- A
Set the data_frequency parameter to 'daily'
Why wrong: While you can set it, BQML auto-detects frequency, so it's optional.
- B
Use the 'num_trials' parameter for hyperparameter tuning
Why wrong: num_trials is for auto. But ARIMA+ does not require manual tuning.
- C
Specify a time_series_timestamp_col and time_series_data_col
These are required columns for time-series.
- D
Set the 'split_method' to 'time_series'
Why wrong: BQML does not have a split_method for time-series; evaluation is done on hold-out.
- E
Set the model_type to 'ARIMA_PLUS'
ARIMA_PLUS is the time-series model in BQML.
Question 412 of 1,000
PDE Preparing and Using Data for Analysis Practice Question
This PDE practice question tests your understanding of preparing and using data for analysis. This is a configuration task: choose the command set that satisfies every stated requirement. Small differences — like 'secret' vs 'password' or 'transport input ssh' vs 'all' — change whether the answer is correct. 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 BigQuery ML to train a time-series forecasting model on historical sales data. The data is recorded daily for 3 years. They need to evaluate model accuracy using time-series aware cross-validation. Which two options should they configure in the CREATE MODEL statement? (Choose TWO)
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
✓
Specify a time_series_timestamp_col and time_series_data_col
For ARIMA+ models, you can set 'horizon' (forecast length) and 'data_frequency' (auto-detect or set). Cross-validation is not built-in for ARIMA; instead, you evaluate on held-out periods.
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.
- ✗
Set the data_frequency parameter to 'daily'
Why it's wrong here
While you can set it, BQML auto-detects frequency, so it's optional.
- ✗
Use the 'num_trials' parameter for hyperparameter tuning
Why it's wrong here
num_trials is for auto. But ARIMA+ does not require manual tuning.
- ✓
Specify a time_series_timestamp_col and time_series_data_col
Why this is correct
These are required columns for time-series.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Set the 'split_method' to 'time_series'
Why it's wrong here
BQML does not have a split_method for time-series; evaluation is done on hold-out.
- ✓
Set the model_type to 'ARIMA_PLUS'
Why this is correct
ARIMA_PLUS is the time-series model in BQML.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.
Detailed technical explanation
How to think about this question
This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.
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.
- Use explanations to understand the rule behind the answer.
TExam Day Tips
- Underline the problem statement mentally.
- 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 PDE exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
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Preparing and Using Data for Analysis — study guide chapter
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FAQ
Questions learners often ask
What does this PDE question test?
Preparing and Using Data for Analysis — This question tests Preparing and Using Data for Analysis — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Specify a time_series_timestamp_col and time_series_data_col — For ARIMA+ models, you can set 'horizon' (forecast length) and 'data_frequency' (auto-detect or set). Cross-validation is not built-in for ARIMA; instead, you evaluate on held-out periods.
What should I do if I get this PDE question wrong?
Identify which PDE exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jul 4, 2026
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