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NEW QUESTION: 1
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. Hot Deck
B. Remove entire row
C. Remove entire column
D. Replace with mean
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:
NEW QUESTION: 2
お客様のネットワークに新しいスイッチをインストールして構成するように求められました。既存のスイッチおよび新しいスイッチへのコンソールアクセスを使用して、正しいデバイス構成を構成および検証します。
構成を参照してください。 SW1とSW2の間のトランクを介して送信されるタグなしフレームは、どのVLANに構成されていますか？
A. VLAN 300
B. VLAN 40
C. VLAN 200
D. VLAN 50
E. VLAN1
F. VLAN 99
G. VLAN 999
Answer: F
Explanation:
The native VLAN is used for untagged frames sent along a trunk. By issuing the "show interface trunk" command on SW1 and SW2 we see the native VLAN is 99.
NEW QUESTION: 3
Which two criteria can be applied in the Response Plan application? (Choose two.)
A. Assign Vendor
B. Assign Material
C. Assign Labor
D. Apply Solution
E. Apply Security
Answer: A,D
NEW QUESTION: 4
Which command can you enter to prevent IS-IS PDUs from using the full MTU size?
A. No hello padding
B. Set-overload-bit on-startup 120
C. Set-overloaded-bit on-startup wait-for-bgp suppress interievel
D. Metric-style wide
Answer: B