY the model denied a credit application. Exam: Explainability is required for regulated industry models.
Bias Detection & Model Fairness
What-If Tool and Vertex AI Explainable AI help detect demographic bias. Critical in banking for fair lending compliance. Exam: Scenario where a credit model shows different accuracy for different demographic groups — use Explainable AI + What-If Tool to investigate.
PHASE 06
Exam Preparation — Mock Exams & Readiness
Week 9–12
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Only book the exam when readiness ≥ 80–85% on mock exams. Do not rush. Two weeks of mock exam practice is worth more than cramming new content.
Official Google Sample Questions
Start here. Google publishes sample questions that reveal the style, tone, and depth of real exam questions. Do these first to calibrate your readiness baseline.
Whizlabs Practice Exams
Best third-party practice exams for GCP ML Engineer. Scenario-based questions that closely mirror the real exam. Use timed mode to simulate exam conditions. Target: 80%+ consistently before booking.
A Cloud Guru / Linux Foundation Practice
Additional practice exam source. Good for exposure to different question styles. Use alongside Whizlabs — not instead of it.
AWS ML Specialty
Starts Month 3 after GCP complete. 4 phases. 8–10 weeks. GCP knowledge transfers directly.
GCP → AWS Concept Map: Vertex AI = SageMaker. Pub/Sub = Kinesis. Dataflow = Kinesis Data Analytics. BigQuery = Redshift + Athena. Cloud Storage = S3. You already know the concepts — you're just learning the AWS service names.
PHASE 01
SageMaker Core & AWS AI Services
Week 1–2
▼
Amazon SageMaker — Core Platform
SageMaker is AWS's fully managed ML platform — equivalent to Vertex AI on GCP. Covers everything from data labeling to model deployment. Know: SageMaker Studio (notebook IDE), SageMaker Training, SageMaker Endpoints (online/batch), SageMaker Model Monitor, SageMaker Pipelines.
SageMaker Built-in Algorithms
AWS provides optimized built-in algorithms: XGBoost, Linear Learner, K-Means, DeepAR (time series), BlazingText (NLP), Object Detection. Exam: Know which algorithm fits which problem type. DeepAR = forecasting. XGBoost = tabular classification/regression.
AWS AI Services (Rekognition, Comprehend, Textract)
Pre-built AI APIs. Rekognition = image/video analysis (= Vision AI). Comprehend = NLP, sentiment, entities (= NLP API). Textract = extract text and structured data from documents (= Document AI). Exam: Map business problem → correct AWS service. Don't overthink it.
SageMaker Ground Truth (Data Labeling)
Managed data labeling service. Human labelers + automated labeling. Exam: If a scenario needs labeled training data at scale with human-in-the-loop review — Ground Truth is the answer.
PHASE 02
AWS Data Engineering for ML
Week 2–4
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AWS Data Pattern: Kinesis → Lambda/Kinesis Analytics → S3 → Glue → Athena/Redshift → SageMaker. Map this to GCP: Pub/Sub → Dataflow → GCS → Dataproc → BigQuery → Vertex AI.
Amazon Kinesis (Data Streams, Firehose, Analytics)
Real-time data streaming on AWS. Kinesis Data Streams = ingestion (= Pub/Sub). Kinesis Firehose = delivery to S3/Redshift (= Pub/Sub + Dataflow). Kinesis Analytics = SQL on streaming data (= Dataflow). Exam: Match the Kinesis variant to the scenario requirement.
S3, Glue & Athena
S3 = object storage (= GCS). AWS Glue = serverless ETL + data catalog (= Dataflow + Data Catalog). Athena = serverless SQL on S3 (= BigQuery). Exam: When data lives in S3 and you need to query it without loading it into a database — Athena. When you need to transform and prepare data — Glue.
Amazon Redshift
Data warehouse for structured analytics (= BigQuery). Redshift ML allows training models using SageMaker Autopilot directly from Redshift SQL. Exam: Structured analytical queries at scale → Redshift. Ad-hoc queries on S3 data → Athena.
PHASE 03
MLOps, Monitoring & Governance on AWS
Week 4–7
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SageMaker Pipelines & Model Registry
End-to-end ML pipeline orchestration on AWS (= Vertex AI Pipelines). Model Registry tracks versions and manages approval workflows for production deployment. Exam: Any scenario about automating the ML lifecycle end-to-end — SageMaker Pipelines.
