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NEW QUESTION: 1
You configure the multi-factor authentication status for three users as shown in the following table.

You create a group named Group1 and add Admin1, Admin2, and Admin3 to the group.
For all cloud apps, you create a conditional access policy that includes Group1. The policy requires multi-factor authentication.
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE:Each correct selection is worth one point.

Answer:
Explanation:

Explanation

Box 1: No
Disabled is the default state for a new user not enrolled in Azure MFA.
Box 2: Yes
Enforced: The user has been enrolled and has completed the registration process for Azure MFA.
Web browser apps require login in this case.
Box 3: Yes
Enabled: The user has been enrolled in Azure MFA, but has not registered. They receive a prompt to register the next time they sign in.
Web browser apps require login in this case.
References:
https://docs.microsoft.com/en-us/azure/active-directory/authentication/howto-mfa-userstates

NEW QUESTION: 2
You have a server named Server1 that runs Windows Server 2012. Server1 has the DNS Server server role installed.
You need to recommend changes to the DNS infrastructure to protect the cache from cache poisoning attacks.
What should you configure on Server1?
A. DNS cache locking
B. DNS devolution
C. DNS Security Extensions (DNSSEC)
D. The global query block list
Answer: A
Explanation:
Ache locking is a new feature available if your DNS server is running Windows Server 2008 R2. When you enable cache locking, the DNS server will not allow cached records to be overwritten for the duration of the time to live (TTL) value. Cache locking provides for enhanced security against cache poisoning attacks.

NEW QUESTION: 3
Your company's appraisal policy requires that the content of the performance document be added
in a collaborative manner.
Implementation requirements include:
-Both the worker and the manager are required to rate the worker.
-Conduct a review meeting for the worker, which the worker must acknowledge.
-Both the manager and the worker should be able to give their comments on the entire evaluation
process. -Workers and managers should start setting goals for the next period.
To meet these requirements, what are the correct tasks and their sequence to be designed in the process flow?
A. Set goals, manager evaluation of worker, share performance document, conform review meeting held, provide final feedback
B. Set next period goals, set goals, first approval, worker self-evaluation, manager evaluation of worker, confirm review meeting held, provide final feedback
C. Set goals, worker self-evaluation, manager evaluation of worker, confirm review meeting held, provide final feedback, set next period goals
D. Set goals, worker self-evaluation, manager evaluation of worker, share performance document, first approval, confirm review meeting held, provide final feedback
E. Set goals, worker self-evaluation, manager evaluation of worker, provide final feedback, confirm review meeting held, set next period goals
F. Set goals, first approval, manager evaluation of worker, worker self-evaluation, provide final feedback, second approval, set next period goals
Answer: D

NEW QUESTION: 4
ワークスペース内のデータと実験を操作するには、Azure Machine Learning SDKを使用する必要があります。
Python環境からワークスペースに接続するには、config.jsonファイルを構成する必要があります。
ワークスペースに接続するために、config.jsonファイルに追加する必要がある2つの追加パラメーターはどれですか。それぞれの正解がソリューションの一部を示しています。
注:それぞれの正しい選択は1ポイントの価値があります。
A. キー
B. resource_group
C. 地域
D. subscription_Id
E. ログイン
Answer: C,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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