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NEW QUESTION: 1
CSVファイルからテキストを前処理する予定です。 Azure Machine Learning Studioのデフォルトのストップワードリストをロードします。
次の要件を満たすように、テキストの前処理モジュールを構成する必要があります。
*単一の標準形式から複数の関連する単語を確認します。
*テキストからパイプ文字を削除します。
*情報検索を最適化するために単語を削除します。
どの3つのオプションを選択する必要がありますか？回答するには、回答エリアで適切なオプションを選択します。
注：それぞれの正しい選択には1ポイントの価値があります。

Answer:
Explanation:

Explanation:
Box 1: Remove stop words
Remove words to optimize information retrieval.
Remove stop words: Select this option if you want to apply a predefined stopword list to the text column. Stop word removal is performed before any other processes.
Box 2: Lemmatization
Ensure that multiple related words from a single canonical form.
Lemmatization converts multiple related words to a single canonical form Box 3: Remove special characters Remove special characters: Use this option to replace any non-alphanumeric special characters with the pipe | character.
References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/preprocess-text
Topic 1, Case Study
Overview
You are a data scientist in a company that provides data science for professional sporting events. Models will be global and local market data to meet the following business goals:
* Understand sentiment of mobile device users at sporting events based on audio from crowd reactions.
* Access a user's tendency to respond to an advertisement.
* Customize styles of ads served on mobile devices.
* Use video to detect penalty events.
Current environment
Requirements
* Media used for penalty event detection will be provided by consumer devices. Media may include images and videos captured during the sporting event and snared using social media. The images and videos will have varying sizes and formats.
* The data available for model building comprises of seven years of sporting event media. The sporting event media includes: recorded videos, transcripts of radio commentary, and logs from related social media feeds feeds captured during the sporting events.
* Crowd sentiment will include audio recordings submitted by event attendees in both mono and stereo Formats.
Advertisements
* Ad response models must be trained at the beginning of each event and applied during the sporting event.
* Market segmentation nxxlels must optimize for similar ad resporr.r history.
* Sampling must guarantee mutual and collective exclusivity local and global segmentation models that share the same features.
* Local market segmentation models will be applied before determining a user's propensity to respond to an advertisement.
* Data scientists must be able to detect model degradation and decay.
* Ad response models must support non linear boundaries features.
* The ad propensity model uses a cut threshold is 0.45 and retrains occur if weighted Kappa deviates from 0.1 +/-5%.
* The ad propensity model uses cost factors shown in the following diagram:

The ad propensity model uses proposed cost factors shown in the following diagram:

Performance curves of current and proposed cost factor scenarios are shown in the following diagram:

Penalty detection and sentiment
Findings
* Data scientists must build an intelligent solution by using multiple machine learning models for penalty event detection.
* Data scientists must build notebooks in a local environment using automatic feature engineering and model building in machine learning pipelines.
* Notebooks must be deployed to retrain by using Spark instances with dynamic worker allocation
* Notebooks must execute with the same code on new Spark instances to recode only the source of the data.
* Global penalty detection models must be trained by using dynamic runtime graph computation during training.
* Local penalty detection models must be written by using BrainScript.
* Experiments for local crowd sentiment models must combine local penalty detection data.
* Crowd sentiment models must identify known sounds such as cheers and known catch phrases. Individual crowd sentiment models will detect similar sounds.
* All shared features for local models are continuous variables.
* Shared features must use double precision. Subsequent layers must have aggregate running mean and standard deviation metrics Available.
segments
During the initial weeks in production, the following was observed:
* Ad response rates declined.
* Drops were not consistent across ad styles.
* The distribution of features across training and production data are not consistent.
Analysis shows that of the 100 numeric features on user location and behavior, the 47 features that come from location sources are being used as raw features. A suggested experiment to remedy the bias and variance issue is to engineer 10 linearly uncorrected features.
Penalty detection and sentiment
* Initial data discovery shows a wide range of densities of target states in training data used for crowd sentiment models.
* All penalty detection models show inference phases using a Stochastic Gradient Descent (SGD) are running too stow.
* Audio samples show that the length of a catch phrase varies between 25%-47%, depending on region.
* The performance of the global penalty detection models show lower variance but higher bias when comparing training and validation sets. Before implementing any feature changes, you must confirm the bias and variance using all training and validation cases.

NEW QUESTION: 2
会社のAWSアカウントは約300人のIAMユーザーで構成されています。現在、100人のIAMユーザーがS3に対して無制限の権限を持つためにはアクセスの変更が必要であるという義務がありますが、システム管理者として、個々のユーザーレベルでポリシーを適用する必要がないように、これを効果的に実装するにはどうすればよいですか？
選んでください：
A. 新しいロールを作成し、各ユーザーをIAMロールに追加します
B. ポリシーを作成し、JSONスクリプトを使用して複数のユーザーに適用します
C. 各ユーザーのAWSアカウントIDを含む無制限アクセスのS3バケットポリシーを作成します
D. IAMグループを使用し、ロールに基づいてユーザーを異なるグループに追加し、グループにポリシーを適用します
Answer: D
Explanation:
Explanation
Option A is incorrect since you don't add a user to the IAM Role
Option C is incorrect since you don't assign multiple users to a policy Option D is incorrect since this is not an ideal approach An IAM group is used to collectively manage users who need the same set of permissions. By having groups, it becomes easier to manage permissions. So if you change the permissions on the group scale, it will affect all the users in that group For more information on IAM Groups, just browse to the below URL:
https://docs.aws.amazon.com/IAM/latest/UserGuide/id_eroups.html
The correct answer is: Use the IAM groups and add users, based upon their role, to different groups and apply the policy to group Submit your Feedback/Queries to our Experts

NEW QUESTION: 3
アジャイルプロジェクトでは、計画が3つの異なる地平線で行われる手法は次のように知られています。
A. ポートフォリオ管理。
B. モンテカルロ分析。
C. プログレッシブエラボレーション。
D. DelphiTechnique。
Answer: C

NEW QUESTION: 4
You have built your own light-weight templating mechanism. Your servlets, which handle each request, dispatch the request to one of a small set of template JSP pages. Each template JSP controls the layout of the view by inserting the header, body, and footer elements into specific locations within the template page. The URLs for these three elements are stored in request-scoped variables called, headerURL, bodyURL, and footerURL, respectively. These attribute names are never used for other purposes. Which
JSP code snippet should be used in the template JSP to insert the JSP content for the body of the page?
A. <jsp:insert page='<%= bodyURL %>' />
B. <jsp:insert page='${bodyURL}' />
C. <jsp:insert file='${bodyURL}' />
D. <jsp:include page='<%= bodyURL %>' />
E. <jsp:include page='${bodyURL}' />
F. <jsp:include file='${bodyURL}' />
Answer: E