Question 931 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 company needs to build a custom model to classify images of products into categories. They have a large labeled dataset. They want to use AutoML but are unsure which options support image classification. Which TWO AutoML products support image classification?
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
✓
AutoML Vision
AutoML Vision (Option C) is the correct choice because it is specifically designed for image classification tasks, allowing users to train custom models on labeled image datasets without writing code. It supports features like object detection and image segmentation, making it ideal for the described use case.
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
Cisco often tests the distinction between AutoML services by focusing on the data modality (text, image, video, tabular), leading candidates to mistakenly choose AutoML Video for image tasks due to its visual nature, but it is strictly for video sequences, not static images.
Detailed technical explanation
How to think about this question
AutoML Vision leverages transfer learning with pre-trained convolutional neural networks (CNNs) such as EfficientNet, fine-tuning them on the user's labeled dataset. Under the hood, it automatically handles data augmentation, hyperparameter tuning, and model architecture selection to optimize for accuracy and latency. In a real-world scenario, a retail company could use AutoML Vision to classify product images into categories like 'electronics' or 'clothing' without needing deep learning expertise.
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: AutoML Vision — AutoML Vision (Option C) is the correct choice because it is specifically designed for image classification tasks, allowing users to train custom models on labeled image datasets without writing code. It supports features like object detection and image segmentation, making it ideal for the described use case.
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.