PMLEGCP2025 최신 문제 업데이트

Google Professional Machine Learning Engineer

Cloud Pass는 GCP PMLE(Google Professional Machine Learning Engineer) 시험 대비를 위한 실전 문제 풀이 앱입니다.

최신 시험 경향을 반영한 실제처럼 구성된 문제, 정확한 정답, 명확한 개념 설명을 제공합니다. 인터넷에 떠도는 검증되지 않은 GCP PMLE 덤프 대신, Cloud Pass에서는 최근 출제 경향 기반의 연습 문제와 상세 해설을 통해 효율적으로 학습하고 점수를 끌어올릴 수 있습니다. 또한 Google Professional Machine Learning Engineer(PMLE)뿐만 아니라 AWS · GCP 자격증 24종을 한 앱에서 모두 학습할 수 있어 클라우드 자격증을 준비하는 수험생에게 최적화되어 있습니다.

📘335개의 문제를 앱에서 풀어보세요

⭐ 실제 사용자들의 PMLE 합격 후기

Cloud Pass로 합격한 사용자들의 생생한 경험

Passed my exam

2025-11-24

Just want to say a massive thank you to the entire Cloud pass, for helping me pass my exam first time. I wont lie, it wasn't easy, especially the way the real exam is worded, however the way practice questions teaches you why your option was wrong, really helps to frame your mind and helps you to understand what the question is asking for and the solutions your mind should be focusing on. Thanks once again.

C***************

학습 기간: 1 month

passed on first attempt

2025-11-23

Good questions banks and explanations that help me practise and pass the exam.

f****

학습 기간: 1 month

합격

2025-11-12

강의 듣고 바로 문제 풀었는데 정답률 80% 가량 나왔고, 높은 점수로 시험 합격했어요. 앱 잘 이용했습니다

민

민**

학습 기간: 1 month

Passed!

2025-11-11

Good mix of theory and practical scenarios

S************

학습 기간: 1 month

Helpful for refreshing ml fundamentals

2025-11-06

I used the app mainly to review the fundamentals—data preparation, model tuning, and deployment options on GCP. The explanations were simple and to the point, which really helped before the exam.

A***********

학습 기간: 1 month

Helped build confidence for the exam

2025-10-31

I wasn’t sure about my readiness at first, but doing a few sets of questions every day helped a lot. By exam day, I felt much more confident. Thanks

J************

학습 기간: 1 month

📝 335개의 시험 문제

2025 최신 업데이트된 문제들을 확인해보세요

Question 1

You deployed a TensorFlow recommendation model to a Vertex AI Prediction endpoint in us-central1 with autoscaling enabled. Over the last week, you observed sustained traffic of ~1,200 requests per hour (about 20 RPS) during business hours, which is 2x higher than your original estimate, and you need to keep P95 latency under 150 ms during future surges. You want the endpoint to scale efficiently to handle this higher baseline and upcoming spikes without causing user-visible latency. What should you do?

Question 2

You plan to fine-tune a video-frame classifier via transfer learning using a pre-trained ResNet-50 backbone. Your labeled dataset contains 18,000 1080p frames, and you will retrain the model once per day; each training run completes in under 60 minutes on 4 V100 GPUs, and you must minimize infrastructure cost and operational overhead. Which platform components and configuration should you choose?

Question 3

Your team is preparing to train a fraud detection model using data in BigQuery that includes several fields containing PII (for example, card_number, customer_email, and phone_number). The dataset has approximately 250 million rows and every column is required as a feature. Security requires that you reduce the sensitivity of PII before training while preserving each column’s format and length so downstream SQL joins and validations continue to work. The transformation must be deterministic so the same input always maps to the same protected value, and authorized teams must be able to decrypt values for audits. How should you proceed?

Question 4

Your team deployed a regression model that predicts hourly water usage for industrial chillers. Four months after launch, a vendor firmware update changed sensor sampling and units for three input features, and the live feature distributions diverged: 5 of 18 features now have a population stability index > 0.25, 27% of temperature readings fall outside the training range, and production RMSE increased from 0.62 to 1.45. How should you address the input differences in production?

Question 5

You are a data scientist at a city transportation agency tasked with forecasting hourly bike-share demand per station to optimize rebalancing. Your historical trips table in BigQuery contains 24 months of data (~22 million rows) with columns: timestamp, station_id, neighborhood, weather_condition (sunny/rainy/snow), special_event (boolean), and surge_pricing_flag (boolean). You need to choose the most effective combination of a BigQuery ML model and feature engineering to minimize RMSE while capturing weekly/seasonal patterns and handling multiple categorical variables; what should you do?