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NEW QUESTION: 1
SCENARIO
Please use the following to answer the next question:
Cheryl is the sole owner of Fitness Coach, Inc., a medium-sized company that helps individuals realize their physical fitness goals through classes, individual instruction, and access to an extensive indoor gym. She has owned the company for ten years and has always been concerned about protecting customer's privacy while maintaining the highest level of service. She is proud that she has built long-lasting customer relationships.
Although Cheryl and her staff have tried to make privacy protection a priority, the company has no formal privacy policy. So Cheryl hired Janice, a privacy professional, to help her develop one.
After an initial assessment, Janice created a first of a new policy. Cheryl read through the draft and was concerned about the many changes the policy would bring throughout the company. For example, the draft policy stipulates that a customer's personal information can only be held for one year after paying for a service such as a session with personal trainer. It also promises that customer information will not be shared with third parties without the written consent of the customer. The wording of these rules worry Cheryl since stored personal information often helps her company to serve her customers, even if there are long pauses between their visits. In addition, there are some third parties that provide crucial services, such as aerobics instructors who teach classes on a contract basis. Having access to customer files and understanding the fitness levels of their students helps instructors to organize their classes.
Janice understood Cheryl's concerns and was already formulating some ideas for revision. She tried to put Cheryl at ease by pointing out that customer data can still be kept, but that it should be classified according to levels of sensitivity. However, Cheryl was skeptical. It seemed that classifying data and treating each type differently would cause undue difficulties in the company's day-to-day operations. Cheryl wants one simple data storage and access system that any employee can access if needed.
Even though the privacy policy was only a draft, she was beginning to see that changes within her company were going to be necessary. She told Janice that she would be more comfortable with implementing the new policy gradually over a period of several months, one department at a time. She was also interested in a layered approach by creating documents listing applicable parts of the new policy for each department.
What is the main problem with Cheryl's suggested method of communicating the new privacy policy?
A. The policy would not be considered valid if not communicated in full.
B. Employees might not understand how the documents relate to the policy as a whole.
C. The policy might not be implemented consistency across departments.
D. Employees would not be comfortable with a policy that is put into action overtime.
Answer: C

NEW QUESTION: 2
Hinweis: Diese Frage ist Teil einer Reihe von Fragen, die dasselbe Szenario darstellen. Jede Frage in der Reihe enthält eine eindeutige Lösung, mit der die angegebenen Ziele erreicht werden können. Einige Fragensätze haben möglicherweise mehr als eine richtige Lösung, während andere möglicherweise keine richtige Lösung haben.
Nachdem Sie eine Frage in diesem Abschnitt beantwortet haben, können Sie NICHT mehr darauf zurückgreifen. Infolgedessen werden diese Fragen im Überprüfungsbildschirm nicht angezeigt.
Sie stellen Windows Server 2016 auf einem Server mit dem Namen Server1 bereit.
Sie müssen sicherstellen, dass Sie Windows Containers auf Server1 ausführen können.
Lösung: Auf Server1 aktivieren Sie die Container-Funktion und installieren dann das PowerShell for Docker-Modul. Sie starten den Server neu.
Erfüllt dies das Ziel?
A. Ja
B. Nein
Answer: A
Explanation:
References:
https://docs.microsoft.com/en-us/virtualization/windowscontainers/deploy-containers/deploy-containers-on-server

NEW QUESTION: 3
You are creating a machine learning model. You have a dataset that contains null rows.
You need to use the Clean Missing Data module in Azure Machine Learning Studio to identify and resolve the null and missing data in the dataset.
Which parameter should you use?
A. Replace with mean
B. Remove entire row
C. Remove entire column
D. Hot Deck
Answer: C
Explanation:
Explanation/Reference:
Explanation:
Remove entire row: Completely removes any row in the dataset that has one or more missing values. This is useful if the missing value can be considered randomly missing.
References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/clean-missing-data 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: