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NEW QUESTION: 1
A firm has determined its cash-to-cash cycle time to be 60 days. The number of days' payables outstanding is 25, and number of days' sales outstanding is 35. If the firm reduces its inventory by 20%, the new cash-to-cash cycle time, in days, will be approximately:
A. 50.
B. 60.
C. 48.
D. 88.
Answer: A
NEW QUESTION: 2
Which of the following description of the DM12 board are correct? (Multiple choice)
A. Electrical signal input and output interfaces for DDN, Unframed E1 and sub-rate services
B. Accessing G.703 64 kbit/s co-directional service signals through the DB44 interface on the DM12 board
C. One DX1 board can be used with two DM12
D. Access the NX6bit/s and sub-rate service signals through the DB28 connector on the DM12 board.
Answer: B,C,D
NEW QUESTION: 3
회사에서 AWS에 배포 된 기존 워크로드에 대한 AWS Well-Architected Framework 검토를 수행하고 있습니다. 검토 결과 다른 AWS 서비스를 지원하기 위해 최근에 설치 한 Microsoft Active Directory 도메인 컨트롤러와 동일한 Amazon EC2 인스턴스에서 실행되는 공개 웹 사이트가 확인되었습니다. 솔루션 설계자는 아키텍처 보안을 개선하고 IT 직원에 대한 관리 요구를 최소화 할 수있는 새로운 디자인을 권장해야 합니다.
솔루션 아키텍트는 무엇을 추천해야 합니까?
A. AWS Directory Service를 사용하여 관리되는 Active Directory를 생성하십시오. 현재 EC2 인스턴스에서 Active Directory를 제거합니다.
B. AWS Directory Service를 사용하여 Active Directory 커넥터를 생성하십시오. 현재 EC2 인스턴스에서 실행중인 Active 도메인 컨트롤러에 대한 Active Directory 요청 프록시.
C. 현재 Active Directory 컨트롤러와 함께 SAML (Security Assertion Markup Language) 2.0 연합을 사용하여 AWS SSO (AWS Single Sign-On)를 활성화합니다. EC2 인스턴스의 보안 그룹을 수정하여 Active Directory에 대한 공개 액세스를 거부하십시오.
D. 동일한 서브넷에 다른 EC2 인스턴스를 생성하고 그에 Active Directory를 다시 설치하십시오. Active Directory를 제거하십시오.
Answer: A
NEW QUESTION: 4
You must use the Azure Machine Learning SDK to interact with data and experiments in the workspace.
You need to configure the config.json file to connect to the workspace from the Python environment.
Which two additional parameters must you add to the config.json file in order to connect to the workspace? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
A. resource_group
B. region
C. Key
D. subscription_Id
E. Login
Answer: B,D
Explanation:
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.
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