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NEW QUESTION: 1
Your customer's business outcome is to drive employee efficiencies. Which key metrics measure this outcome?
A. number of activities completed or increase in direct time
B. customer and employee feedback
C. reduction in headcount or operational support costs
D. increase in new subscribers or increase in end users
E. number of incidents reported or number of compliance issues
Answer: E

NEW QUESTION: 2
DRAG DROP
The Azure Queue service hosts a queue named userRegistrationQueue. You are developing a web job to process messages from the queue. You create a new console application by using Microsoft Visual Studio. You also create an Azure storage connection string and store the connection string in the application configuration file.
All trigger listeners and jobs must run on the current thread.
You need to ensure that the web job processes the messages from the queue.
How should you complete the relevant code? To answer, drag the appropriate code segments to the correct location or locations. Each code segment may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.

Answer:
Explanation:

NEW QUESTION: 3
ローカルペナルティ検出データのスケーリング戦略を実装する必要があります。
どの正規化タイプを使用する必要がありますか？
A. Cosine
B. Streaming
C. Weight
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: 4
A Company has a security event whereby an Amazon S3 bucket with sensitive information was made public. Company policy is to never have public S3 objects, and the Compliance team must be informed immediately when any public objects are identified.
How can the presence of a public S3 object be detected, set to trigger alarm notifications, and automatically remediated in the future? (Choose two.)
A. Configure an Amazon CloudWatch Events rule that invokes an AWS Lambda function to secure the S3 bucket.
B. Turn on object-level logging for Amazon S3. Configure a CloudWatch event to notify by using an SNS topic when a PutObject API call with public-read permission is detected in the AWS CloudTrail logs.
C. Turn on object-level logging for Amazon S3. Turn on Amazon S3 event notifications to notify by using an Amazon SNS topic when a PutObject API call is made with a public-read permission.
D. Use the S3 bucket permissions for AWS Trusted Advisor and configure a CloudWatch event to notify by using Amazon SNS.
E. Schedule a recursive Lambda function to regularly change all object permissions inside the S3 bucket.
https://aws.amazon.com/blogs/security/how-to-detect-and-automatically-remediate-unintended-permissions-in-amazon-s3-object-acls-with-cloudwatch-events/
Answer: A,B