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NEW QUESTION: 1
注：この質問は、同じシナリオを提示する一連の質問の一部です。シリーズの各質問には、指定された目標を達成する可能性のある独自のソリューションが含まれています。一部の質問セットには複数の正しい解決策がある場合がありますが、他の質問セットには正しい解決策がない場合があります。
このセクションの質問に回答した後は、その質問に戻ることはできません。その結果、これらの質問はレビュー画面に表示されません。
Adatumという名前のAzure Active Directory（Azure AD）テナントとSubscription1という名前のAzureサブスクリプションがあります。 Adatumには、Developersという名前のグループが含まれています。 Subscription1には、Devという名前のリソースグループが含まれています。
開発者グループに、DevリソースグループでAzureロジックアプリを作成する機能を提供する必要があります。
解決策：開発では、ロジックアプリの共同作成者の役割を開発者グループに割り当てます。
これは目標を達成していますか？
A. はい
B. いいえ
Answer: A
Explanation:
Explanation
The Logic App Contributor role lets you manage logic app, but not access to them. It provides access to view, edit, and update a logic app.
References:
https://docs.microsoft.com/en-us/azure/role-based-access-control/built-in-roles
https://docs.microsoft.com/en-us/azure/logic-apps/logic-apps-securing-a-logic-app

NEW QUESTION: 2
Cisco UCS Manager、Cisco Intersight APIについて正しいのは次のうちどれですか。 （2つ選択してください。）
A. Cisco Intersight APIインタラクションはXMLまたはJSONでエンコードでき、認証のためにHTTPヘッダーにAPIキーが必要です。
B. UCSマネージャーAPIの相互作用はXMLエンコードされており、認証方法にCookieが必要です。
C. UCS Managerは、JSONを使用してAPIインタラクションをエンコードし、認証にHTTPヘッダーでBase64エンコードされた資格情報を利用します。
D. USC Director APIの相互作用はXMLまたはJSONでエンコードでき、認証のためにHTTPヘッダーにAPLキーが必要です。
E. Cisco IntersightはXMLを使用してAPI相互作用をエンコードし、認証にはAPIキーペアを必要とします。
Answer: B,D

NEW QUESTION: 3
Azure Machine Learning Studioを使用して、バイナリ分類モデルを作成します。
モデルのパラメータースイープを実行して、ハイパーパラメーターを調整する必要があります。パラメータスイープは、次の要件を満たしている必要があります。
*ハイパーパラメーターの可能なすべての組み合わせを反復する
*スイープの実行に必要なコンピューティングリソースを最小限に抑える
*モデルのパラメータースイープを実行する必要があります。
どのパラメータースイープモードを使用する必要がありますか？
A. ランダムスイープ
B. スイープクラスタリング
C. ランダムグリッド
D. ランダムシード
E. グリッド全体
Answer: C
Explanation:
Explanation
Maximum number of runs on random grid: This option also controls the number of iterations over a random sampling of parameter values, but the values are not generated randomly from the specified range; instead, a matrix is created of all possible combinations of parameter values and a random sampling is taken over the matrix. This method is more efficient and less prone to regional oversampling or undersampling.
If you are training a model that supports an integrated parameter sweep, you can also set a range of seed values to use and iterate over the random seeds as well. This is optional, but can be useful for avoiding bias introduced by seed selection.
Topic 2, Case Study 1
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: 4
A client reports that someone is in the room and trying to kill him. The nurse's best response is:
A. "Just tell the person to go away."
B. "No one is in your room. Let's get you more medicine."
C. "I do not see anyone, but you seem to be very frightened."
D. "No one can hurt you here."
Answer: C
Explanation:
Explanation/Reference:
Explanation:
It is important to acknowledges the client's fear. The other responses deny the client's perceptions.
Psychosocial Integrity