Create ML Models with BigQuery ML: Challenge Lab recensioni

Mahesh M. · Recensione inserita 1 giorno fa

anugrah S. · Recensione inserita 2 giorni fa

Sergii M. · Recensione inserita 2 giorni fa

Clauderson B. · Recensione inserita 2 giorni fa

zumlath s. · Recensione inserita 2 giorni fa

NIKHIL R. · Recensione inserita 2 giorni fa

Abhishek G. · Recensione inserita 2 giorni fa

Arijit G. · Recensione inserita 2 giorni fa

manaswin s. · Recensione inserita 2 giorni fa

good

Zaki Bimo N. · Recensione inserita 2 giorni fa

Jeni J. · Recensione inserita 2 giorni fa

Setp to evaluate an improved model is failing always

Mahesh M. · Recensione inserita 2 giorni fa

Sushant B. · Recensione inserita 2 giorni fa

thanks

Bagus A. · Recensione inserita 2 giorni fa

Kiran R. · Recensione inserita 2 giorni fa

good lab

Jit O. · Recensione inserita 2 giorni fa

Sheikh E. · Recensione inserita 2 giorni fa

Mahesh M. · Recensione inserita 2 giorni fa

Rohit P. · Recensione inserita 2 giorni fa

Brayen C. · Recensione inserita 2 giorni fa

Sarwaarth C. · Recensione inserita 2 giorni fa

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. · Recensione inserita 2 giorni fa

Aldi S. · Recensione inserita 2 giorni fa

dinda l. · Recensione inserita 2 giorni fa

Abhishek R. · Recensione inserita 2 giorni fa