Google Professional-Machine-Learning-Engineer최신시험예상문제모음 & Professional-Machine-Learning-Engineer시험대비최신버전덤프자료

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Google Professional Machine Learning Engineer 인증 시험은 기계 학습 엔지니어링 분야 전문가의 기술과 지식을 인증하는 인증 프로그램입니다. 이 인증은 Google Cloud에서 제공되며, Google Cloud 기술을 사용하여 기계 학습 모델을 구축하고 배포하는 경험이 있는 개인을 대상으로 합니다. 이 인증 시험은 후보자의 비즈니스 목표를 충족하는 기계 학습 솔루션을 설계, 구축 및 운영할 수 있는 능력을 평가합니다.

>> Google Professional-Machine-Learning-Engineer최신 시험 예상문제모음 <<

최신 Google Cloud Certified Professional-Machine-Learning-Engineer 무료샘플문제 (Q39-Q44):

질문 # 39
You work for a bank and are building a random forest model for fraud detection. You have a dataset that includes transactions, of which 1% are identified as fraudulent.
Which data transformation strategy would likely improve the performance of your classifier?

A. Write your data in TFRecords.
B. Oversample the fraudulent transaction 10 times.
C. Use one-hot encoding on all categorical features.
D. Z-normalize all the numeric features.

정답：B

질문 # 40
You are a data scientist at an industrial equipment manufacturing company. You are developing a regression model to estimate the power consumption in the company's manufacturing plants based on sensor data collected from all of the plants. The sensors collect tens of millions of records every day. You need to schedule daily training runs for your model that use all the data collected up to the current date. You want your model to scale smoothly and require minimal development work. What should you do?

A. Train a regression model using AutoML Tables.
B. Develop a custom TensorFlow regression model, and optimize it using Vertex AI Training.
C. Develop a regression model using BigQuery ML.
D. Develop a custom scikit-learn regression model, and optimize it using Vertex AI Training.

정답：A

질문 # 41
You are an ML engineer at a mobile gaming company. A data scientist on your team recently trained a TensorFlow model, and you are responsible for deploying this model into a mobile application. You discover that the inference latency of the current model doesn't meet production requirements. You need to reduce the inference time by 50%, and you are willing to accept a small decrease in model accuracy in order to reach the latency requirement. Without training a new model, which model optimization technique for reducing latency should you try first?

A. Weight pruning
B. Model distillation
C. Dimensionality reduction
D. Dynamic range quantization

정답：B

질문 # 42
You work for the AI team of an automobile company, and you are developing a visual defect detection model using TensorFlow and Keras. To improve your model performance, you want to incorporate some image augmentation functions such as translation, cropping, and contrast tweaking. You randomly apply these functions to each training batch. You want to optimize your data processing pipeline for run time and compute resources utilization. What should you do?

A. Use Dataflow to create the augmentations dynamically per training run, and stage them as TFRecords.
B. Embed the augmentation functions dynamically as part of Keras generators.
C. Use Dataflow to create all possible augmentations, and store them as TFRecords.
D. Embed the augmentation functions dynamically in the tf.Data pipeline.

정답：C

질문 # 43
A Data Scientist wants to gain real-time insights into a data stream of GZIP files.
Which solution would allow the use of SQL to query the stream with the LEAST latency?

A. AWS Glue with a custom ETL script to transform the data.
B. An Amazon Kinesis Client Library to transform the data and save it to an Amazon ES cluster.
C. Amazon Kinesis Data Firehose to transform the data and put it into an Amazon S3 bucket.
D. Amazon Kinesis Data Analytics with an AWS Lambda function to transform the data.

정답：D

설명：
Explanation/Reference: https://aws.amazon.com/big-data/real-time-analytics-featured-partners/

질문 # 44
......

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