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NEW QUESTION: 1
Answer:
Explanation:
Explanation:
HQ office server - AlwaysOn primary replica;
Satellite office server - AlwaysOn secondary replica;
Cloud server - file backup server
NEW QUESTION: 2
After obtaining an understanding of the entity and its environment, including its internal control, an auditor
decided to perform tests of controls. This is likely because:
A. An increase in the assessed level of control risk is justified for certain financial statement assertions.
B. Evidence to support a reduction in control risk is not available.
C. There were many internal control weaknesses that could allow errors to enter the accounting system.
D. The auditor's risk assessment is based on the effective operation of controls.
Answer: D
Explanation:
Explanation/Reference:
Explanation:
Choice "A" is correct. After obtaining an understanding the entity and its environment, including its internal
control, the auditor may make a risk assessment that assumes controls are operating effectively. In such
cases, the auditor performs tests of controls to obtain evidence supporting this assessment.
Choice "B" is incorrect. If evidence to support a reduction in control risk is not available, tests of controls
would, by definition, not be possible.
Choice "C" is incorrect. When auditors decide to perform tests of controls, they have made a preliminary
assessment that controls are operating effectively. Accordingly, the performance of tests of controls is not
indicative of an increase in the assessed level of control risk.
Choice "D" is incorrect. When many internal control weaknesses are identified, the auditor would not be
likely to perform tests of controls. The assessed level of control risk is increased, and more reliance would
be placed on substantive tests as opposed to tests of controls.
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:
NEW QUESTION: 4
Table of Acronyms
Refer to the exhibit.
An administrator wants to establish a remote LDP session between PE-1 (loopback IP address
10.0 0.1) and PE-2 (loopback IP address; 10.0.0.2). What is required to do this?
A. Both PE-1 and PE-2 advertise the same label for their loopback IP address
B. Both PE-1 and PE-2 have a VPN instance that is defined with the same route distinguisher
(RD)
C. Both PE-1 and PE-2 use ordered mode for LDP.
D. Both PE-1 and PE-2 advertise their loopback IP address with OSPF and with LDP.
Answer: D
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