The answer is BigQuery ML ARIMA_PLUS with holiday regression. This is the correct choice because it is a low-code ML solution that natively handles time series forecasting while automatically incorporating holiday effects through built-in holiday regression, making it ideal for 5 years of hourly data without requiring custom feature engineering. On the Google Professional Machine Learning Engineer exam, this scenario tests your understanding of when to use BigQuery ML’s native time series capabilities versus custom TensorFlow or AutoML models—a common trap is choosing a more complex solution like LSTMs or Prophet when ARIMA_PLUS already supports holiday seasonality out of the box. Remember that ARIMA_PLUS automatically detects and models holidays, trend, and multiple seasonalities, so for low-code forecasting with holiday effects, it is the direct path. Memory tip: “ARIMA_PLUS adds holidays without the fuss.”
PMLE Architecting low-code ML solutions Practice Question
This PMLE practice question tests your understanding of architecting low-code ml solutions. 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 company wants to use low-code ML for time series forecasting with 5 years of hourly data. They need to incorporate holiday effects. Which solution best meets these requirements?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "best"
Why it matters: Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
BigQuery ML ARIMA_PLUS with holiday regression
BigQuery ML ARIMA_PLUS with holiday regression is the correct choice because it is a low-code solution that natively supports time series forecasting with built-in holiday effect modeling. ARIMA_PLUS automatically handles seasonality, trend, and holiday regression without requiring custom code, making it ideal for 5 years of hourly data.
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.
✗
Custom LSTM model
Why it's wrong here
This is not a low-code solution.
✓
BigQuery ML ARIMA_PLUS with holiday regression
Why this is correct
ARIMA_PLUS directly supports holiday effects in its model.
Clue confirmation
The clue word "best" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
✗
Vertex AI AutoML Tables with timestamp and holiday features
Why it's wrong here
AutoML Tables is for tabular data, not specifically optimized for time series and holiday effects.
✗
Vertex AI AutoML Forecasting with timestamp and holiday feature
Why it's wrong here
AutoML Forecasting is not yet GA and may not support holiday regression.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google Cloud often tests the distinction between AutoML Forecasting and BigQuery ML ARIMA_PLUS, where candidates mistakenly assume AutoML Forecasting natively handles holiday regression, but it requires explicit feature engineering, while ARIMA_PLUS provides built-in holiday support.
Detailed technical explanation
How to think about this question
ARIMA_PLUS in BigQuery ML automatically detects and models multiple seasonalities (e.g., hourly, daily, weekly) and uses holiday regression to adjust for known holiday effects by incorporating a holiday indicator matrix. This approach scales efficiently for large datasets like 5 years of hourly data (over 43,000 data points) without requiring manual feature engineering or custom code.
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.
Architecting low-code ML solutions — This question tests Architecting low-code ML solutions — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: BigQuery ML ARIMA_PLUS with holiday regression — BigQuery ML ARIMA_PLUS with holiday regression is the correct choice because it is a low-code solution that natively supports time series forecasting with built-in holiday effect modeling. ARIMA_PLUS automatically handles seasonality, trend, and holiday regression without requiring custom code, making it ideal for 5 years of hourly data.
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.
Are there clue words in this question I should notice?
Yes — watch for: "best". Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
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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