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NEW QUESTION: 1
IIA 지침에 따르면, 다음 중 최종 감사 보고서와 관련하여 최고 감사 책임자의 책임을 정확하게 설명하는 것은 무엇입니까?
1. 참여 후 회의를 조정하여 경영진과 최종 감사 보고서를 논의합니다.
2. 최종 감사 보고서에 경영진의 답변을 포함시킵니다.
3. 최종 감사 보고서를 검토하고 승인하십시오.
최종 감사 보고서를받을 사람을 결정합니다.
A. 3과 4
B. 2와 3
C. 1과 4
D. 1과 2
Answer: A

NEW QUESTION: 2
A company is developing a mobile app for field service employees using Azure App Service Mobile Apps as the backend.
The company's network connectivity varies throughout the day. The solution must support offline use and synchronize changes in the background when the app is online app.
You need to implement the solution.
How should you complete the code segment? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:
Explanation:

Explanation

Box 1: var todoTable = client GetSyncTable<TodoItem>()
To setup offline access, when connecting to your mobile service, use the method GetSyncTable instead of GetTable (example):
IMobileServiceSyncTable todoTable = App.MobileService.GetSyncTable(); / Box 2: await todoTable.PullAsync("allTodoItems",todo.Table.CreateQuery()); Your app should now use IMobileServiceSyncTable (instead of IMobileServiceTable) for CRUD operations.
This will save changes to the local database and also keep a log of the changes. When the app is ready to synchronize its changes with the Mobile Service, use the methods PushAsync and PullAsync (example):
await App.MobileService.SyncContext.PushAsync();
await todoTable.PullAsync();
References:
https://azure.microsoft.com/es-es/blog/offline-sync-for-mobile-services/

NEW QUESTION: 3
You are moving a large dataset from Azure Machine Learning Studio to a Weka environment.
You need to format the data for the Weka environment.
Which module should you use?
A. Convert to SVMLight
B. Convert to CSV
C. Convert to Dataset
D. Convert to ARFF
Answer: D
Explanation:
Explanation/Reference:
Explanation:
Use the Convert to ARFF module in Azure Machine Learning Studio, to convert datasets and results in Azure Machine Learning to the attribute-relation file format used by the Weka toolset. This format is known as ARFF.
The ARFF data specification for Weka supports multiple machine learning tasks, including data preprocessing, classification, and feature selection. In this format, data is organized by entites and their attributes, and is contained in a single text file.
References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/convert-to-arff Testlet 1 Case study Overview You are a data scientist in a company that provides data science for professional sporting events. Models will use 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.

Assess 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
Media used for penalty event detection will be provided by consumer devices. Media may include

images and videos captured during the sporting event and shared 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 video transcripts or radio commentary, and logs from related social media feeds captured during the sporting events.
Crowd sentiment will include audio recordings submitted by event attendees in both mono and stereo

formats.
Penalty detection and sentiment
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.
Advertisements
During the initial weeks in production, the following was observed:
Ad response rated 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 uncorrelated features.
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 slow.
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 shows 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.
Ad response models must be trained at the beginning of each event and applied during the sporting

event.
Market segmentation models must optimize for similar ad response history.

Sampling must guarantee mutual and collective exclusively between 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.
Ad response models must support non-linear boundaries of features.

The ad propensity model uses a cut threshold is 0.45 and retrains occur if weighted Kappa deviated

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:

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