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Exam Code: Associate-Data-Practitioner Dumps

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NEW QUESTION: 1
What are two valid RAID levels supported by the HUS VM storage system? (Choose two.)
A. RAID 10 (8D+2P)
B. RAID 6 (10D+2P)
C. RAID 5 (3D+1P,7D+1P)
D. RAID 6 (14D+2P)
Answer: C,D

NEW QUESTION: 2
What are two likely causes for a DRS cluster to become unbalanced? (Choose two.)
A. Migration threshold is too low.
B. Affinity rules are preventing virtual machines from being moved.
C. Migration cost is too low.
D. A device is mounted to a virtual machine preventing vMotion.
Answer: B,D
Explanation:
Explanation/Reference:
Explanation:
Load Imbalance on Cluster
A cluster has a load imbalance of resources.
Problem
A cluster might become unbalanced because of uneven resource demands from virtual machines and unequal capacities of hosts.
Cause
The following are possible reasons why the cluster has a load imbalance:
■ The migration threshold is too high.
A higher threshold makes the cluster a more likely candidate for load imbalance.
■ VM/VM or VM/Host DRS rules prevent virtual machines from being moved.
■ DRS is disabled for one or more virtual machines.
■ A device is mounted to one or more virtual machines preventing DRS from moving the virtual machine in order to balance the load.
■ Virtual machines are not compatible with the hosts to which DRS would move them. That is, at least one of the hosts in the cluster is incompatible for the virtual machines that would be migrated. For example, if host A's CPU is not vMotion-compatible with host B's CPU, then host A becomes incompatible for powered-on virtual machines running on host B.
■ It would be more detrimental for the virtual machine's performance to move it than for it to run where it is currently located. This may occur when loads are unstable or the migration cost is high compared to the benefit gained from moving the virtual machine.
■ vMotion is not enabled or set up for the hosts in the cluster.
Reference:
https://pubs.vmware.com/vsphere-51/index.jsp?topic=%2Fcom.vmware.vsphere.troubleshooting.doc%
2FGUID-0C132C85-E7BF-445E-925D-31420A06B01C.html

NEW QUESTION: 3
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


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