Create ML Models with BigQuery ML: Challenge Lab Reviews

Mahesh M. · Reviewed יום אחד ago

anugrah S. · Reviewed 2 ימים ago

Sergii M. · Reviewed 2 ימים ago

Clauderson B. · Reviewed 2 ימים ago

zumlath s. · Reviewed 2 ימים ago

NIKHIL R. · Reviewed 2 ימים ago

Abhishek G. · Reviewed 2 ימים ago

Arijit G. · Reviewed 2 ימים ago

manaswin s. · Reviewed 2 ימים ago

good

Zaki Bimo N. · Reviewed 2 ימים ago

Jeni J. · Reviewed 2 ימים ago

Setp to evaluate an improved model is failing always

Mahesh M. · Reviewed 2 ימים ago

Sushant B. · Reviewed 2 ימים ago

thanks

Bagus A. · Reviewed 2 ימים ago

Kiran R. · Reviewed 2 ימים ago

good lab

Jit O. · Reviewed 2 ימים ago

Sheikh E. · Reviewed 2 ימים ago

Mahesh M. · Reviewed 2 ימים ago

Rohit P. · Reviewed 2 ימים ago

Brayen C. · Reviewed 2 ימים ago

Sarwaarth C. · Reviewed 2 ימים ago

The lab's core BQML syntax (CREATE MODEL, ML.PREDICT) is straightforward, but the surrounding task design has significant friction points that go beyond testing ML engineering skills: Undocumented domain knowledge required. The lab assumes prior familiarity with the Google Analytics BigQuery export schema — e.g. mapping hits.eCommerceAction.action_type numeric codes (0-8) to their meaning, or knowing that "traffic source" in the prompt actually maps to channelGrouping rather than the more intuitively-named trafficSource column. None of this is documented in the lab itself; it requires external research or prior GA360 experience.Mismatch with stated learning objective. This is positioned as an "ML Engineer" cert path (model deployment focus), but a large share of the lab's difficulty is data-analyst-level work: schema archaeology, e-commerce funnel semantics, and column disambiguation — not ML deployment itself. BQML's train/predict duplication isn't called out as a limitation. Feature engineering logic must be manually duplicated between the CREATE MODEL query and the ML.PREDICT query, with no pipeline abstraction (unlike sklearn/Vertex AI pipelines). This isn't explained anywhere in the lab, so a mismatch (e.g. missing a feature column) only surfaces as an opaque runtime error. Suggestion: Either provide a schema reference / glossary as part of the lab material, or explicitly frame this lab as also testing data exploration skills — not just ML deployment.

VINCENT L. · Reviewed 2 ימים ago

Aldi S. · Reviewed 2 ימים ago

dinda l. · Reviewed 2 ימים ago

Abhishek R. · Reviewed 2 ימים ago