Google Professional-Machine-Learning-Engineer 問題集

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。

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。

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。

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 認定試験の出題範囲：

参照：https://cloud.google.com/certification/guides/machine-learning-engineer

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

問題 #1
You are building a linear regression model on BigQuery ML to predict a customer's likelihood of purchasing your company's products. Your model uses a city name variable as a key predictive component. In order to train and serve the model, your data must be organized in columns. You want to prepare your data using the least amount of coding while maintaining the predictable variables. What should you do?

A. Use Dataprep to transform the state column using a one-hot encoding method, and make each city a column with binary values.
B. Create a new view with BigQuery that does not include a column with city information
C. Use TensorFlow to create a categorical variable with a vocabulary list. Create the vocabulary file, and upload it as part of your model to BigQuery ML.
D. Use Cloud Data Fusion to assign each city to a region labeled as 1, 2, 3, 4, or 5, and then use that number to represent the city in the model.

問題 #2
You are an ML engineer at a large retail company. You have a BigQuery table containing five years of daily sales data for 50,000 distinct product SKUs. You need to generate a daily demand forecast for the next 30 days for each individual SKU. The solution must automatically account for holidays and weekly seasonality. You want to minimize data egress costs and operational complexity by keeping the data and model within the data warehouse. What should you do?

A. Use the gcloud ai custom-jobs create command to run a PyTorch LSTM container on Agent Platform that pulls data from a CSV into Cloud Storage.
B. Use the google-cloud-aiplatform SDK to create a TimeSeriesDataset and trigger an AutoMLForecastingTrainingJob.
C. Use the CREATE MODEL... OPTIONS(model_type='LINEAR_REG') statement in BigQuery to predict sales using the transaction date as a feature.
D. Use the CREATE MODEL... OPTIONS(model_type='ARIMA_PLUS') statement in BigQuery to generate a forecast based on historical SKU sales.

問題 #3
Your team is building an application for a global bank that will be used by millions of customers.
You built a forecasting model that predicts customers' account balances 3 days in the future.
Your team will use the results in a new feature that will notify users when their account balance is likely to drop below $25. How should you serve your predictions?

A. 1. Build a notification system on Firebase.
2. Register each user with a user ID on the Firebase Cloud Messaging server, which sends a notification when your model predicts that a user's account balance will drop below the $25 threshold.
B. 1. Create a Pub/Sub topic for each user.
2. Deploy an application on the App Engine standard environment that sends a notification when your model predicts that a user's account balance will drop below the $25 threshold.
C. 1. Create a Pub/Sub topic for each user.
2. Deploy a Cloud Function that sends a notification when your model predicts that a user's account balance will drop below the $25 threshold.
D. 1. Build a notification system on Firebase.
2. Register each user with a user ID on the Firebase Cloud Messaging server, which sends a notification when the average of all account balance predictions drops below the $25 threshold.

問題 #4
You have been given a dataset with sales predictions based on your company's marketing activities. The data is structured and stored in BigQuery, and has been carefully managed by a team of data analysts. You need to prepare a report providing insights into the predictive capabilities of the data. You were asked to run several ML models with different levels of sophistication, including simple models and multilayered neural networks. You only have a few hours to gather the results of your experiments. Which Google Cloud tools should you use to complete this task in the most efficient and self-serviced way?

A. Use Vertex AI Workbench user-managed notebooks with scikit-learn code for a variety of ML algorithms and performance metrics.
B. Use BigQuery ML to run several regression models, and analyze their performance.
C. Train a custom TensorFlow model with Vertex AI, reading the data from BigQuery featuring a variety of ML algorithms.
D. Read the data from BigQuery using Dataproc, and run several models using SparkML.

問題 #5
You work for a food product company. Your company's historical sales data is stored in BigQuery.You need to use Vertex AI's custom training service to train multiple TensorFlow models that read the data from BigQuery and predict future sales. You plan to implement a data preprocessing algorithm that performs mm-max scaling and bucketing on a large number of features before you start experimenting with the models. You want to minimize preprocessing time, cost, and development effort. How should you configure this workflow?

A. Add the transformations as a preprocessing layer in the TensorFlow models.
B. Write SQL queries to transform the data in-place in BigQuery.
C. Create a Dataflow pipeline that uses the BigQuerylO connector to ingest the data, process it, and write it back to BigQuery.
D. Write the transformations into Spark that uses the spark-bigquery-connector, and use Dataproc to preprocess the data.

解説：