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NEW QUESTION: 1
注：この質問は、同じシナリオを提示する一連の質問の一部です。連載の各質問には、記載されている目標を達成できる可能性のある固有の解決策が含まれています。他の人が正しい解決策を持っていないかもしれない間、いくつかの質問セットは複数の正しい解決策を持っているかもしれません。
このセクションで質問に答えた後は、それに戻ることはできません。その結果、これらの質問はレビュー画面に表示されません。
売上データを格納するデータウェアハウスがあります。 1つのファクトテーブルには1億行あります。
データウェアハウスに必要なストレージを減らす必要があります。
列ベースのストレージを使用し、運用上の作業負荷についてリアルタイムの分析を提供するソリューションを実装する必要があります。
解決方法：新しいインスタンスに新しい証明書を生成します。
解決策は目標を満たしていますか？
A. いいえ
B. はい
Answer: A
Explanation:
Explanation
Certificates are of no use in this scenario.
NEW QUESTION: 2
You need to implement a scaling strategy for the local penalty detection data.
Which normalization type should you use?
A. Weight
B. Streaming
C. Cosine
D. Batch
Answer: D
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: 3
次のユーザーを含むContoso.comという名前のAzure Active Directoryテナントがあります。
Contoso.comには、次のWindows 10デバイスが含まれています。
Contoso.comで次のセキュリティグループを作成します。
次の各文について、文が真の場合は「はい」を選択します。それ以外の場合は、[いいえ]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: Yes
User1 is a Cloud Device Administrator.
Device2 is Azure AD joined.
Group1 has the assigned to join type. User1 is the owner of Group1.
Note: Assigned groups - Manually add users or devices into a static group.
Azure AD joined or hybrid Azure AD joined devices utilize an organizational account in Azure AD Box 2: No User2 is a User Administrator.
Device1 is Azure AD registered.
Group1 has the assigned join type, and the owner is User1.
Note: Azure AD registered devices utilize an account managed by the end user, this account is either a Microsoft account or another locally managed credential.
Box 3: Yes
User2 is a User Administrator.
Device2 is Azure AD joined.
Group2 has the Dynamic Device join type, and the owner is User2.
References:
https://docs.microsoft.com/en-us/azure/active-directory/devices/overview
NEW QUESTION: 4
Answer:
Explanation:
Use the following answer for this simulation task.
Source IP Destination IP Port number TCP/UDP Allow/Deny
10.4.255.10/24 10.4.255.101 443 TCP Allow
10.4.255.10/23 10.4.255.2 22 TCP Allow
10.4.255.10/25 10.4.255.101 Any Any Allow
10.4.255.10/25 10.4.255.102 Any Any Allow
Explanation:
Firewall rules act like ACLs, and they are used to dictate what traffic can pass between the firewall and the internal network. Three possible actions can be taken based on the rule's criteria:
Block the connection
Allow the connection
Allow the connection only if it is secured
TCP is responsible for providing a reliable, one-to-one, connection-oriented session. TCP establishes a connection and ensures that the other end receives any packets sent. Two hosts communicate packet results with each other. TCP also ensures that packets are decoded and sequenced properly. This connection is persistent during the session. When the session ends, the connection is torn down.
UDP provides an unreliable connectionless communication method between hosts. UDP is considered a best-effort protocol, but it's considerably faster than TCP. The sessions don't establish a synchronized session like the kind used in
TCP, and UDP doesn't guarantee error-free communications. The primary purpose of UDP is to send small packets of information. The application is responsible for acknowledging the correct reception of the data.
Port 22 is used by both SSH and SCP with UDP.
Port 443 is used for secure web connections - HTTPS and is a TCP port.
Thus to make sure only the Accounting computer has HTTPS access to the Administrative server you should use TCP port 443 and set the rule to allow communication between 10.4.255.10/24 (Accounting) and 10.4.255.101
(Administrative server1)
Thus to make sure that only the HR computer has access to Server2 over SCP you need use of TCP port 22 and set the rule to allow communication between 10.4.255.10/23 (HR) and 10.4.255.2 (server2)
Thus to make sure that the IT computer can access both the Administrative servers you need to use a port and accompanying port number and set the rule to allow communication between:
10.4.255.10.25 (IT computer) and 10.4.255.101 (Administrative server1)
10.4.255.10.25 (IT computer) and 10.4.255.102 (Administrative server2)
References:
Dulaney, Emmett and Chuck Eastton, CompTIA Security+ Study Guide, 6th Edition, Sybex, Indianapolis, 2014, pp. 77,
83, 96, 157