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NEW QUESTION: 1
You work as an Office Assistant for Media Perfect Inc. You are creating a report in Microsoft Excel 2010. The report worksheet will include the sales made by all the sales managers in the month of January.
In the worksheet, you are required to place a pictorial representation displaying the percentage of
total sales made by each sales manager in January. Which of the following charts will you use to
accomplish the task?
A. Column
B. Pie
C. Line
D. Bar
Answer: B
Explanation:
In order to accomplish the task, you will have to insert a pie chart in the worksheet. Excel
provides a pie chart facility to display data that is arranged in one column or row on a
worksheet.
Pie charts display the size of items in one data series, proportional to the sum of the items.

NEW QUESTION: 2
Note: This question is a part of a series of questions that presents 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 Microsoft 365 subscription.
You have a distribution list named Finance.
You need to ensure that you can use Finance to create a team in Microsoft Teams.
Solution: From the Exchange admin center, you upgrade the distribution list.
Does this meet the goal?
A. No
B. Yes
Answer: B

NEW QUESTION: 3
Which codec complexity mode, when deployed on Cisco IOS routers with DSPs using the C5510 chipset, supports the most G.711 calls per DSP?
A. Secure
B. Low
C. Medium
D. High
E. Flex
Answer: E
Explanation:
The flex parameter allows the complexity to automatically adjust to either medium or high complexity depending on the needs of a call. For example, if a call uses the G.711 codec, the C5510 chipset automatically adjusts to the medium-complexity mode. However, if the call uses
G.729, the C5510 chipset uses the high complexity mode

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. 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.