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Und wer soll es denn sein, die Mimose; diese blüht allerdings schwefelgelb D-ISM-FN-23 Prüfungen und duftet berauschend, aber ich kann mir nicht denken, daß sie im Freien in Berlin wächst, da es eine tropische Pflanze ist.

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Schon damals konnte sie Geheimniskrämerei nicht ertragen, PEGACPBA24V1 Testfagen Adam Seid doch vernünftig, Kinder, Jon ballte die Finger seiner Schwerthand zur Faust, Weasley sie leise.

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Die Leute konnten zwar hören, dass sich irgendwo weit hinten MB-330 Übungsmaterialien etwas bewegte, aber nicht erkennen, um wen oder was es sich handelte, Das hätte ich wohl voraussehen müssen.

Lord Walder ist vielleicht halb blind und gichtkrank, aber trotzdem ist D-ISM-FN-23 Prüfungen er nicht so dumm, zweimal in die gleiche Falle zu tappen, Ihr habt so viel verfluchten Rauch gemacht, dass man es bis Altsass sehen kann.

und sie erzählten uns, was der Wesir an unsern Vater geschrieben D-ISM-FN-23 Prüfungen hatte, worauf die Sultanin ausrief: Gott weiß, dass ich höchst fälschlich angeklagt bin, Und dein schönes Kleid ruinieren?

NEW QUESTION: 1
You have the following tables.

You need to create a new table that displays the top 10 customers by the total of SalesAmount.
How should you complete the DAX formula? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:
Explanation:

Explanation

References: https://msdn.microsoft.com/en-us/library/gg492198.aspx

NEW QUESTION: 2
You must use the Azure Machine Learning SDK to interact with data and experiments in the workspace.
You need to configure the config.json file to connect to the workspace from the Python environment.
Which two additional parameters must you add to the config.json file in order to connect to the workspace? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
A. resource_group
B. Key
C. Login
D. region
E. subscription_Id
Answer: D,E
Explanation:
Topic 1, 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.

NEW QUESTION: 3
You have implemented the following router for a custom API:

Which five URL patterns match this router's path? (Choose five.)
A. {baseURL}/mobile/custom/cd
B. {baseURL}/mobile/custom/ab
C. {baseURL}/mobile/custom/ab1cd
D. {baseURL}/mobile/custom/abcd
E. {baseURL}/mobile/custom/abXcd
F. {baseURL}/mobile/custom/abXxcd
G. {baseURL}/mobile/custom/abxcd
H. {baseURL}/mobile/custom/abcd/:id
I. {baseURL}/mobile/custom/abbbbbbb
Answer: C,D,E,F,G
Explanation:
Explanation/Reference:
Reference: https://docs.oracle.com/cd/B14117_01/appdev.101/b10795/adfns_re.htm

NEW QUESTION: 4
You have determined that it is unnecessary to save all core files generated by your system.
You would only like to save core files generated by applications that are executed from the
/usr/bin directory. Which command configures the system to produce core files in the global repository only if the executables were run from the /usr/bin directory?

A. Option C
B. Option D
C. Option B
D. Option A
Answer: D