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Exam CLF-C01 Questions Answers | Examcollection CLF-C01 Free Dumps & CLF-C01 Test Pdf - Lp-Prime

Vendor: Amazon
Exam Code: CLF-C01
Exam Name: Amazon AWS Certified Cloud Practitioner
Certification: Amazon Foundational
Total Questions: 137 Q&A
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NEW QUESTION: 1

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

NEW QUESTION: 2
In a public key infrastructure (PKI), which of the following may be relied upon to prove that an online transaction was authorized by a specific customer?
A. Nonrepudiation
B. Integrity
C. Encryption
D. Authentication
Answer: A
Explanation:
Explanation/Reference:
Explanation:
Nonrepudiation, achieved through the use of digital signatures, prevents the claimed sender from later denying that they generated and sent the message. Encryption may protect the data transmitted over the Internet, but may not prove that the transactions were made. Authentication is necessary to establish the identification of all parties to a communication. Integrity ensures that transactions are accurate but does not provide the identification of the customer.

NEW QUESTION: 3
Which option correctly describes the high-level process from loading measurements through calculatingusage?
A. 1. Load Initial Measurements
2. Validate, evaluate, and estimate Initial Measurements
3. Update Initial Measurements
4. Calculate Usage
B. 1. Load Initial Measurements
2. Validate, edit, and estimate Initial Measurements
3. Create Final Measurements
4. Update Initial Measurements
C. 1. Load Initial Measurements
2. Validate, edit, and estimate Initial Measurements
3. Create Final Measurements
4. Calculate Usage
D. 1. Load Initial Measurements
2. Verify, evaluate, and enter Initial Measurements
3. Create Final Measurements
4. Calculate Usage
Answer: D

NEW QUESTION: 4
You have a Python data frame named salesData in the following format:

The data frame must be unpivoted to a long data format as follows:

You need to use the pandas.melt() function in Python to perform the transformation.
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: dataFrame
Syntax: pandas.melt(frame, id_vars=None, value_vars=None, var_name=None, value_name='value', col_level=None)[source] Where frame is a DataFrame Box 2: shop Paramter id_vars id_vars : tuple, list, or ndarray, optional Column(s) to use as identifier variables.
Box 3: ['2017','2018']
value_vars : tuple, list, or ndarray, optional
Column(s) to unpivot. If not specified, uses all columns that are not set as id_vars.
Example:
df = pd.DataFrame({'A': {0: 'a', 1: 'b', 2: 'c'},
... 'B': {0: 1, 1: 3, 2: 5},
... 'C': {0: 2, 1: 4, 2: 6}})
pd.melt(df, id_vars=['A'], value_vars=['B', 'C'])
A variable value
0 a B 1
1 b B 3
2 c B 5
3 a C 2
4 b C 4
5 c C 6
References:
https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.melt.html