GCP ML Engineer (Google Cloud Certifications) common mistakes
GCP ML Engineer · Understand the traps and how to avoid them.
Writes a full custom training container in TensorFlow when the requirement is tabular prediction or document/entity extraction that AutoML Tables, BigQuery ML, or a prebuilt Document AI/Natural Language processor already covers.
Candidates study Vertex AI custom training deeply and default to it; they skim the low-code section and under-weight 'choose the simplest solution that meets the requirement' style items.
What to do instead
Before designing anything, list candidate options in a tier: prebuilt API, AutoML, BigQuery ML, Model Garden, custom training. Pick the lowest tier that satisfies the stated accuracy, latency, and explainability constraints, and be able to justify why higher tiers are rejected.
Assumes a Model Garden foundation model can be deployed to an endpoint in any region and immediately serve production traffic, ignoring regional availability, quota, and the tuning option that fits the budget.
Model Garden is presented as a catalog, so candidates treat region and quota as an afterthought and skip the model card details.
What to do instead
For each Model Garden choice, verify the model card's supported regions, deployment container, accelerator requirements, and quota; choose between tuning approaches (prompt design, supervised fine-tuning, distillation) based on data volume and cost, not on which option sounds most advanced.
Designs pipelines and notebooks that read and overwrite a single shared BigQuery table in place, so a model cannot be retrained against the exact data it was trained on.
Local prototyping habits carry over; the candidate optimizes for 'the query runs' rather than for reproducibility across teams.
What to do instead
Use versioned Vertex AI managed datasets or BigQuery table snapshots/copies per run, pass dataset resource IDs as pipeline parameters, and rely on Dataplex/Vertex ML Metadata lineage to record which data version produced which model artifact.
Grants Vertex AI Administrator or Owner to a shared service account (or uses the default compute service account) so every step 'just works', rather than defining per-workload service accounts.
IAM failures surface as opaque pipeline errors, so the fastest unblock is to escalate permissions and move on.
What to do instead
Create a dedicated service account per workload (training, prediction, pipeline runner, monitoring) with the narrowest predefined or custom role, attach it explicitly to Vertex AI custom jobs and endpoints, and use Workload Identity Federation for external CI systems instead of downloaded keys.
Splits a time-ordered or entity-correlated dataset with a random 80/20 train-test split.
Random splitting is the default in most tutorials and libraries; the leak only shows up as an evaluation score that is too good, which candidates interpret as success.
What to do instead
Use a time-based cutoff for temporal data and group/entity-level splits when rows from the same user or device must not span splits; encode the split as an explicit column or use the managed dataset split so the same logic is reproducible in pipelines.
Trains on a sampled extract pulled into a notebook with pandas and calls the prototype 'scaled', leaving no plan for distributed training or in-warehouse training on the full dataset.
Notebooks are where prototypes are validated, and the small-sample path is the only one the candidate has actually executed.
What to do instead
For full-scale training, choose deliberately: Vertex AI custom job with a distributed strategy and reduction server for large data-parallel jobs, or BigQuery ML when the data should stay in the warehouse. Verify the choice against data volume, training time, and accelerator count.
Scores a million rows by looping over a deployed online endpoint, or conversely builds an always-on endpoint for a workload that runs nightly.
The candidate learns one serving pattern (online endpoints) and applies it everywhere, without comparing throughput, cost, and latency requirements.
What to do instead
Match the pattern to the workload: Vertex AI batch prediction with BigQuery or Cloud Storage input/output for bulk scoring, online endpoints for low-latency per-request serving, and private endpoints or VPC-SC when the requirement is network isolation.
Deploys one endpoint per model version with a fixed high replica count and no autoscaling or traffic splitting policy, then cannot explain how a new model would be rolled out safely.
Deployment is treated as a one-time checkbox; canary rollout and cost controls are easy to forget when the focus is on getting a prediction response.
What to do instead
Configure autoscaling with a sensible min/max replica range, use traffic split on a single endpoint for canary rollouts before promoting a version, and consider multi-model endpoints or shared resources when many low-traffic models must be served affordably.
Writes pipeline components whose inputs and outputs are hard-coded bucket paths or implicit global variables instead of declared parameters and artifacts, and runs the pipeline by hand each time.
The pipeline was converted from a notebook script line by line, so the component interface was never designed; caching and reruns appear to work by luck in the small case.
What to do instead
Declare every component's typed inputs, outputs, and artifacts in the KFP component spec so Vertex AI Pipelines can cache and fan out correctly; compile a parameterized template and trigger it via a schedule, Cloud Scheduler, or an Eventarc/Cloud Functions event on new data instead of manual runs.
Sets up alerting only on infrastructure metrics such as CPU, memory, and prediction latency, and never configures training-serving skew, prediction drift, or ground-truth ingestion.
Model quality decay is invisible in infrastructure dashboards, so monitoring looks complete while the model silently degrades.
What to do instead
Enable Vertex AI Model Monitoring on the deployed endpoint with a training dataset baseline, set skew and drift thresholds with an appropriate sampling rate, route alerts through Cloud Monitoring notification channels, and define how ground-truth labels are logged and compared to close the loop.