Home › GCP ML Engineer › cloudcomputing › mleprototyping › Which Google Cloud feature should an ML Engineer…
Which Google Cloud feature should an ML Engineer use to leverage Apache Spark in a Jupyter notebook — for distributed feature engineering on huge tabular datasets — referenced in PMLE §2.2?
ASingle-node pandas on a laptop
BCloud DNS
CNotebooks on Dataproc (Spark kernels in JupyterLab) or BigQuery DataFrames + Spark
DCloud Memorystore
Answer & Solution
Correct answer: C. Notebooks on Dataproc (Spark kernels in JupyterLab) or BigQuery DataFrames + Spark
Dataproc-backed notebooks expose Spark kernels (per PMLE §2.2). Single-node pandas doesn't scale; the others aren't Spark.
Related questions
Which industry-specific Google Cloud ML API should an ML Engineer use to extract structureWhich Vertex AI service should an ML Engineer use to MANAGE labelled datasets (image / texWhich Vertex AI capability should an ML Engineer use to handle PII / PHI during data preprWhich Vertex AI capability should an ML Engineer use to track + compare experiment runs (hWhich Google Cloud service provides COLAB notebooks with enterprise IAM + VPC controls + pWhich Vertex AI service provides managed Jupyter notebooks with GCP integration + idle-shuWhich Vertex AI feature stores precomputed features in a centralised online + offline storWhich Vertex AI capability should an ML Engineer use to train custom models on tabular / i