PMLE Flashcards — Free Google Professional Machine Learning Engineer Study Cards

@dsl.component(base_image='python:3.9')\ndef my_component(input_text: str) -> Metrics:\n from kfp.dsl import Metrics\n metrics = Metrics()\n metrics.log_metric('length', len(input_text))\n return metrics

Option D is correct because it uses the `@dsl.component` decorator with a `base_image` parameter, which is required for lightweight Python function components in Kubeflow Pipelines SDK v2. The decorator enables the component to run without a custom container by specifying a base image (here, `python:3.9`), and the function correctly returns a `Metrics` artifact after logging a metric. Without the decorator or with an incorrect decorator, the component would not be recognized as a pipeline component.

Use MLflow autologging by calling `mlflow.autolog()` before the training code and wrap the training script with `mlflow.start_run()`.

Vertex AI Experiments supports autologging via the MLflow library. By wrapping the training code with mlflow.start_run() and enabling autolog, all parameters, metrics, and artifacts are captured automatically.

n1-standard-4 with 1 NVIDIA Tesla T4

Option A is correct because the n1-standard-4 machine type supports attaching GPUs such as the NVIDIA Tesla T4, which provides GPU acceleration for low-latency online predictions. Vertex AI endpoints require a machine type that allows GPU attachment, and the n1-series is one of the few families that supports GPUs, while the T4 offers a good balance of cost and performance for inference workloads.

Vertex AI Model Monitoring

Vertex AI Model Monitoring is specifically designed to monitor deployed models for feature drift, feature skew, and prediction drift. It uses statistical methods to compare serving distributions over time or against training data.

Use BigQuery ML to create a logistic regression model (LOGISTIC_REG) on the data directly in BigQuery.

Option C is correct because BigQuery ML allows the team to build and train a logistic regression model directly on data stored in BigQuery using SQL syntax, without moving data to a separate environment. The LOGISTIC_REG model type is specifically designed for binary classification tasks like churn prediction, and it runs entirely within BigQuery's serverless infrastructure, satisfying the team's requirement to avoid data movement.

tf.distribute.MirroredStrategy

MirroredStrategy performs synchronous data parallelism across multiple GPUs on a single machine. MultiWorkerMirroredStrategy is for multiple machines, not needed here. ParameterServerStrategy is for distributed asynchronous training. TPUStrategy is for TPUs.

Vertex AI Feature Store with access control and Vertex AI ML Metadata for model versioning

Vertex AI Feature Store provides fine-grained access control to training data, ensuring only authorized users can access it. Vertex AI ML Metadata automatically tracks and versions model artifacts, lineage, and parameters, which aligns with the requirement for automated versioning and tracking.

Use Vertex AI Forecasting to train a time-series model with holiday and weather features

Vertex AI Forecasting is purpose-built for time-series forecasting with support for exogenous features like holidays and weather, making it the ideal choice for weekly sales predictions across 500 stores. It handles multiple time series automatically and integrates with the required weekly retraining cycle, unlike generic regression models that lack temporal awareness.