PMLE Solving business challenges with ML Practice Question
This PMLE practice question tests your understanding of solving business challenges with ml. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
A retail company wants to forecast daily sales for inventory planning. They have 3 years of historical sales data with clear weekly and yearly seasonality. Which approach should they use?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Use BigQuery ML to train an ARIMA_PLUS model
Option D is correct because ARIMA_PLUS in BigQuery ML is specifically designed for time-series forecasting with multiple seasonalities (weekly and yearly). It automatically handles seasonality detection, trend decomposition, and holiday effects, making it ideal for retail sales data with clear periodic patterns.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
✗
Call a pre-built Google Cloud API for sales prediction
Why it's wrong here
No pre-built API exists for custom sales forecasting.
✗
Use a linear regression model in Vertex AI
Why it's wrong here
Linear regression does not capture seasonality and time dependencies.
✗
Use Vertex AI AutoML Tables with date as feature
Why it's wrong here
AutoML Tables is less efficient and may not explicitly model seasonality.
✓
Use BigQuery ML to train an ARIMA_PLUS model
Why this is correct
ARIMA_PLUS handles seasonality and is optimized for time series.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often choose AutoML Tables (Option C) thinking it can handle any structured data, but they miss that AutoML Tables is not a dedicated time-series model and requires manual feature engineering to capture seasonality, whereas ARIMA_PLUS is purpose-built for this scenario.
Detailed technical explanation
How to think about this question
ARIMA_PLUS extends classical ARIMA by automatically selecting differencing orders (p,d,q) and incorporating seasonal components (e.g., weekly period=7, yearly period=365.25) via Fourier terms or seasonal differencing. It also handles missing values, outlier detection, and holiday effects through built-in holiday calendars, which is critical for retail data where sales spikes occur on holidays. In practice, this model can be trained directly in BigQuery ML using `CREATE MODEL` with `model_type='ARIMA_PLUS'` and `time_series_timestamp_col` and `time_series_data_col` parameters, avoiding the need to export data to external tools.
KKey Concepts to Remember
Read the scenario before looking for a memorised answer.
Find the constraint that changes the correct option.
Eliminate answers that are true in general but not in this case.
TExam Day Tips
→Watch for words such as best, first, most likely and least administrative effort.
→Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Solving business challenges with ML — This question tests Solving business challenges with ML — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Use BigQuery ML to train an ARIMA_PLUS model — Option D is correct because ARIMA_PLUS in BigQuery ML is specifically designed for time-series forecasting with multiple seasonalities (weekly and yearly). It automatically handles seasonality detection, trend decomposition, and holiday effects, making it ideal for retail sales data with clear periodic patterns.
What should I do if I get this PMLE question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Question Discussion
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