Study guide
5.1 Organizing & Exploring Tabular, Text & Image Data
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
5.1 Organizing & Exploring Tabular, Text & Image Data
Key Takeaways Explain This Topic AI
BigQuery is the default home for structured tabular ML data, and Cloud Storage is the default home for unstructured files such as images, audio, video, and text documents.
A managed dataset is required for AutoML and optional for custom training, and it adds lineage tracking, dataset versions, statistics, and automatic data splits.
Cloud Storage FUSE streams large files to training jobs as if they were on a local disk, which speeds up data loading for distributed training.
Knowledge Catalog, formerly Dataplex Universal Catalog, is the metadata service for discovering datasets, including Agent Platform managed datasets, across projects and regions.
Exploratory analysis should check label distribution, missing values, duplicates, leakage, and whether training data matches production data before any model is trained.
Last updated: September 2026
The exam guide opens Section 2 with organizing and exploring different data types (tabular, text, and images) for efficient experimenting, training, and serving . In practice, most "the model is bad" problems come from data: the wrong storage layout, unreadable formats, leakage, or training data that doesn't look like production data.
Where Each Data Type Belongs
Data type Primary home Why Access patterns
Structured tabular (transactions, customers, events) BigQuery Serverless SQL at terabyte scale, BigQuery ML, and direct integration with AutoML, pipelines, and Feature Store SQL, BigQuery DataFrames, BigQuery Storage Read API into training code
Unstructured files (images, audio, video, PDFs) Cloud Storage Cheap, durable object storage for large individual files Import files for managed datasets, Cloud Storage FUSE in training jobs, BigQuery object tables for SQL over file metadata
Text corpora Cloud Storage (documents) or BigQuery (short text in rows) Long documents stay as files. Short text such as reviews and tickets lives next to its structured attributes Gemini batch inference from Cloud Storage JSONL or BigQuery, AI.GENERATE_TEXT in SQL
ML-specific formats (TFRecord, sharded files) Cloud Storage Efficient sequential reads for large-scale training tf.data , PyTorch data loaders via FUSE
Features for online serving Feature Store backed by BigQuery Point-in-time training data plus low-latency serving (Section 5.3) Feature views, online stores
Keep storage in the same region as training and serving compute. Cross-region reads add latency and egress cost, and they can break data residency rules.
Managed Datasets and Versions
A managed dataset on Agent Platform is required for AutoML and optional for custom training . Google lists these benefits:
Central organization and governance of data assets across projects and experiments.
Labeling: create annotation sets and labeling tasks inside the dataset.
Lineage: automatically links data to the models trained on it, which helps reproducibility and audits.
Fair comparison: AutoML and custom models trained on the same dataset can be compared directly.
Statistics and visualizations for exploratory analysis.
Automatic splits by fraction, filter, predefined column, or timestamp.
Dataset versions so you can track changes and roll back.
For custom training, the job receives data locations through environment variables such as AIP_TRAINING_DATA_URI , AIP_VALIDATION_DATA_URI , and AIP_TEST_DATA_URI .
Getting Data into Training Efficiently
Option Choose when
Cloud Storage FUSE Unstructured or TFRecord data, very large files, distributed training where workers read in parallel. Streams data instead of copying whole files to each replica
NFS share Very high throughput and low latency, POSIX semantics, or an existing NFS in your VPC shared across jobs and clusters
BigQuery Structured data queried directly by pipelines, notebooks, and training code, including continuous-training triggers on new rows
Managed dataset You want governance, lineage, versions, and automatic splits
Many small files (millions of tiny JPEGs) slow training because each file needs a separate request. Pack them into larger sharded files such as TFRecord for sequential reads.
Discovering Data with Knowledge Catalog
Knowledge Catalog (renamed from Dataplex Universal Catalog on April 10, 2026) is the metadata layer for finding and understanding data across projects and regions. It's integrated with Agent Platform, so managed datasets can be searched and enriched with aspects (similar to the tags in the deprecated Data Catalog). Use it to answer "which table is the approved source for customer churn labels?" before you train on an unofficial copy.
- Exploratory Data Analysis (EDA) Checklist
- Tabular data
Target distribution: class balance for classification, skew and outliers for regression.
Missing values per column and whether missingness itself predicts the label.
Cardinality of categorical columns. A very high-cardinality ID can let the model memorize individual rows.
Leakage: columns recorded after the outcome, such as cancellation_reason when predicting churn.
Duplicates and rows that appear in both training and test sets.
Time coverage: does training cover the seasons and conditions that production will see?
Text data
Length distribution in characters and tokens, which drives model context and cost.
Language mix and encoding problems.
Label consistency, since different annotators often label the same text differently.
Sensitive data such as names, account numbers, and emails (Section 5.4).
Image data
Images per label (AutoML image classification recommends about 1,000 per label, minimum 10).
Resolution, aspect ratio, lighting, and camera differences between training and production.
Near-duplicate images that inflate evaluation metrics.
A "none of the above" class if production images can fall outside every label.
Tools for Exploration
BigQuery SQL for profiling ( COUNT , APPROX_QUANTILES , COUNTIF(col IS NULL) ), directly on full tables.
BigQuery DataFrames ( bigframes ) for pandas-style exploration that runs in BigQuery, so terabyte tables never load into notebook memory.
Colab Enterprise or Workbench notebooks for visualization and iterative analysis (Chapter 6).
TensorFlow Data Validation-style statistics : BigQuery ML's ML.TFDV_DESCRIBE computes statistics you can later compare with ML.TFDV_VALIDATE to catch schema drift (Chapter 15).
Worked Scenario: A Multimodal Claims Dataset
An insurer wants a model that predicts claim severity from structured policy data, adjuster notes, and damage photos.
Policy and claim attributes go in BigQuery, partitioned by claim date and clustered by region for efficient training queries.
Photos stay in Cloud Storage. A BigQuery object table exposes their URIs and metadata, so each photo can be joined to its claim in SQL.
Adjuster notes are short text, so they live in a BigQuery column next to the claim, after PII redaction (Section 5.4).
A managed dataset version freezes the exact training snapshot, and lineage ties it to the resulting model for audits.
EDA shows 70% of photos come from one smartphone app launched last year. The team checks that older claims (scanned prints) are still represented, so production doesn't meet an unfamiliar image style.
Designing for Serving from Day One
Exploration decisions carry into production. If a feature will be needed online, confirm it's available at prediction time and from a low-latency source. Record how each feature is computed so training and serving use the same definition, which is the job of the Feature Store and shared transformations (Sections 5.3 and 15.3).