Google Professional-Machine-Learning-Engineer 問題集

弊社は無料Google Professional-Machine-Learning-Engineerサンプルを提供します

お客様は問題集を購入する時、問題集の質量を心配するかもしれませんが、我々はこのことを解決するために、お客様に無料Professional-Machine-Learning-Engineerサンプルを提供いたします。そうすると、お客様は購入する前にサンプルをダウンロードしてやってみることができます。君はこのProfessional-Machine-Learning-Engineer問題集は自分に適するかどうか判断して購入を決めることができます。

Professional-Machine-Learning-Engineer試験ツール：あなたの訓練に便利をもたらすために、あなたは自分のペースによって複数のパソコンで設置できます。

弊社のGoogle Professional-Machine-Learning-Engineerを利用すれば試験に合格できます

弊社のGoogle Professional-Machine-Learning-Engineerは専門家たちが長年の経験を通して最新のシラバスに従って研究し出した勉強資料です。弊社はProfessional-Machine-Learning-Engineer問題集の質問と答えが間違いないのを保証いたします。

この問題集は過去のデータから分析して作成されて、カバー率が高くて、受験者としてのあなたを助けて時間とお金を節約して試験に合格する通過率を高めます。我々の問題集は的中率が高くて、100％の合格率を保証します。我々の高質量のGoogle Professional-Machine-Learning-Engineerを利用すれば、君は一回で試験に合格できます。

弊社は失敗したら全額で返金することを承諾します

我々は弊社のProfessional-Machine-Learning-Engineer問題集に自信を持っていますから、試験に失敗したら返金する承諾をします。我々のGoogle Professional-Machine-Learning-Engineerを利用して君は試験に合格できると信じています。もし試験に失敗したら、我々は君の支払ったお金を君に全額で返して、君の試験の失敗する経済損失を減少します。

一年間の無料更新サービスを提供します

君が弊社のGoogle Professional-Machine-Learning-Engineerをご購入になってから、我々の承諾する一年間の更新サービスが無料で得られています。弊社の専門家たちは毎日更新状態を検査していますから、この一年間、更新されたら、弊社は更新されたGoogle Professional-Machine-Learning-Engineerをお客様のメールアドレスにお送りいたします。だから、お客様はいつもタイムリーに更新の通知を受けることができます。我々は購入した一年間でお客様がずっと最新版のGoogle Professional-Machine-Learning-Engineerを持っていることを保証します。

安全的な支払方式を利用しています

Credit Cardは今まで全世界の一番安全の支払方式です。少数の手続きの費用かかる必要がありますとはいえ、保障があります。お客様の利益を保障するために、弊社のProfessional-Machine-Learning-Engineer問題集は全部Credit Cardで支払われることができます。

領収書について：社名入りの領収書が必要な場合、メールで社名に記入していただき送信してください。弊社はPDF版の領収書を提供いたします。

TopExamは君にProfessional-Machine-Learning-Engineerの問題集を提供して、あなたの試験への復習にヘルプを提供して、君に難しい専門知識を楽に勉強させます。TopExamは君の試験への合格を期待しています。

Google Professional Machine Learning Engineer 認定 Professional-Machine-Learning-Engineer 試験問題:

1. You work at a mobile gaming startup that creates online multiplayer games Recently, your company observed an increase in players cheating in the games, leading to a loss of revenue and a poor user experience. You built a binary classification model to determine whether a player cheated after a completed game session, and then send a message to other downstream systems to ban the player that cheated Your model has performed well during testing, and you now need to deploy the model to production You want your serving solution to provide immediate classifications after a completed game session to avoid further loss of revenue. What should you do?

A) Import the model into Vertex Al Model Registry. Use the Vertex Batch Prediction service to run batch inference jobs.
B) Import the model into Vertex Al Model Registry Create a Vertex Al endpoint that hosts the model and make online inference requests.
C) Save the model files in a VM Load the model files each time there is a prediction request and run an inference job on the VM.
D) Save the model files in a Cloud Storage Bucket Create a Cloud Function to read the model files and make online inference requests on the Cloud Function.

