PMLE Architecting Low-Code ML Solutions Practice Question
A retail company wants to forecast daily sales for each of its 500 stores for the next 90 days. They have three years of historical daily sales data stored in BigQuery, including promotions, holidays, and store attributes. The data science team has minimal ML expertise and wants to use SQL to build and deploy the model with minimal coding. Which approach should they use?
⚠ Common exam trap
The trap here is assuming that any regression model can handle time series forecasting, but linear regression fails to capture temporal patterns like seasonality and trends.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Create an ARIMA_PLUS model in BigQuery ML using the sales time series and specify the store ID as the time series identifier.
BigQuery ML's ARIMA_PLUS is purpose-built for time series forecasting and can handle multiple related time series by specifying an identifier column. It automatically models seasonality, holidays, and trends, and supports additional regressors. This requires only SQL, making it ideal for teams with limited ML expertise who need per-store forecasts.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Build a custom LSTM model in Vertex AI using TensorFlow, training on the historical sales data.
Why it's wrong here
Building a custom LSTM requires significant ML expertise and coding, which the team lacks. It also involves managing training infrastructure and deployment, which is not low-code. While LSTMs can model time series, this approach is overkill and does not align with the requirement to use SQL with minimal coding.
- ✗
Use AutoML Forecasting in Vertex AI, exporting the data from BigQuery to Cloud Storage.
Why it's wrong here
AutoML Forecasting requires exporting data to Cloud Storage and using the Vertex AI UI or API, which involves more steps and coding than SQL. While it can forecast, it does not meet the low-code, SQL-only requirement. The team has minimal ML expertise, so a fully managed SQL approach is preferable.
- ✗
Train a linear regression model in BigQuery ML with store ID as a feature and date as a numeric feature.
Why it's wrong here
Linear regression assumes a linear relationship and cannot capture complex seasonality, holidays, or non-linear trends in time series data. Using date as a numeric feature imposes a linear trend, which is unrealistic. Store ID as a categorical feature would require one-hot encoding and would not model temporal dependencies, leading to poor forecasts.
- ✓
Create an ARIMA_PLUS model in BigQuery ML using the sales time series and specify the store ID as the time series identifier.
Why this is correct
ARIMA_PLUS is designed for univariate time series forecasting and can handle multiple time series by specifying a time series ID column. It automatically handles seasonality, holidays, and trends, and can incorporate additional regressors like promotions. This requires only SQL, matching the team's low-code requirement and the need to forecast per store.
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