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NEW QUESTION: 1
A customer is using blade servers with only one HBA.
Which two design concepts apply to this scenario? (Choose two.)
A. Constraint
B. Requirement
C. Assumption
D. Risk
Answer: A,D
Explanation:
Explanation
One HBA is a single point of failure and therefore a risk.Using this hardware configuration is a constraint (which although might also be a non-functional requirement

NEW QUESTION: 2
You are performing feature engineering on a dataset.
You must add a feature named CityName and populate the column value with the text London.
You need to add the new feature to the dataset.
Which Azure Machine Learning Studio module should you use?
A. Latent Dirichlet Allocation
B. Edit Metadata
C. Preprocess Text
D. Execute Python Script
Answer: B
Explanation:
Explanation/Reference:
Explanation:
Typical metadata changes might include marking columns as features.
References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/edit-metadata 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: 3
To ensure that VMAC mode is enabled, which CLI command you should run on all cluster members? Choose the best answer.
A. cphaprob -a if
B. fw ctl get int fwha vmac global param enabled; result of command should return value 1
C. fw ctl get int fwha_vmac_global_param_enabled; result of command should return value 1
D. fw ctl set int fwha vmac global param enabled
Answer: B

NEW QUESTION: 4
Gene therapy offers a new treatment paradigm for curing human disease. Rather than altering the disease phenotype by using agents that interact with gene products, or are themselves gene products, gene therapy can theoretically modify specific genes resulting in disease cure following a single administration.
Initially gene therapy was envisioned for the treatment of genetic disorders, but is currently being studied for use with a wide range of diseases, including cancer, peripheral vascular disease, arthritis, Neurodegenerative disorders and other acquired diseases.
Certain key elements are required for a successful gene therapy strategy. The most elementary of these is that the relevant gene be identified and cloned. Upon completion of the Human Genome Project, gene availability will be unlimited. Once identified and cloned, the next consideration must be expression of the gene. Questions pertaining to the efficiency of gene transfer and gene expression remain at the forefront of gene therapy research, with current debates revolving around the transfer of desired genes to appropriate cells, and then to obtaining sufficient levels of expression for disease treatment. With luck, future research on gene transfer and tissue-specific gene expression will resolve these issues for the majority of gene therapy protocols.
Other important considerations for a gene therapy strategy include a sufficient understanding of the pathogenesis of the targeted disorder, potential side effects of the gene therapy treatment, and a more in depth understanding of the target cells which are to receive gene therapy.
Gene transfer vector is the mechanism by which the gene is transferred into a cell. Currently there are at least 150 clinical gene therapy protocols worldwide. Since the approval process for these protocols is not as public outside the U.S., it is difficult to ascertain the exact number of worldwide protocols. As of December 1995, 1024 patients had been treated with either a gene transfer or gene therapy protocol.
Much controversy exists regarding how many of these patients have benefited from their gene therapy, and no one has yet been cured.
Public controversy in the field of human gene therapy is driven by several factors. Ordinary citizens as well as scientists easily understand the enormous potential of gene therapy, but the former may not appreciate all the pitfalls and uncertainly that lie in the immediate future. The financial interests of biotechnology firms and, some have asserted the career interests of some gene therapists have encouraged extravagant, or at least verily optimistic public statements about contemporary gene therapy. In spite of the proliferation of protocols, the actual number of patients treated remains small, and only one genuinely controlled study of human gene therapy has been published as of this date All of the following are mentioned in the passage as elements that are required for a successful gene therapy strategy EXCEPT:
A. Identifying the relevant gene
B. Gaining and a more in depth understanding of the target cells which are to receive gene therapy.
C. Understanding of the pathogenesis of the targeted disorder
D. Determining the side effects of the relevant gene
E. Expressing the relevant gene
Answer: D
Explanation:
Explanation/Reference:
Explanation:
The best answer is C.
One must determine the side effects of the relevant gene therapy treatment, and not of the relevant gene itself

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