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NEW QUESTION: 1
You need to implement a scaling strategy for the local penalty detection data.
Which normalization type should you use?
A. Batch
B. Weight
C. Cosine
D. Streaming
Answer: A
Explanation:
Post batch normalization statistics (PBN) is the Microsoft Cognitive Toolkit (CNTK) version of how to evaluate the population mean and variance of Batch Normalization which could be used in inference Original Paper.
In CNTK, custom networks are defined using the BrainScriptNetworkBuilder and described in the CNTK network description language "BrainScript." Scenario:
Local penalty detection models must be written by using BrainScript.
References:
https://docs.microsoft.com/en-us/cognitive-toolkit/post-batch-normalization-statistics
Topic 1, One 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
The BEST methods for a web developer to prevent the website application code from being vulnerable to cross-site request forgery (XSRF) are to: (Select TWO).
A. validate and filter input on the server side and client side.
B. restrict and sanitize use of special characters in input and URLs.
C. permit redirection to Internet-facing web URLs.
D. ensure all HTML tags are enclosed in angle brackets, e.g., "<" and ">".
E. use a web proxy to pass website requests between the user and the application.
Answer: A,B

NEW QUESTION: 3
귀사에서 대량의 항공 이미지 데이터를 S3에 업로드했습니다. 과거에는 온-프레미스 환경에서 전용 서버 그룹을 사용하여이 데이터를 일괄 처리하고 오픈 소스 메시징 시스템 인 RabbitMQ를 사용하여 작업 정보를 서버로 가져 왔습니다. 일단 처리되면 데이터는 테이프로 이동하여 오프 사이트로 배송됩니다. 관리자는 현재 설계를 유지하고 AWS 보관 스토리지 및 메시징 서비스를 활용하여 비용을 최소화하라고 지시했습니다.
어느 것이 맞습니까?
A. SQS에서 메시지를 처리하기 위해 스팟 인스턴스를 사용하는 대기열 깊이에 의해 트리거되는 자동 확장 작업자 설정.
데이터가 처리되면 S3 객체의 스토리지 클래스를 Reduced Redundancy Storage로 변경하십시오.
B. 작업 메시지 전달에 SQS를 사용하고 EC2 작업자 인스턴스가 유휴 상태가 되면 CloudWatch 경보를 사용하여 EC2 작업자 인스턴스를 종료합니다.
데이터가 처리되면 S3 객체의 스토리지 클래스를 Reduced Redundancy Storage로 변경하십시오.
C. SNS를 사용하여 작업 메시지를 전달하고 CloudWatch 경보를 사용하여 스팟 작업자 인스턴스가 유휴 상태가 되면 종료합니다.
데이터가 처리되면 S3 객체의 스토리지 클래스를 Glacier로 변경하십시오.
D. SQS에서 메시지를 처리하기 위해 스팟 인스턴스를 사용하는 대기열 깊이에 의해 트리거되는 자동 확장 작업자 설정.
데이터가 처리되면 S3 객체의 스토리지 클래스를 Glacier로 변경하십시오.
Answer: D
Explanation:
설명:
중점을 두어야 할 중요한 부분은 "AWS 아카이브 스토리지 및 메시징 서비스를 활용하여 비용을 최소화하는 것"입니다. 답변에서 가장 저렴한 비용의 스토리지는 Glacier이므로, 메시징 비용이 SQS보다 적으며 SNS가 100 만 건을 초과하면 무료입니다. 위의 두 가지 기준을 만족하는 유일한 답변은 답변 C입니다.
또한 메시징 속도가 급박 한 것 같지 않으므로 SQS는 이러한 요구를 충족시킵니다. 보다 실시간 전달 메커니즘 인 SNS