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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. Streaming
B. Batch
C. Cosine
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.

NEW QUESTION: 2
Das Projektteam folgt bei seiner Bereitstellung einem hybriden Rahmen. Während der Ausführung eines Projekts erfährt das Team von einer neuen Compliance-Anforderung, die vor allen anderen Anforderungen erfüllt werden muss. Wie sollte der Projektmanager mit dieser Situation umgehen?
A. Nehmen Sie den Compliance-Lead in die Stakeholder-Liste auf und warten Sie auf die nächste Statusbesprechung
B. Bitten Sie das Team, die Compliance-Anforderungen in den aktuellen Sprint aufzunehmen und zu liefern.
C. Arbeiten Sie mit dem Compliance-Teammitglied zusammen, um die Bereitstellung der Anforderung zu überprüfen und zu priorisieren
D. Fügen Sie die neue Compliance-Anforderung zum Backlog hinzu, da das technische Team keine Kapazität hat.
Answer: C

NEW QUESTION: 3
The variable attributes of SAS data sets ONE and TWO are shown below:
ONE TWO
# Variable Type Len Pos # Variable Type Len Pos
2 sales Num 8 8 2 budget Num 8 8
1 year Num 8 0 3 sales Char 8 16
1 year Num 8 0
Data set ONE contains 100 observations. Data set TWO contains 50 observations. Both data sets are sorted by the variable YEAR.
The following SAS program is submitted:
data three;
merge one two;
by year;
run;
Which one of the following is the result of the program execution?
A. Data set THREE is created with three variables and 100 observations.
B. No messages are written to the SAS log.
C. ERROR and WARNING messages are written to the SAS log.
D. Data set THREE is created with two variables and 50 observations.
Answer: C

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