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NEW QUESTION: 1
You are performing feature engineering on a dataset.
You must add a feature named CityName and populate the column value with the text London.
You need to add the new feature to the dataset.
Which Azure Machine Learning Studio module should you use?
A. Execute Python Script
B. Preprocess Text
C. Edit Metadata
D. Latent Dirichlet Allocation
Answer: C
Explanation:
Explanation/Reference:
Explanation:
Typical metadata changes might include marking columns as features.
References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/edit-metadata Testlet 1 Case study Overview You are a data scientist in a company that provides data science for professional sporting events. Models will use 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.
Assess 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
Media used for penalty event detection will be provided by consumer devices. Media may include
images and videos captured during the sporting event and shared 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 video transcripts or radio commentary, and logs from related social media feeds captured during the sporting events.
Crowd sentiment will include audio recordings submitted by event attendees in both mono and stereo
formats.
Penalty detection and sentiment
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.
Advertisements
During the initial weeks in production, the following was observed:
Ad response rated 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 uncorrelated features.
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 slow.
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 shows 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.
Ad response models must be trained at the beginning of each event and applied during the sporting
event.
Market segmentation models must optimize for similar ad response history.
Sampling must guarantee mutual and collective exclusively between 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.
Ad response models must support non-linear boundaries of features.
The ad propensity model uses a cut threshold is 0.45 and retrains occur if weighted Kappa deviated
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:
NEW QUESTION: 2
You have a five-node failover cluster named Cluster1. All the nodes run Windows Server 2012 R2. All the nodes run on physical hardware.
You need to upgrade Cluster1 to Windows Server 2016. The solution must minimize administrative effort and costs.
What should you do first?
A. Create a new Windows Server 2016 failover cluster
B. Drain and evict a node from Cluster1
C. Add a Windows Server 2016 node to Cluster1
D. Enable Cluster-Aware updating (CAU) on Cluster1
Answer: B
Explanation:
Explanation/Reference:
References:
https://docs.microsoft.com/en-us/windows-server/failover-clustering/upgrade-option-same-hardware
NEW QUESTION: 3
Organizational Units and Best Practices
How do you manage the organizational structure in SAP S/4HANA cloud?
A. The new SAP S/4HANA Cloud tenant comes with no organizational structure.The structure is generated upon client activation
B. The new SAP S/4HANA Cloud tenant comes with a basic organizational structure. You create new units as copies of the existing ones
C. The new SAP S/4HANA Cloud tenant comes with a best-practice organizational structure, The structure is fixed and it CANNOT be modified
D. The new SAP S/4HANA Cloud tenant comes with an organizational structure that includes all the possible organizational units, You can delete the organizational units you do NOT need
Answer: B
NEW QUESTION: 4
開発チームは、社内のサーバーレスアプリケーションとして新しいAPIをデプロイしています。チームは現在、AWSマネジメントコンソールを使用してAmazon API Gateway、AWS Lambda、およびAmazon DynamoDBリソースをプロビジョニングしています。 Solutions Architectは、これらのサーバーレスAPIの将来の展開を自動化することを任務としています。
どのようにこれを達成することができますか？
A. API Cloudをプロビジョニングするために、LambdaベースのカスタムリソースでAWS CloudFormationを使用します。
AWS :: DynamoDB :: TableリソースとAWS :: Lambda :: Functionリソースを使用して、Amazon DynamoDBテーブルとLambda関数を作成します。 CloudFormationテンプレートのデプロイを自動化するためのスクリプトを作成してください。
B. AWS Serverless Application Modelを使用してリソースを定義します。 YAMLテンプレートとアプリケーションファイルをコードリポジトリにアップロードします。 AWS CodePipelineを使用してコードリポジトリに接続し、AWS CodeBuildを使用して構築するためのアクションを作成します。 CodePipelineでAWS CloudFormationデプロイプロバイダーを使用してソリューションをデプロイします。
C. AWS CloudFormationを使用してサーバーレスアプリケーションを定義します。 Lambda関数にバージョン管理を実装し、バージョンを指すエイリアスを作成します。デプロイするときは、トラフィックを最新バージョンに移行するように重みを設定し、トラフィックが移動するにつれて徐々に重みを更新します。
D. アプリケーションコードをAWS CodeCommitコードリポジトリにコミットします。 AWS CodePipelineを使用してCodeCommitコードレポジトリに接続します。 AWS CodeDeployを使用してLambda関数を構築およびデプロイするには、AWS CodeBuildを使用します。 CodeDeployでデプロイメント設定タイプを指定して、トラフィックを徐々に新しいバージョンに切り替えます。
Answer: B