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Google Professional-Machine-Learning-Engineer最新知識 & Professional-Machine-Learning-Engineer練習問題集

GoogleのProfessional-Machine-Learning-Engineer認定試験はIT職員が欠くことができない認証です。IT職員のキャリアと関連しますから。 GoogleのProfessional-Machine-Learning-Engineer試験トレーニング資料は受験生の皆さんが必要とした勉強資料です。Jpexamのトレーニング資料は受験生が一番ほしい唯一なトレーニング資料です。JpexamのGoogleのProfessional-Machine-Learning-Engineer試験トレーニング資料を手に入れたら、試験に合格することができるようになります。

Google Professional-Machine-Learning-Engineer 認定試験の出題範囲：

トピック出題範囲トピック 1

Organization and tracking experiments and pipeline runs
Hooking models into existing CI
CD deployment system

トピック 2

Performance and business quality of ML model predictions
Establishing continuous evaluation metrics

トピック 3

Choose appropriate Google Cloud software components
Assessing and communicating business impact

トピック 4

Design architecture that complies with regulatory and security concerns
Define business success criteria

トピック 5

Optimization and simplification of input pipeline for training
Aligning with Google AI principles and practices

トピック 6

Automation of data preparation and model training
deployment
Determination of when a model is deemed unsuccessful

トピック 7

Training a model as a job in different environments
Constructing and testing of parameterized pipeline definition in SDK

トピック 8

Model performance against baselines, simpler models, and across the time dimension
Defining outcome of model predictions

Google Professional Machine Learning Engineer 認定 Professional-Machine-Learning-Engineer 試験問題 (Q144-Q149):

質問 # 144
You need to design a customized deep neural network in Keras that will predict customer purchases based on their purchase history. You want to explore model performance using multiple model architectures, store training data, and be able to compare the evaluation metrics in the same dashboard. What should you do?

A. Run multiple training jobs on Al Platform with similar job names
B. Automate multiple training runs using Cloud Composer
C. Create multiple models using AutoML Tables
D. Create an experiment in Kubeflow Pipelines to organize multiple runs

正解：D

解説：
https://www.kubeflow.org/docs/components/pipelines/concepts/experiment/ https://www.kubeflow.org/docs/components/pipelines/concepts/run/

質問 # 145
You are developing ML models with Al Platform for image segmentation on CT scans. You frequently update your model architectures based on the newest available research papers, and have to rerun training on the same dataset to benchmark their performance. You want to minimize computation costs and manual intervention while having version control for your code. What should you do?

A. Create an automated workflow in Cloud Composer that runs daily and looks for changes in code in Cloud Storage using a sensor.
B. Use Cloud Build linked with Cloud Source Repositories to trigger retraining when new code is pushed to the repository
C. Use Cloud Functions to identify changes to your code in Cloud Storage and trigger a retraining job
D. Use the gcloud command-line tool to submit training jobs on Al Platform when you update your code

正解：D

質問 # 146
You work for a company that provides an anti-spam service that flags and hides spam posts on social media platforms. Your company currently uses a list of 200,000 keywords to identify suspected spam posts. If a post contains more than a few of these keywords, the post is identified as spam. You want to start using machine learning to flag spam posts for human review. What is the main advantage of implementing machine learning for this business case?

A. New problematic phrases can be identified in spam posts.
B. A much longer keyword list can be used to flag spam posts.
C. Spam posts can be flagged using far fewer keywords.
D. Posts can be compared to the keyword list much more quickly.

正解：D

質問 # 147
You need to train a computer vision model that predicts the type of government ID present in a given image using a GPU-powered virtual machine on Compute Engine. You use the following parameters:
* Optimizer: SGD
* Image shape = 224x224
* Batch size = 64
* Epochs = 10
* Verbose = 2
During training you encounter the following error: ResourceExhaustedError: out of Memory (oom) when allocating tensor. What should you do?

A. Reduce the image shape
B. Reduce the batch size
C. Change the optimizer
D. Change the learning rate

正解：B

解説：
Reference:
https://stackoverflow.com/questions/59394947/how-to-fix-resourceexhaustederror-oom-when-allocating-tensor/59395251#:~:text=OOM%20stands%20for%20%22out%20of,in%20your%20Dense%20%2C%20Conv2D%20layers

質問 # 148
A Machine Learning Specialist at a company sensitive to security is preparing a dataset for model training. The dataset is stored in Amazon S3 and contains Personally Identifiable Information (PII).
The dataset:
* Must be accessible from a VPC only.
* Must not traverse the public internet.
How can these requirements be satisfied?

A. Create a VPC endpoint and apply a bucket access policy that allows access from the given VPC endpoint and an Amazon EC2 instance.
B. Create a VPC endpoint and use security groups to restrict access to the given VPC endpoint and an Amazon EC2 instance
C. Create a VPC endpoint and use Network Access Control Lists (NACLs) to allow traffic between only the given VPC endpoint and an Amazon EC2 instance.
D. Create a VPC endpoint and apply a bucket access policy that restricts access to the given VPC endpoint and the VPC.

正解：D

質問 # 149
......

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