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高品質のProfessional-Machine-Learning-Engineer準備ガイドを購入できるだけでなく、当社から大きな勇気と信頼を得ることもできます。多くのオンライン教育プラットフォームのリソースは、購入後に使用するためにユーザー登録によって提供される必要がありますが、それは当社のウェブサイトでは簡単です。 Professional-Machine-Learning-Engineerガイドトレントの無料デモを提供しています。登録せずにいつでもダウンロードできます。高速配信-支払い後、10分以内にProfessional-Machine-Learning-Engineer試験トレントを受信できるため、迅速かつ効率的に学習できます。 何を待っていますか？ Professional-Machine-Learning-Engineer試験問題を購入してください。

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	MLモデル予測のパフォーマンスとビジネス品質 継続的な評価指標の確立
トピック 2	ベースライン、より単純なモデル、および時間ディメンション全体に対するモデルのパフォーマンス モデル予測の結果の定義
トピック 3	大規模なデータパイプラインのバッチ処理とストリーミング 誤った結果の管理 非MLソリューションの特定
トピック 4	データ準備とモデルトレーニング 展開の自動化 モデルが失敗したと見なされる時期の決定
トピック 5	入力（機能）と予測される出力形式の定義 解釈可能性の要件を考慮したモデリング手法

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Professional-Machine-Learning-Engineer試験の準備方法｜最新のProfessional-Machine-Learning-Engineer学習資料試験｜高品質なGoogle Professional Machine Learning Engineer学習範囲

GoShikenのProfessional-Machine-Learning-Engineer資料を言及するたびに、多くの人の反応は高い出題率です。Google認証に参加する人が不安の状態から平静になって、試験に順調に合格しました。新しい資料がないなら、努力だけが不足です。Professional-Machine-Learning-Engineer試験に合格したいなら、我々の全面的な資料を参考として試験を準備しましょう。

Google Professional Machine Learning Engineer 認定 Professional-Machine-Learning-Engineer 試験問題 (Q92-Q97):

質問 # 92
A data scientist uses an Amazon SageMaker notebook instance to conduct data exploration and analysis. This requires certain Python packages that are not natively available on Amazon SageMaker to be installed on the notebook instance.
How can a machine learning specialist ensure that required packages are automatically available on the notebook instance for the data scientist to use?

• A. Create a Jupyter notebook file (.ipynb) with cells containing the package installation commands to execute and place the file under the /etc/init directory of each Amazon SageMaker notebook instance.
• B. Use the conda package manager from within the Jupyter notebook console to apply the necessary conda packages to the default kernel of the notebook.
• C. Install AWS Systems Manager Agent on the underlying Amazon EC2 instance and use Systems Manager Automation to execute the package installation commands.
• D. Create an Amazon SageMaker lifecycle configuration with package installation commands and assign the lifecycle configuration to the notebook instance.

正解：A 解説：
Explanation
Explanation/Reference: https://towardsdatascience.com/automating-aws-sagemaker-notebooks-2dec62bc2c84
質問 # 93
A financial services company is building a robust serverless data lake on Amazon S3. The data lake should be flexible and meet the following requirements:
* Support querying old and new data on Amazon S3 through Amazon Athena and Amazon Redshift Spectrum.
* Support event-driven ETL pipelines
* Provide a quick and easy way to understand metadata
Which approach meets these requirements?

• A. Use an AWS Glue crawler to crawl S3 data, an AWS Lambda function to trigger an AWS Glue ETL job, and an AWS Glue Data catalog to search and discover metadata.
• B. Use an AWS Glue crawler to crawl S3 data, an Amazon CloudWatch alarm to trigger an AWS Glue ETL job, and an external Apache Hive metastore to search and discover metadata.
• C. Use an AWS Glue crawler to crawl S3 data, an AWS Lambda function to trigger an AWS Batch job, and an external Apache Hive metastore to search and discover metadata.
• D. Use an AWS Glue crawler to crawl S3 data, an Amazon CloudWatch alarm to trigger an AWS Batch job, and an AWS Glue Data Catalog to search and discover metadata.

正解：A
質問 # 94
You are developing an ML model to predict house prices. While preparing the data, you discover that an important predictor variable, distance from the closest school, is often missing and does not have high variance. Every instance (row) in your data is important. How should you handle the missing data?

• A. Apply feature crossing with another column that does not have missing values.
• B. Replace the missing values with zeros.
• C. Delete the rows that have missing values.
• D. Predict the missing values using linear regression.

正解：D
質問 # 95
Your task is classify if a company logo is present on an image. You found out that 96% of a data does not include a logo. You are dealing with data imbalance problem. Which metric do you use to evaluate to model?

• A. F1 Score
• B. F Score with higher recall weighted than precision
• C. F Score with higher precision weighting than recall
• D. RMSE

正解：B
質問 # 96
Your team is working on an NLP research project to predict political affiliation of authors based on articles they have written. You have a large training dataset that is structured like this:

You followed the standard 80%-10%-10% data distribution across the training, testing, and evaluation subsets. How should you distribute the training examples across the train-test-eval subsets while maintaining the 80-10-10 proportion?
A)

B)

C)

D)

• A. Option D
• B. Option B
• C. Option C
• D. Option A

正解：B 解説：
If we just put inside the Training set , Validation set and Test set , randomly Text, Paragraph or sentences the model will have the ability to learn specific qualities about The Author's use of language beyond just his own articles. Therefore the model will mixed up different opinions. Rather if we divided things up a the author level, so that given authors were only on the training data, or only in the test data or only in the validation data. The model will find more difficult to get a high accuracy on the test validation (What is correct and have more sense!). Because it will need to really focus in author by author articles rather than get a single political affiliation based on a bunch of mixed articles from different authors. https://developers.google.com/machine-learning/crash-course/18th-century-literature For example, suppose you are training a model with purchase data from a number of stores. You know, however, that the model will be used primarily to make predictions for stores that are not in the training data. To ensure that the model can generalize to unseen stores, you should segregate your data sets by stores. In other words, your test set should include only stores different from the evaluation set, and the evaluation set should include only stores different from the training set. https://cloud.google.com/automl-tables/docs/prepare#ml-use
質問 # 97
...... 今競争の激しいIT業界で地位を固めたいですが、Google Professional-Machine-Learning-Engineer認証試験に合格しなければなりません。IT業界ではさらに強くなるために強い専門知識が必要です。Google Professional-Machine-Learning-Engineer認証試験に合格することが簡単ではなくて、Google Professional-Machine-Learning-Engineer証明書は君にとってはIT業界に入るの一つの手づるになるかもしれません。しかし必ずしも大量の時間とエネルギーで復習しなくて、弊社が丹精にできあがった問題集を使って、試験なんて問題ではありません。 Professional-Machine-Learning-Engineer学習範囲: https://www.goshiken.com/Google/Professional-Machine-Learning-Engineer-mondaishu.html