2. You work for a credit card company and have been asked to create a custom fraud detection model based on historical data using AutoML Tables. You need to prioritize detection of fraudulent transactions while minimizing false positives. Which optimization objective should you use when training the model?

A) An optimization objective that maximizes the area under the precision-recall curve (AUC PR) value
B) An optimization objective that minimizes Log loss
C) An optimization objective that maximizes the area under the receiver operating characteristic curve (AUC ROC) value
D) An optimization objective that maximizes the Precision at a Recall value of 0.50

3. You work for an international manufacturing organization that ships scientific products all over the world Instruction manuals for these products need to be translated to 15 different languages Your organization's leadership team wants to start using machine learning to reduce the cost of manual human translations and increase translation speed. You need to implement a scalable solution that maximizes accuracy and minimizes operational overhead. You also want to include a process to evaluate and fix incorrect translations. What should you do?

A) Create a Vertex Al pipeline that processes the documents1 launches an AutoML Translation training job evaluates the translations, and deploys the model to a Vertex Al endpoint with autoscaling and model monitoring When there is a predetermined skew between training and live data re-trigger the pipeline with the latest data.
B) Use Vertex Al custom training jobs to fine-tune a state-of-the-art open source pretrained model with your data Deploy the model to a Vertex Al endpoint with autoscaling and model monitoring When there is a predetermined skew between the training and live data, configure a trigger to run another training job with the latest data.
C) Use AutoML Translation to tram a model Configure a Translation Hub project and use the trained model to translate the documents Use human reviewers to evaluate the incorrect translations
D) Create a workflow using Cloud Function Triggers Configure a Cloud Function that is triggered when documents are uploaded to an input Cloud Storage bucket Configure another Cloud Function that translates the documents using the Cloud Translation API and saves the translations to an output Cloud Storage bucket Use human reviewers to evaluate the incorrect translations.

4. You work for a retail company. You have been asked to develop a model to predict whether a customer will purchase a product on a given day. Your team has processed the company's sales data, and created a table with the following rows:
* Customer_id
* Product_id
* Date
* Days_since_last_purchase (measured in days)
* Average_purchase_frequency (measured in 1/days)
* Purchase (binary class, if customer purchased product on the Date)
You need to interpret your models results for each individual prediction. What should you do?

A) Create a BigQuery table Use BigQuery ML to build a logistic regression classification model Use the values of the coefficients of the model to interpret the feature importance with higher values corresponding to more importance.
B) Create a Vertex Al tabular dataset Train an AutoML model to predict customer purchases Deploy the model to a Vertex Al endpoint. At each prediction enable L1 regularization to detect non-informative features.
C) Create a Vertex Al tabular dataset Train an AutoML model to predict customer purchases Deploy the model to a Vertex Al endpoint and enable feature attributions Use the "explain" method to get feature attribution values for each individual prediction.
D) Create a BigQuery table Use BigQuery ML to build a boosted tree classifier Inspect the partition rules of the trees to understand how each prediction flows through the trees.

5. You are developing an image recognition model using PyTorch based on ResNet50 architecture Your code is working fine on your local laptop on a small subsample. Your full dataset has 200k labeled images You want to quickly scale your training workload while minimizing cost. You plan to use 4 V100 GPUs What should you do?

A) Create a Vertex Al Workbench user-managed notebooks instance with 4 V100 GPUs, and use it to tram your model.
B) Create a Google Kubernetes Engine cluster with a node pool that has 4 V100 GPUs Prepare and submit a TFJob operator to this node pool.
C) Configure a Compute Engine VM with all the dependencies that launches the training Tram your model with Vertex Al using a custom tier that contains the required GPUs.
D) Package your code with Setuptools and use a pre-built container. Train your model with Vertex Al using a custom tier that contains the required GPUs.

質問と回答：