Study guide
8.1 Choosing the Model Type: ARIMA, Trees, DNNs & LLMs
Google Cloud Professional Machine Learning Engineer PMLE
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1 Chapter 1: Exam Overview & the Agent Platform Map
1.1 PMLE Exam Facts, Policies & the June 2026 Blueprint 1.2 From Vertex AI to Gemini Enterprise Agent Platform: The Product Name Map
2 Chapter 2: Low-Code ML with BigQuery ML
2.1 Choosing, Training & Predicting with BigQuery ML Models 2.2 Feature Engineering & Feature Selection in BigQuery ML 2.3 Fine-Tuning & Evaluating Gemini Models from BigQuery
3 Chapter 3: AutoML & Google Cloud AI APIs
3.1 Training Models with Agent Platform AutoML 3.2 Building with Document AI, Vision, Translation & Other AI APIs
4 Chapter 4: Foundation Models in Model Garden
4.1 Evaluating & Selecting Models in Model Garden 4.2 Building & Tuning Solutions with Gemini, Imagen, Veo & MaaS 4.3 Optimizing Gemini Applications for Cost, Latency & Availability
5 Chapter 5: Exploring, Preprocessing & Protecting Data
5.1 Organizing & Exploring Tabular, Text & Image Data 5.2 Choosing a Preprocessing Tool: BigQuery, Dataflow, Spark or Python 5.3 Creating & Consolidating Features in Agent Platform Feature Store 5.4 Protecting PII & Sensitive Data in ML Workflows
6 Chapter 6: Notebooks, Prototyping & Experiment Tracking
6.1 Workbench vs. Colab Enterprise: Secure, Collaborative Notebooks 6.2 Prototyping with PyTorch, scikit-learn, JAX & Model Garden in Notebooks 6.3 Experiment Environments, Tracking & Lineage with Experiments and ML Metadata
7 Chapter 7: Evaluating Predictive & Generative AI Solutions
7.1 Evaluation Metrics for Predictive Models 7.2 Evaluating Gen AI: Computation Metrics, Rubrics & LLM-as-a-Judge
8 Chapter 8: Choosing the Modeling Approach
8.1 Choosing the Model Type: ARIMA, Trees, DNNs & LLMs 8.2 Choosing the Product & Deployment Strategy 8.3 Modeling Techniques for Interpretability Requirements
9 Chapter 9: Training Data & Training SDKs
9.1 Organizing & Ingesting Training Data from Cloud Storage & BigQuery 9.2 Custom Training on Agent Platform: Containers, Jobs & Worker Pools 9.3 Kubeflow on GKE, AutoML & Tabular Workflows as Training Choices
10 Chapter 10: Troubleshooting, Hyperparameter Tuning & Fine-Tuning
10.1 Troubleshooting ML Training Failures 10.2 Hyperparameter Tuning on Agent Platform 10.3 Fine-Tuning Foundation Models & When to Tune
11 Chapter 11: Training Hardware & Distributed Training
11.1 Choosing CPUs, GPUs & TPUs for Training 11.2 Distributed Training: Data & Model Parallelism
12 Chapter 12: Deploying Models for Inference
12.1 Batch vs. Online Inference on Agent Platform 12.2 Serving Beyond Agent Platform Endpoints: Model Garden, Cloud Run & GKE 12.3 Prebuilt & Custom Serving Containers
13 Chapter 13: Model Versions, Rollouts & Inference Logic
13.1 Organizing & Versioning Models in Model Registry 13.2 Rollout Strategies: A/B Tests, Canaries & Traffic Splits 13.3 Inference Preprocessing & Postprocessing
14 Chapter 14: Scaling Online Serving
14.1 Serving Features Online with Feature Store 14.2 Public, Dedicated & Private Endpoints 14.3 Serving Hardware & Autoscaling for Throughput 14.4 Tuning Models for Production Training & Serving
15 Chapter 15: End-to-End ML Pipelines
15.1 Validating Data & Models in Pipelines 15.2 Agent Platform Pipelines with KFP: Components, Templates & Caching 15.3 Ensuring Consistent Preprocessing Between Training & Serving
16 Chapter 16: Orchestration with Managed Airflow & Ray
16.1 Orchestrating ML Workflows with Managed Service for Apache Airflow 16.2 Ray on Agent Platform for Distributed ML Workloads
17 Chapter 17: Automating Retraining & CI/CD/CT
17.1 Choosing a Retraining Policy 17.2 CI/CD/CT for ML with Cloud Build
18 Chapter 18: Securing AI Systems, Responsible AI & Explainability
18.1 Protecting AI Systems from Data & Model Exfiltration 18.2 Defending Gen AI Apps: Safety Filters, Model Armor & Regex Guardrails 18.3 Responsible AI: Detecting & Monitoring Bias 18.4 Model Explainability on Agent Platform
19 Chapter 19: Monitoring AI Solutions
19.1 Configuring Model Monitoring on Agent Platform 19.2 Training-Serving Skew, Data Drift, Concept Drift & Attribution Drift 19.3 Monitoring, Testing & Evaluating Gen AI Solutions in Production
8.1 Choosing the Model Type: ARIMA, Trees, DNNs & LLMs
Key Takeaways Explain This Topic AI
Gradient-boosted trees are usually the strongest first choice for structured tabular data, while DNNs lead on images, audio, and raw text.
ARIMA_PLUS in BigQuery ML trains one statistical model per time series and trains faster than neural forecasting, which makes it a good baseline.
Neural forecasting methods such as TiDE and Temporal Fusion Transformer fit many related series with covariates, at higher training cost.
