Question 274 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 healthcare organization wants to build a model to predict patient readmission risk using structured electronic health record (EHR) data. They need to train a model using SQL in BigQuery, but they also want to leverage AutoML's ability to automatically search for the best architecture. Which approach should they take?
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
✓
Use BigQuery ML with the AUTO model type
BigQuery ML's AUTOML_CLASSIFIER model type automatically performs architecture search and hyperparameter tuning, making it ideal for users who want to leverage AutoML capabilities directly within SQL on structured EHR data. This approach avoids manual model selection while staying entirely within BigQuery's SQL interface, which is the stated requirement.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse AutoML Tables (a separate Vertex AI service) with BigQuery ML's built-in AUTO model type, assuming they must export data to use AutoML, when in fact BigQuery ML provides AutoML capabilities directly within SQL.
Detailed technical explanation
How to think about this question
BigQuery ML's AUTO model type leverages Google's AutoML technology under the hood, automatically evaluating multiple model architectures (e.g., linear, deep neural networks, gradient-boosted trees) and tuning hyperparameters via Bayesian optimization. This is particularly useful for healthcare organizations with limited ML expertise, as it handles feature engineering, model selection, and validation splits automatically while keeping all data within BigQuery's security and compliance boundaries.
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.
Quick reference
Common DNS Record Types
| Record | Purpose | Example |
|---|---|---|
| A | IPv4 address mapping | example.com → 93.184.216.34 |
| AAAA | IPv6 address mapping | example.com → 2606:2800::1 |
| CNAME | Alias to another hostname | www → example.com |
| MX | Mail server for domain | example.com → mail.example.com (priority 10) |
| TXT | Text data (SPF, DKIM, verification) | v=spf1 include:_spf.example.com ~all |
| NS | Authoritative name servers | example.com NS ns1.example.com |
| PTR | Reverse DNS (IP → hostname) | 34.216.184.93.in-addr.arpa → example.com |
| SOA | Zone authority record | Primary NS, admin email, serial, TTL defaults |
What to study next
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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: Use BigQuery ML with the AUTO model type — BigQuery ML's AUTOML_CLASSIFIER model type automatically performs architecture search and hyperparameter tuning, making it ideal for users who want to leverage AutoML capabilities directly within SQL on structured EHR data. This approach avoids manual model selection while staying entirely within BigQuery's SQL interface, which is the stated requirement.
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 →
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Last reviewed: Jul 4, 2026
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