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NEW QUESTION: 1
An International Mobile Equipment Identifier (IMEI) is a 15-digit number that indicates the manufacturer, model type, and country of approval for GSM devices. The first eight digits of an IMEI number that provide information about the model and origin of the mobile device are also known as:
A. Type Allocation Code (TAC)
B. Manufacturer Identification Code (MIC)
C. Device Origin Code (DOC)
D. Integrated Circuit Code (ICC)
Answer: A

NEW QUESTION: 2
Refer to the exhibit.

The Cisco ASA is dropping all the traffic that is sourced from the internet and is destined to any security context inside interface. Which configuration should be verified on the Cisco ASA to solve this problem?
A. The Cisco ASA is using inside dynamic NAT on each security context.
B. The Cisco ASA is using a unique MAC address on each security context outside interface.
C. The Cisco ASA has NAT control disabled on each security context.
D. The Cisco ASA packet classifier is configured to use the outside physical interface to assign the packets to each security context.
E. The Cisco ASA is using a unique dynamic routing protocol process on each security context.
Answer: B
Explanation:
http://www.cisco.com/en/US/docs/security/asa/asa84/configuration/guide/mode_contexts.html#wp 1134937

NEW QUESTION: 3
ASP1이라는 Azure App Service 계획에서 실행되는 WebApp1이라는 Azure 웹앱이 있습니다. ASP1은 D1 가격 계층을 기반으로 합니다.
온-프레미스 네트워크의 컴퓨터에서만 WebApp1에 액세스할수 있어야 합니다. 솔루션은 비용을 최소화해야 합니다.
무엇을 구성해야 합니까? 답변하려면 답변 영역에서 적절한 옵션을 선택하십시오.
참고 : 각각의 올바른 선택은 1 포인트의 가치가 있습니다.

Answer:
Explanation:

Explanation

Box 1: B1
B1 (Basic) would minimize cost compared P1v2 (premium) and S1 (standard).
Box 2: Cross Origin Resource Sharing (CORS)
Once you set the CORS rules for the service, then a properly authenticated request made against the service from a different domain will be evaluated to determine whether it is allowed according to the rules you have specified.
Note: CORS (Cross Origin Resource Sharing) is an HTTP feature that enables a web application running under one domain to access resources in another domain. In order to reduce the possibility of cross-site scripting attacks, all modern web browsers implement a security restriction known as same-origin policy. This prevents a web page from calling APIs in a different domain. CORS provides a secure way to allow one origin (the origin domain) to call APIs in another origin.
References:
https://azure.microsoft.com/en-us/pricing/details/app-service/windows/
https://docs.microsoft.com/en-us/azure/cdn/cdn-cors

NEW QUESTION: 4
CSVファイルからテキストを前処理する予定です。 Azure Machine Learning Studioのデフォルトのストップワードリストをロードします。
次の要件を満たすように、テキストの前処理モジュールを構成する必要があります。
*単一の標準形式から複数の関連する単語を確認します。
*テキストからパイプ文字を削除します。
*情報検索を最適化するために単語を削除します。
どの3つのオプションを選択する必要がありますか？回答するには、回答エリアで適切なオプションを選択します。
注：それぞれの正しい選択には1ポイントの価値があります。

Answer:
Explanation:

Explanation:
Box 1: Remove stop words
Remove words to optimize information retrieval.
Remove stop words: Select this option if you want to apply a predefined stopword list to the text column. Stop word removal is performed before any other processes.
Box 2: Lemmatization
Ensure that multiple related words from a single canonical form.
Lemmatization converts multiple related words to a single canonical form Box 3: Remove special characters Remove special characters: Use this option to replace any non-alphanumeric special characters with the pipe | character.
References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/preprocess-text
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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