SageMaker Model Monitor
Detects data drift, model quality drift, and bias drift in deployed models (= Vertex AI Model Monitoring). Exam: If a question asks how to detect degrading model performance post-deployment — SageMaker Model Monitor.
SageMaker Clarify (Bias & Explainability)
Detects bias in datasets and trained models. Provides SHAP-based feature attributions (= Vertex AI Explainable AI). Critical for regulated industries. Exam: Fair lending compliance scenario → SageMaker Clarify.
AWS Security for ML (IAM, VPC, KMS)
Heavily tested in AWS exams. IAM = who can access what. VPC = network isolation for training jobs. KMS = encrypt data at rest and in transit. Banking requirement: All ML training data must be encrypted. Training jobs must run inside a private VPC.
PHASE 04
Exam Preparation — Mock Exams & Readiness
Week 7–10
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AWS Official Practice Exam
AWS sells an official practice exam ($40) that reflects the real exam style closely. Do this first to calibrate. Then use Tutorials Dojo for volume.
Tutorials Dojo Practice Exams
Best third-party practice exams for AWS ML Specialty. Detailed explanations. Timed mode available. Community-maintained and highly accurate. Target 80%+ before booking exam.
Salesforce AI Specialist
Parallel cert. Runs Month 1–2 at 10 min/day. Leverages your existing 4+ Salesforce certifications. 4 phases.
Your Advantage: You already understand Salesforce architecture, CRM data models, and platform capabilities. You are learning the AI layer on top of what you already know — not starting from scratch. This is why it's only 3–4 weeks.
PHASE 01
Einstein AI Architecture & Data Cloud Foundation
Week 1
▼
Einstein AI 4-Layer Architecture
Layer 1: Einstein Copilot (conversational AI assistant). Layer 2: Einstein Features (Lead Scoring, Forecasting, etc.). Layer 3: Einstein Platform (build custom AI). Layer 4: Data Cloud (the fuel — unified customer data). Every exam question maps to one of these layers. Know the hierarchy cold.
Data Cloud as AI Foundation
Data Cloud unifies all customer data into a single profile. Einstein AI features get more accurate as Data Cloud provides richer data. Exam: Why is Einstein Lead Scoring inaccurate? Most likely = insufficient or poor quality CRM data. Data Cloud solves this by unifying data sources.
PHASE 02
Einstein Predictions & Scoring Features
Week 1–2
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Einstein Lead & Opportunity Scoring
Ranks leads and opportunities by likelihood to convert. Requires minimum 1,000 records with historical outcomes to activate. Exam: Most common trap = saying Einstein Scoring doesn't work because it hasn't been configured — it actually requires sufficient historical data first.
Einstein Prediction Builder
Build custom AI predictions on any Salesforce object without code. Exam: When a business needs a prediction that Einstein doesn't provide out of the box (e.g., predict customer churn on a custom object) → Prediction Builder.
Einstein Next Best Action
Surfaces AI-powered recommendations to sales/service reps at the right moment. Combines predictions + business rules. Exam: When a question asks about surfacing contextual recommendations to reps during a customer interaction → Next Best Action.
PHASE 03
Einstein Copilot, Agentforce & Prompt Builder
Week 2–3
▼
Highest growth area on the exam. Agentforce and Einstein Copilot are Salesforce's biggest AI bets right now. Expect heavy exam weighting here.
Einstein Copilot
Conversational AI assistant embedded across Salesforce. Users ask questions in natural language, Copilot takes actions. Powered by LLMs. Exam: Know what Copilot can and cannot do. It operates within the Salesforce data model — it can't access external data without Prompt Builder + Data Cloud grounding.
Agentforce (Autonomous AI Agents)
Next generation beyond Copilot. Agents can autonomously complete multi-step tasks without human prompting. Built on Topics + Actions framework. Exam: Copilot = assists humans. Agentforce = operates autonomously. Know when each is appropriate.
Prompt Builder
Build, manage, and deploy prompt templates with dynamic data from Salesforce. Uses merge fields to inject live CRM data into prompts. Can be grounded with Data Cloud for richer context. Exam: Any scenario about customizing what an LLM knows or says using Salesforce data → Prompt Builder.
