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NEW QUESTION: 1
You need to implement a scaling strategy for the local penalty detection data.
Which normalization type should you use?
A. Batch
B. Cosine
C. Streaming
D. Weight
Answer: A
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: 2
Please wait while the virtual machine loads. Once loaded, you may proceed to the lab section. This may take a few minutes, and the wait time will not be deducted from your overall test time.
When the Next button is available, click it to access the lab section. In this section, you will perform a set of tasks in a live environment. While most functionality will be available to you as it would be in a live environment, some functionality (e.g., copy and paste, ability to navigate to external websites) will not be possible by design.
Scoring is based on the outcome of performing the tasks stated in the lab. In other words, it doesn't matter how you accomplish the task, if you successfully perform it, you will earn credit for that task.
Labs are not timed separately, and this exam may more than one lab that you must complete. You can use as much time as you would like to complete each lab. But, you should manage your time appropriately to ensure that you are able to complete the lab(s) and all other sections of the exam in the time provided.
Please note that once you submit your work by clicking the Next button within a lab, you will NOT be able to return to the lab.
Username and password

Use the following login credentials as needed:
To enter your username, place your cursor in the Sign in box and click on the username below.
To enter your password, place your cursor in the Enter password box and click on the password below.
Microsoft 365 Username:
admin@[email protected]
Microsoft 365 Password: #HSP.ug?$p6un
If the Microsoft 365 portal does not load successfully in the browser, press CTRL-K to reload the portal in a new browser tab.
The following information is for technical support only:
Lab instance: 11122308









You need to ensure that a user named Allan Deyoung uses multi-factor authentication (MFA) for all authentication requests.
To complete this task, sign in to the Microsoft 365 admin center.
Answer:
Explanation:
See explanation below.
Explanation
1. Open the Admin Center and go to Users > Active Users
2. Open Multi-factor authentication
Don't select any user yet, just open the Multi-factor authentication screen. You will find the button in the toolbar.

3. Open the Service settingsBefore we start enabling MFA for the users, we first go through the service settings. The button to the settings screen doesn't stand out, but it's just below the title

4. Setup MFA Office 365
A few settings are important here:
* Make sure you check the App password. Otherwise, users can't authenticate in some applications (like the default mail app in Android).
* Also, take a look at the remember function. By default, it is set to 14 days.

5. Enable MFA for Office 365 users
After you have set the settings to your liking click on save and then on users (just below the title Multi-factor authentication).
You see the list of your users again. Here you can select single or multiple users to enable MFA.
At the moment you enable Office 365 MFA for a user it can get the setup screen as soon as the users browse to one of the Office 365 products.

Reference:
https://lazyadmin.nl/office-365/how-to-setup-mfa-in-office-365/

NEW QUESTION: 3
Sie haben eine SQL-Datenbank, die eine Tabelle mit dem Namen Products enthält.
Sie implementieren eine gespeicherte Prozedur, die die Liste der Produkte abruft, eine benutzerdefinierte Geschäftslogik ausführt und dann die Liste der Produkte erneut abruft.
Die benutzerdefinierte Geschäftslogik in der gespeicherten Prozedur ändert keine Daten aus der Tabelle "Products".
Die gespeicherte Prozedur enthält Folgendes:

Sie müssen Zeile 01 der gespeicherten Prozedur vervollständigen, um sicherzustellen, dass die aus der SELECT * FROM Products-Anweisung in Zeile 05 gelesenen Daten mit den aus der SELECT * FROM Products-Anweisung in Zeile 10 gelesenen Daten identisch sind. Die Lösung muss die Parallelität maximieren.
Ein Teil des richtigen Transact-SQL wurde im Antwortbereich unten bereitgestellt. Geben Sie den Code in den Antwortbereich ein, der das Problem löst und die angegebenen Ziele oder Anforderungen erfüllt. Sie können Code sowohl innerhalb als auch unterhalb des bereitgestellten Codes hinzufügen.


Verwenden Sie die Schaltfläche Syntax überprüfen, um Ihre Arbeit zu überprüfen. Alle Syntax- oder Rechtschreibfehler werden nach Zeilen- und Zeichenposition gemeldet.
Answer:
Explanation:
WIEDERHOLBARES LESEN
Erläuterung
Fügen Sie REPEATABLE READ in Zeile 1 ein, um Folgendes zu erhalten:
SET TRANSACTIONISOLATION LEVEL REPEATABLE READ;
REPEATABLE READ gibt an, dass Anweisungen keine Daten lesen können, die geändert, aber noch nicht von anderen Transaktionen festgeschrieben wurden, und dass keine anderen Transaktionen Daten ändern können, die von der aktuellen Transaktion gelesen wurden, bis die aktuelle Transaktion abgeschlossen ist.

NEW QUESTION: 4
IS 감사인이 조직의 가상 네트워크 구현을 평가하고 있습니다. 다음 중 가장 중대한 위험으로 간주되는 관찰은 무엇입니까?
A. 가상 네트워크를 통한 통신 성능은 모니터링되지 않습니다.
B. 물리적 및 가상 네트워크 구성은 동일한 팀에서 관리하지 않습니다.
C. 가상 네트워크 장치가 복제되어 오프라인 모드로 저장됩니다.
D. 가상 네트워크를 통한 트래픽은 보안 보호 장치에 표시되지 않습니다.
Answer: D