LLMs such as Gemini fit open-ended language, reasoning, and multimodal tasks, but a small supervised model is often cheaper and faster for narrow, high-volume predictions.
Model choice balances four exam constraints: cost, complexity, latency, and scalability.
Last updated: September 2026
The exam guide lists choosing the model type (for example, ARIMA, DNN, and LLM) while considering cost, complexity, latency, and scalability . Most wrong answers pick a technique that could work but is too expensive, too slow, or too complex for the stated constraint.
Start with the Problem, Not the Model
Is ML needed at all? If a stable rule solves it ("flag orders over the card limit"), rules are cheaper and easier to explain.
What is the output? A class, a number, a sequence over time, a ranked list, a group, an anomaly flag, or generated content.
What is the input? Structured tables, time series, images, audio, text, or a mix.
What are the constraints? Latency, request volume, budget, labeled data, interpretability, and update frequency.
Model Families and When They Fit
Family Strengths Weaknesses Typical use
Linear / logistic regression Fast, cheap, interpretable, good baseline Misses non-linear interactions unless you engineer features Credit scoring with explanation requirements, quick baselines
Gradient-boosted trees / random forests (XGBoost) Top accuracy on tabular data, handle mixed types and missing values Larger models, less transparent than linear Churn, fraud, propensity, pricing on structured data
Deep neural networks (DNN) Learn complex patterns from large data, including images, audio, text, and embeddings Need more data, compute, tuning, and effort to explain Image classification, speech, wide-and-deep recommendations
CNNs / vision transformers Spatial patterns in images GPU/TPU training cost Defect detection, medical imaging
Sequence models / transformers Order and context in sequences Compute-heavy Text classification, custom sequence tasks
Statistical time series (ARIMA_PLUS) Fast, decomposable (trend, seasonality, holidays), one model per series Limited cross-series learning Store or SKU forecasts, anomaly detection on metrics
Neural forecasting (TiDE, TFT, Seq2Seq+) Learn across many related series, use covariates Slower, costlier training Large retail demand planning with promotions
Time-series foundation model (TimesFM via AI.FORECAST ) No training, instant baseline Low customization and explainability Quick forecasts across many metrics
Matrix factorization / two-tower retrieval Learn user-item preferences Cold start for new users and items Recommendations
k-means clustering Unsupervised segments Choosing k, and business meaning isn't guaranteed Customer segmentation
Autoencoders / PCA Compression, anomaly detection by reconstruction error Need tuning to separate normal from rare Sensor anomaly detection
Embeddings + vector search Semantic similarity across text, images, and products Embedding model choice, index maintenance Semantic search, RAG retrieval, deduplication
LLMs (Gemini, partner, open) Open-ended generation, reasoning, extraction, multimodal understanding, zero-shot tasks Per-token cost, latency, output variability, need for evaluation and safety controls Summarization, conversational agents, document Q&A, complex extraction
Weighing the Four Constraints
Constraint Pushes toward Pushes away from
Low cost Linear models, trees, BigQuery ML, small models, batch scoring Large LLM calls per record, big GPU training
Low complexity / small team BigQuery ML, AutoML, pre-trained APIs, Gemini prompting Custom distributed deep learning
Low latency (ms) Small trees or linear models, distilled or quantized networks, precomputed features Large LLM generation on the request path
High scalability Models that parallelize and serve on autoscaling endpoints, batch inference Stateful, single-machine models
- ARIMA vs. DNN vs. LLM: Worked Comparisons
- Forecasting weekly sales for 2,000 stores
ARIMA_PLUS in BigQuery ML: one statement fits one model per store, trains quickly, and explains trend, seasonality, and holidays. It's the best first choice when explanations matter and covariates are limited.
Neural forecasting (TiDE or TFT through AutoML): consider it when promotions, prices, and weather drive demand across related stores and the extra accuracy justifies the training cost.
LLM : not a forecasting tool for this job. It adds cost and variability without the statistical structure.
Classifying 50 million support tickets a month into 12 categories
Gemini with prompting gets high quality quickly with no training data, but per-token cost at 50 million requests is significant.
Tuned smaller model (Flash-Lite, or distillation into a smaller model) cuts cost and latency once labeled examples exist.
A supervised text classifier is cheapest per prediction at huge scale, but it needs labeled data and maintenance.
A common pattern: start with Gemini to launch fast and generate labels, then tune or distill to a cheaper model as volume grows.
Detecting defects on an assembly line at 30 frames per second
A CNN or AutoML Edge image model running at the edge meets the latency need.
A cloud LLM call per frame is too slow and too expensive.
Data Volume and Label Availability
Labeled data available Reasonable options
None Pre-trained APIs, Gemini zero-shot or few-shot, unsupervised methods (clustering, anomaly detection), TimesFM
- Hundreds of examples Gemini supervised tuning, transfer learning from pre-trained vision or text models
- Thousands to millions of rows Trees, DNNs, AutoML, custom training
- Latency Budget Thinking
When a scenario gives a latency target, add up the request path: feature retrieval (Feature Store lookup), preprocessing, model inference, and postprocessing. A 50 ms budget with a 20 ms feature lookup leaves about 30 ms for inference, which rules out a large generative model call but fits a tree ensemble on a CPU endpoint.
Exam Traps
Picking a DNN for a small tabular dataset where boosted trees are faster and usually more accurate.
Using an LLM for numeric forecasting or high-volume, fixed-label classification when cheaper models meet the bar.
Ignoring interpretability requirements (Section 8.3) when a regulated decision is involved.
Forgetting cold start in recommendations. New items need content-based features or embeddings.