PHASE 04
Einstein Trust Layer & Exam Preparation
Week 3–4
▼
Most heavily tested topic for regulated industries. Banking clients will ask you about this constantly. Know it cold — every component and why it matters for data security and compliance.
Einstein Trust Layer — All 5 Components
Salesforce's security framework for AI. 1) Zero Data Retention — LLM provider never stores your data. 2) Data Masking — PII stripped before data leaves Salesforce. 3) Toxicity Detection — filters harmful outputs. 4) Audit Trail — logs all AI interactions for compliance. 5) Grounding — grounds AI responses in your actual Salesforce data. Exam: Know what each component does and why it exists.
Focus on Force Practice Exams
The gold standard for Salesforce exam prep. Questions are style-matched to real Salesforce exams. Detailed explanations. Use when you hit 50% readiness in coaching sessions.
Official Salesforce AI Specialist Exam Guide
Download this on Day 1 and use it as your checklist. Every exam topic is listed with percentage weighting. Study the highest-weighted topics most deeply. Agentforce + Trust Layer = highest weight right now.
Daily Coaching Structure
30 minutes. Every session. High-impact and focused.
OPTION ASplit Day (Preferred)
0–7 min
Concept deep-dive — primary cert. Mental models, not memorization.
7–13 min
Real-world application — banking/enterprise scenario mapping.
13–22 min
3–5 exam-style practice questions — scenario-based, elimination technique.
22–27 min
Answer breakdown — correct + incorrect options explained.
27–30 min
Secondary cert (10 min Salesforce) OR weakness log update.
OPTION BAlternating Days
Day 1
Full 30 min → GCP ML Engineer (Primary)
Day 2
Full 30 min → Salesforce AI Specialist (Secondary)
Day 3
Full 30 min → GCP (repeat cycle)
Use when
Concept is complex and needs full focus. Better for deep topics.
Special Session Modes
Use these commands to trigger different coaching modes.
💬 "Revision Mode"
Rapid-fire questions on weak areas. No explanations until you answer. Timed. Forces active recall. Use 3+ days before exam.
🧪 "Mock Exam"
20–30 timed questions. Real exam simulation. Score + weak area report delivered after. Use weekly at 60%+ readiness.
🔍 "Deep Dive [Topic]"
Full 30 min on one concept. Maximum depth. Use when a topic is unclear or exam-critical. Example: "Deep Dive MLOps"
📊 "Dashboard Update"
Review readiness %, update weak areas, plan next 5 sessions. Use weekly or when pivoting topics.
Weekly Rhythm
Structure your week to balance depth, practice, and review.
Mon–Wed
New concepts — go deep on 2–3 topics per week maximum.
Thu
Application day — map concepts to real banking/enterprise scenarios.
Fri
Practice questions — 10–15 questions across the week's topics.
Sat/Sun
Optional: 15–20 min Trailhead or GCP Skills Boost lab to reinforce.
Weekly
From Week 9: Add one 20–30 min timed mock exam per week.
All Study Resources
Every resource ranked by priority, cost, and when to use it. Use these alongside daily coaching sessions.
Strategy: Week 1–2 = coaching only. Week 3+ = add official platform (Trailhead / GCP Skills Boost) for the same topic as that day's session. At 60% readiness = start practice exams.
