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NEW QUESTION: 1
Note: This question is part of a series of questions that present the same scenario. Each question
in the series contains a unique solution that might meet the stated goals. Some question sets
might have more than one correct solution, while others might not have a correct solution.
After you answer a question in this section, you will NOT be able to return to it. As a result, these
questions will not appear in the review screen.
You have a hybrid configuration of Azure Active Directory (AzureAD).
You have an Azure HDInsight cluster on a virtual network.
You plan to allow users to authenticate to the cluster by using their on-premises Active Directory
credentials.
You need to configure the environment to support the planned authentication.
Solution: You create a site-to-site VPN between the virtual network and the on-premises network.
Does this meet the goal?
A. Yes
B. No
Answer: A
Explanation:
Explanation/Reference:
Explanation:
You can connect HDInsight to your on-premises network by using Azure Virtual Networks and a VPN
gateway.
Note: To allow HDInsight and resources in the joined network to communicate by name, you must perform
the following actions:
Create Azure Virtual Network.

Create a custom DNS server in the Azure Virtual Network.

Configure the virtual network to use the custom DNS server instead of the default Azure Recursive

Resolver.
Configure forwarding between the custom DNS server and your on-premises DNS server.

References:
https://docs.microsoft.com/en-us/azure/hdinsight/connect-on-premises-network

NEW QUESTION: 2
管理者は、5つのCAT6ケーブルを新しいサーバーに実行する必要があります。 管理者は以下のステップのうちのどれが1番にするべきであるか？
A. ベルクロまたはジップタイでケーブルを束ねます。
B. ケーブルの両端に固有の識別子を付けます。
C. ケーブルの曲げ半径を確認します。
D. ケーブルをケーブルトレイにインストールします。
Answer: B

NEW QUESTION: 3

Based on the provided ASDM configuration for the remote ASA, which one of the following is correct?
A. The tunnel can also be established on TCP port 10000
B. A route to 192.168.22.0/24 will not be automatically installed in the routing table
C. The ASA will use a window of 128 packets (64x2) to perform the anti-replay check _
D. An access-list must be configured on the outside interface to permit inbound VPN traffic
Answer: C
Explanation:
Cisco IP security (IPsec) authentication provides anti-replay protection against an attacker duplicating encrypted packets by assigning a unique sequence number to each encrypted packet. The decryptor keeps track of which packets it has seen on the basis of these numbers. Currently, the default window size is 64 packets. Generally, this number (window size) is sufficient, but there are times when you may want to expand this window size. The IPsec Anti-Replay Window: Expanding and Disabling feature allows you to expand the window size, allowing the decryptor to keep track of more than 64 packets.

NEW QUESTION: 4
You need to implement a scaling strategy for the local penalty detection data.
Which normalization type should you use?
A. Cosine
B. Batch
C. Streaming
D. Weight
Answer: B
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.