- A
The analyst must call ML.TRAIN after CREATE MODEL to start training
Why wrong: ML.TRAIN is not a valid function; training happens in CREATE MODEL.
- B
The trained model is stored in Cloud Storage
Why wrong: The model is stored in BigQuery, not Cloud Storage.
- C
The model must be exported to Vertex AI for prediction
Why wrong: Predictions can be done within BigQuery using ML.PREDICT.
- D
The model is automatically evaluated on a held-out test set if data splitting is enabled
By default, BigQuery ML splits data into training and evaluation sets.
- E
Training is performed using the CREATE MODEL statement
BigQuery ML uses CREATE MODEL to train models.
Question 971 of 1,000
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 data analyst wants to use BigQuery ML to train a linear regression model (LINEAR_REG) to predict house prices. They have a table with features like square footage, number of bedrooms, and location. Which TWO statements about the training process are correct?
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
✓
The model is automatically evaluated on a held-out test set if data splitting is enabled
Option D is correct because when data splitting is enabled in BigQuery ML, the `CREATE MODEL` statement automatically reserves a portion of the input data as a held-out test set. After training completes, BigQuery ML evaluates the model on this test set and reports metrics like mean absolute error and R², without requiring any manual split or separate evaluation step.
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.
- ✗
The analyst must call ML.TRAIN after CREATE MODEL to start training
Why it's wrong here
ML.TRAIN is not a valid function; training happens in CREATE MODEL.
- ✗
The trained model is stored in Cloud Storage
Why it's wrong here
The model is stored in BigQuery, not Cloud Storage.
- ✗
The model must be exported to Vertex AI for prediction
Why it's wrong here
Predictions can be done within BigQuery using ML.PREDICT.
- ✓
The model is automatically evaluated on a held-out test set if data splitting is enabled
Why this is correct
By default, BigQuery ML splits data into training and evaluation sets.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Training is performed using the CREATE MODEL statement
Why this is correct
BigQuery ML uses CREATE MODEL to train models.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the misconception that BigQuery ML requires an explicit training command (like `ML.TRAIN`) or that models are stored in Cloud Storage by default, when in fact training is fully encapsulated in `CREATE MODEL` and models reside in BigQuery's internal storage.
Detailed technical explanation
How to think about this question
Under the hood, BigQuery ML uses the `CREATE MODEL` DDL statement to trigger a distributed training job on the BigQuery compute infrastructure. The `OPTIONS(model_type='LINEAR_REG', data_split_method='RANDOM')` parameter controls how the data is split; by default, 20% of the data is held out for evaluation. The model metadata, including weights and evaluation metrics, is stored in the `INFORMATION_SCHEMA.MODELS` view, not as a file in Cloud Storage.
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 media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
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 — study guide chapter
Learn the concepts, then practise the questions
- →
Architecting Low-Code ML Solutions practice questions
Targeted practice on this topic area only
- →
All PMLE questions
1,000 questions across all exam domains
- →
Google Professional Machine Learning Engineer study guide
Full concept coverage aligned to exam objectives
- →
PMLE practice test guide
How to use practice tests most effectively before exam day
Related practice questions
Related PMLE practice-question pages
Use these pages to review the topic behind this question. This is how one missed question becomes focused revision.
Automating and Orchestrating ML Pipelines practice questions
Practise PMLE questions linked to Automating and Orchestrating ML Pipelines.
Collaborating Within and Across Teams to Manage Data and Models practice questions
Practise PMLE questions linked to Collaborating Within and Across Teams to Manage Data and Models.
Serving and Scaling Models practice questions
Practise PMLE questions linked to Serving and Scaling Models.
Monitoring ML Solutions practice questions
Practise PMLE questions linked to Monitoring ML Solutions.
Architecting Low-Code ML Solutions practice questions
Practise PMLE questions linked to Architecting Low-Code ML Solutions.
Scaling Prototypes into ML Models practice questions
Practise PMLE questions linked to Scaling Prototypes into ML Models.
Collaborating to manage data and models practice questions
Practise PMLE questions linked to Collaborating to manage data and models.
Solving business challenges with ML practice questions
Practise PMLE questions linked to Solving business challenges with ML.
PMLE fundamentals practice questions
Practise PMLE questions linked to PMLE fundamentals.
PMLE scenario practice questions
Practise PMLE questions linked to PMLE scenario.
PMLE troubleshooting practice questions
Practise PMLE questions linked to PMLE troubleshooting.
Practice this exam
Start a free PMLE practice session
Short sessions build daily habit. Longer sessions build exam-day stamina. Try a timed session to simulate real conditions.
FAQ
Questions learners often ask
What does this PMLE question test?
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: The model is automatically evaluated on a held-out test set if data splitting is enabled — Option D is correct because when data splitting is enabled in BigQuery ML, the `CREATE MODEL` statement automatically reserves a portion of the input data as a held-out test set. After training completes, BigQuery ML evaluates the model on this test set and reports metrics like mean absolute error and R², without requiring any manual split or separate evaluation step.
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.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
Last reviewed: Jul 4, 2026
This PMLE practice question is part of Courseiva's free Google Cloud certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the PMLE exam.
Question Discussion
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
Sign in to join the discussion.