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NEW QUESTION: 1
You have a dataset created for multiclass classification tasks that contains a normalized numerical feature set with 10,000 data points and 150 features.
You use 75 percent of the data points for training and 25 percent for testing. You are using the scikit-learn machine learning library in Python. You use X to denote the feature set and Y to denote class labels.
You create the following Python data frames:
You need to apply the Principal Component Analysis (PCA) method to reduce the dimensionality of the feature set to 10 features in both training and testing sets.
How should you complete the code segment? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation
Box 1: PCA(n_components = 10)
Need to reduce the dimensionality of the feature set to 10 features in both training and testing sets.
Example:
from sklearn.decomposition import PCA
pca = PCA(n_components=2) ;2 dimensions
principalComponents = pca.fit_transform(x)
Box 2: pca
fit_transform(X[, y])fits the model with X and apply the dimensionality reduction on X.
Box 3: transform(x_test)
transform(X) applies dimensionality reduction to X.
References:
https://scikit-learn.org/stable/modules/generated/sklearn.decomposition.PCA.html
NEW QUESTION: 2
Refer to the exhibit. Some 2950 series switches are connected to the conference area of the corporate headquarters network. The switches provide two to three jacks per conference room to host laptop connections for employees who visit the headquarters office. When large groups of employees come from other locations, the network administrator often finds that hubs have been connected to wall jacks in the conference area although the ports on the access layer switches were not intended to support multiple workstations.
What action could the network administrator take to prevent access by multiple laptops through a single switch port and still leave the switch functional for its intended use?
A. Use the mac-address-table 1 global configuration command to limit each port to one source MAC address.
B. Configure an ACL to allow only a single MAC address to connect to the switch at one time.
C. Configure static entries in the switch MAC address table to include the range of addresses used by visiting employees.
D. Implement Port Security on all interfaces and use the port-security mac-address sticky command to limit access to a single MAC address.
E. Implement Port Security at global configuration mode and use the port-security maximum 1 command to allow each switch only one attached hub.
F. Implement Port Security on all interfaces and use the port-security maximum 1 command to limit port access to a single MAC address.
Answer: F
NEW QUESTION: 3
비즈니스 팀에는 회사의 모든 과거 판매 데이터를 저장하기위한 구조화 된 스토리지 솔루션이 필요합니다. 현재 4TB의 데이터가 있으며 몇 년 안에 수백 테라 바이트로 증가 할 것입니다. 팀은 최신 비즈니스 인텔리전스 도구를 사용하여 데이터에 대해 정기적으로 쿼리를 실행할 수 있어야 합니다. 데이터 세트 증가에도 불구하고 빠른 성능이 필요합니다.
회사는 어떤 솔루션을 사용해야 합니까?
A. 아마존 S3
B. 아마존 레드 시프트
C. 아마존 오로라
D. Amazon DynamoDB
Answer: B
Explanation:
설명:
https://aws.amazon.com/blogs/aws/category/amazon-redshift/
NEW QUESTION: 4
An organization uses Azure Machine Learning service and wants to expand their use of machine learning.
You have the following compute environments. The organization does not want to create another compute environment.
You need to determine which compute environment to use for the following scenarios.
Which compute types should you use? To answer, drag the appropriate compute environments to the correct scenarios. Each compute environment may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Box 1: nb_server
Box 2: mlc_cluster
With Azure Machine Learning, you can train your model on a variety of resources or environments, collectively referred to as compute targets. A compute target can be a local machine or a cloud resource, such as an Azure Machine Learning Compute, Azure HDInsight or a remote virtual machine.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/concept-compute-target
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-set-up-training-targets