GCP ML Engineer Resources
| Resource | Type | Cost | When to Use | Link |
|---|---|---|---|---|
GCP ML Engineer Exam Guide Official exam blueprint — lists every topic and domain weighting |
Official | Free | Download Week 1. Use as checklist throughout. | Open → |
Google Cloud Skills Boost Official Google learning platform with ML path, labs, and quizzes |
Official | Free/Paid | Week 3+. Use after each session on the same topic. | Open → |
Google ML Crash Course Free ML fundamentals by Google engineers. Covers core concepts. |
Free | Free | Week 1–2. Foundation concepts only. | Open → |
Coursera: GCP ML Engineer Certificate Official Google course on Coursera. Structured video learning. |
Course | ~$50/mo | Open → | |
GCP Architecture Center Real reference architectures — exactly what the exam scenario tests |
Official | Free | Phase 4–5. When studying MLOps and system design. | Open → |
Whizlabs Practice Exams Best third-party practice exam for GCP ML Engineer |
Practice | ~$30 | At 60%+ readiness. Weekly timed practice. | Open → |
A Cloud Guru — GCP ML Engineer Video course + practice exams. Good supplementary resource. |
Course | ~$40/mo | Optional. Additional practice exam source at 60%+. | Open → |
AWS ML Specialty Resources
| Resource | Type | Cost | When to Use | Link |
|---|---|---|---|---|
AWS ML Specialty Exam Guide Official exam blueprint with domain weightings and topic list |
Official | Free | Download at start of Month 3. Use as checklist. | Open → |
AWS Free ML Training AWS's own free ML learning content and digital courses |
Official | Free | Phase 1–2 of AWS. Use after sessions on same topic. | Open → |
AWS Official Practice Exam Official practice questions from AWS — closest to real exam style |
Official | $40 | At 60%+ readiness. First practice exam to take. | Open → |
Tutorials Dojo — AWS ML Specialty Best third-party practice exams for AWS. Detailed explanations. |
Practice | ~$20 | At 60%+ readiness. Weekly timed practice exams. | Open → |
AWS SageMaker Documentation Official SageMaker docs — reference when concepts are unclear |
Official | Free | Use as reference throughout. Don't read cover to cover. | Open → |
Salesforce AI Specialist Resources
| Resource | Type | Cost | When to Use | Link |
|---|---|---|---|---|
Salesforce AI Specialist Exam Guide Official exam blueprint — download Week 1 and use as checklist |
Official | Free | Download Week 1. Check off topics as you cover them. | Open → |
Trailhead: AI for Salesforce Trail Official Salesforce learning path for AI concepts and features |
Official | Free | Week 1+. Your primary study material alongside coaching. | Open → |
Trailhead: Einstein Trust Layer Deep dive module on the 5 components of Einstein Trust Layer |
Official | Free | Phase 4. Critical for exam and banking clients. | Open → |
Trailhead: Agentforce Trail Getting started with Agentforce — highest exam growth area |
Official | Free | Phase 3. High exam weighting — prioritize this. | Open → |
Focus on Force — AI Specialist Best Salesforce exam prep. Questions closest to real exam style. |
Practice | ~$40 | At 50%+ readiness. Weekly practice before exam. | Open → |
Certification Readiness Dashboard
Track your progress. Updated each session. Exam only when ≥ 80–85% readiness.
📊 Readiness Tracker
GCP ML Engineer
5%
Salesforce AI Specialist
5%
AWS ML Specialty
0%
✅ Strengths (So Far)
✓ Build vs Buy decision framework
✓ Batch vs Real-time pattern
✓ GCP 4-layer architecture overview
✓ Salesforce platform foundation
More will be added each session
⚠️ Weak Areas (Focus Here)
→ GCP service differentiation (Dataflow vs Composer)
→ Vertex AI feature depth
→ MLOps pipeline design
→ Einstein Trust Layer components
→ Feature Store vs BigQuery ML
📅 Current Phase
Month 1 — Week 1
Primary: GCP Phase 1 — Architecture
Secondary: SF Phase 1 — Einstein Architecture
Next: GCP Phase 2 — BigQuery & Data Engineering
Your 3 Actions Right Now
Complete these before Session 2 begins.
01
Download the GCP ML Engineer Exam Guide and save it as your master checklist → cloud.google.com/certification/machine-learning-engineer
02
Bookmark GCP Skills Boost ML Path — this is your daily lab resource → cloudskillsboost.google/paths/17
03
Open the Salesforce AI Specialist trail on Trailhead → trailhead.salesforce.com/credentials/aispecialist
Session 1 Pending Question
Answer this before Session 2 to unlock the next topic.
Question: Einstein Lead Scoring is showing inaccurate predictions for a client. What is the MOST likely cause?
A) Einstein Scoring hasn't been activated in Setup
B) There is insufficient or poor quality historical CRM data
C) The org is on the wrong Salesforce license tier
D) Einstein Lead Scoring requires Data Cloud to function
Answer in your next message to unlock Session 2.
A) Einstein Scoring hasn't been activated in Setup
B) There is insufficient or poor quality historical CRM data
C) The org is on the wrong Salesforce license tier
D) Einstein Lead Scoring requires Data Cloud to function
Answer in your next message to unlock Session 2.