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NEW QUESTION: 1
A. 2つのターゲットグループでApplication Load Balancerを作成します。 Amazon API Gatewayプライベート統合用にApplication Load Balancerをセットアップします。 1つのターゲットグループを現在のバージョンに関連付け、もう1つのターゲットグループを新しいバージョンに関連付けます。着信トラフィックの10％を新しいバージョンにルーティングするようにAPI Gatewayを構成します。新しいバージョンが安定したら、すべてのトラフィックを新しいバージョンに送信し、ロードバランサーから古いバージョンを切り離すようにAPI Gatewayを構成します。
B. Amazon API Gatewayで、通常の展開の段階にカナリア設定を追加して、カナリアリリースの展開を作成します。着信トラフィックの10％をカナリアリリースにルーティングするようにAPI Gatewayを構成します。
C. 2つのターゲットグループを持つELB Network Load Balancerを作成します。 Amazon API Gatewayプライベート統合用のネットワークロードバランサーをセットアップする1つのターゲットグループを現在のバージョンに関連付け、もう1つのターゲットグループを新しいバージョンに関連付けます。着信トラフィックの10％を新しいバージョンにルーティングするようにロードバランサーを構成します。新しいバージョンが安定したら、ロードバランサーから古いバージョンをデタッチします。
D. 現在のバージョンと新しいバージョンの両方を指すAWS Lambda関数のエイリアスを作成します。着信トラフィックの10％を新しいバージョンにルーティングするようにエイリアスを構成します。新しいバージョンは安定していると見なされるため、エイリアスを更新してすべてのトラフィックを新しいバージョンにルーティングします。
E. 古いバージョンと新しいバージョンのAWS Lambdaエンドポイントを指すフェールオーバーレコードセットをAWS Route 53に作成します。着信トラフィックの10％を新しいバージョンにルーティングするようにRoute 53を構成します。新しいバージョンが安定したら、DNSレコードを更新して、すべてのトラフィックを新しいバージョンにルーティングします。
NEW QUESTION: 2
NEW QUESTION: 3
한 회사에서 직원이 개인 Amazon S3 버킷에 프로필 사진을 업로드 할 수 있는 웹 애플리케이션을 개발하고 있습니다. 직원이 로그인 할 때마다 표시되어야 하는 프로필 사진에는 크기 제한이 없습니다. 보안상의 이유로 사진은 불가능합니다. 공개적으로 액세스 할 수 있습니다.
이 시나리오에서 실행 가능한 장기 솔루션은 무엇입니까? ''
A. Amazon DynamoDB 테이블에 사진의 S3 키를 저장합니다. 직원이 로그인 할 때마다 미리 서명 된 URL을 생성하는 함수를 사용합니다. URL을 브라우저에 반환합니다.
B. Amazon DynamoDB 테이블에 사진의 S3 키 저장 직원이 로그인 한 후 사진을 다운로드 할 수 있도록 Amazon S3 VPC 엔드 포인트를 생성합니다.
C. base64를 사용하여 그림 인코딩 Amazon DynamoDB 테이블에 base64 문자열 저장 브라우저가 문자열을 검색하여 그림으로 변환하도록 허용
D. 사진 업로드시 미리 서명 된 URL 생성 Amazon DynamoDB 테이블에 URL 저장 직원이 로그인 할 때 브라우저에 URL 반환
NEW QUESTION: 4
Box 1: 0.859122
Box 2: a positively linear relationship
+1 indicates a strong positive linear relationship
-1 indicates a strong negative linear correlation
0 denotes no linear relationship between the two variables.
Topic 1, Case Study
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.
* 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.
* 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
* 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.
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.
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