- A
Call ML.PREDICT on a BigQuery table that is updated every hour
Why wrong: ML.PREDICT is not designed for low-latency real-time serving; it is a batch operation.
- B
Use a BigQuery materialized view that refreshes every minute and apply ML.PREDICT
Why wrong: Materialized views cannot call ML.PREDICT directly; also not real-time.
- C
Stream data into Pub/Sub and use a Dataflow pipeline with Apache Beam's model inference
Why wrong: This adds complexity and cost; Dataflow streaming may be overkill for simple fraud detection.
- D
Export the model to a Cloud Storage bucket and deploy it to AI Platform Prediction
Exporting to AI Platform Prediction provides low-latency serving with autoscaling, cost-effective for hourly retraining.
Question 236 of 1,000
PDE BigQuery ML export Practice Question
This PDE practice question tests your understanding of preparing and using data for analysis. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. A key principle to apply: bigQuery ML export. 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.
You are building a real-time fraud detection system using BigQuery streaming and a BQML logistic regression model. The model must be retrained every hour with new labeled data. What is the MOST cost-effective approach to serve predictions with low latency?
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
✓
Export the model to a Cloud Storage bucket and deploy it to AI Platform Prediction
Exporting the model to Cloud Storage and deploying to AI Platform Prediction is the most cost-effective approach because AI Platform Prediction provides managed, autoscaling prediction serving with pay-per-prediction pricing. It avoids the cost and latency of repeatedly querying BigQuery with ML.PREDICT, which consumes slots and is not designed for real-time serving. Option C (Dataflow with model inference) incurs streaming pipeline costs, while options A and B are inefficient due to repeated BigQuery queries or unsupported materialized views with ML.PREDICT.
Key principle: BigQuery ML export
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Call ML.PREDICT on a BigQuery table that is updated every hour
Why it's wrong here
ML.PREDICT is not designed for low-latency real-time serving; it is a batch operation.
- ✗
Use a BigQuery materialized view that refreshes every minute and apply ML.PREDICT
Why it's wrong here
Materialized views cannot call ML.PREDICT directly; also not real-time.
- ✗
Stream data into Pub/Sub and use a Dataflow pipeline with Apache Beam's model inference
Why it's wrong here
This adds complexity and cost; Dataflow streaming may be overkill for simple fraud detection.
- ✓
Export the model to a Cloud Storage bucket and deploy it to AI Platform Prediction
Why this is correct
Exporting to AI Platform Prediction provides low-latency serving with autoscaling, cost-effective for hourly retraining.
Related concept
BigQuery ML export
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
Treat this as a scenario question. Identify the problem, the constraint, and the best action. Then compare each option against those facts.
KKey Concepts to Remember
- BigQuery ML export
- AI Platform Prediction
- Cost-effective serving
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
BigQuery ML export
Real-world example
How this comes up in practice
A startup's cloud architect reviews their monthly bill and notices costs are higher than expected for a long-running batch job. Switching from on-demand instances to Reserved Instances — or using Spot/Preemptible VMs — can reduce compute costs by up to 72 %. Questions like this test whether you understand the tradeoffs between commitment, flexibility, and cost across cloud pricing models.
What to study next
Got this wrong? Here's your next step.
Review bigQuery ML export, then practise related PDE questions on the same topic to reinforce the concept.
- →
Preparing and Using Data for Analysis — study guide chapter
Learn the concepts, then practise the questions
- →
Preparing and Using Data for Analysis practice questions
Targeted practice on this topic area only
- →
All PDE questions
1,000 questions across all exam domains
- →
Google Professional Data Engineer study guide
Full concept coverage aligned to exam objectives
- →
PDE practice test guide
How to use practice tests most effectively before exam day
Related practice questions
Related PDE practice-question pages
Use these pages to review the topic behind this question. This is how one missed question becomes focused revision.
Designing Data Processing Systems practice questions
Practise PDE questions linked to Designing Data Processing Systems.
Ingesting and Processing the Data practice questions
Practise PDE questions linked to Ingesting and Processing the Data.
Storing the Data practice questions
Practise PDE questions linked to Storing the Data.
Preparing and Using Data for Analysis practice questions
Practise PDE questions linked to Preparing and Using Data for Analysis.
Maintaining and Automating Data Workloads practice questions
Practise PDE questions linked to Maintaining and Automating Data Workloads.
Building and operationalizing data processing systems practice questions
Practise PDE questions linked to Building and operationalizing data processing systems.
Operationalizing machine learning models practice questions
Practise PDE questions linked to Operationalizing machine learning models.
Ensuring solution quality practice questions
Practise PDE questions linked to Ensuring solution quality.
PDE fundamentals practice questions
Practise PDE questions linked to PDE fundamentals.
PDE scenario practice questions
Practise PDE questions linked to PDE scenario.
PDE troubleshooting practice questions
Practise PDE questions linked to PDE troubleshooting.
Practice this exam
Start a free PDE 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 PDE question test?
Preparing and Using Data for Analysis — This question tests Preparing and Using Data for Analysis — BigQuery ML export.
What is the correct answer to this question?
The correct answer is: Export the model to a Cloud Storage bucket and deploy it to AI Platform Prediction — Exporting the model to Cloud Storage and deploying to AI Platform Prediction is the most cost-effective approach because AI Platform Prediction provides managed, autoscaling prediction serving with pay-per-prediction pricing. It avoids the cost and latency of repeatedly querying BigQuery with ML.PREDICT, which consumes slots and is not designed for real-time serving. Option C (Dataflow with model inference) incurs streaming pipeline costs, while options A and B are inefficient due to repeated BigQuery queries or unsupported materialized views with ML.PREDICT.
What should I do if I get this PDE question wrong?
Review bigQuery ML export, then practise related PDE questions on the same topic to reinforce the concept.
What is the key concept behind this question?
BigQuery ML export
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 →
Keep practising
More PDE practice questions
- A company wants to process large CSV files stored in Cloud Storage and load them into BigQuery. The files are generated…
- A company runs a Dataflow streaming pipeline that reads from Cloud Pub/Sub and writes to BigQuery. The pipeline uses a s…
- A company uses Cloud Dataproc for ephemeral clusters to run batch jobs. They want to ensure job reliability and data qua…
- Your company uses Vertex AI Pipelines to automate model retraining. The pipeline has three steps: data extraction from B…
- A company wants to use BigQuery to query data stored in Parquet files in Cloud Storage without loading the data into Big…
- A company has deployed a machine learning model to AI Platform Prediction. The model uses a custom container with a Tens…
Last reviewed: Jul 4, 2026
This PDE 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 PDE 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.