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New DP-100 Practice Materials | DP-100 Latest Exam Registration

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Passing the DP-100 exam provides candidates with a valuable certification that demonstrates their expertise in designing and implementing data solutions on Azure. Designing and Implementing a Data Science Solution on Azure certification is recognized by employers around the world, and can help individuals to advance their careers in the field of data science and machine learning. With the increasing demand for skilled data scientists and machine learning engineers, the DP-100 exam is an excellent way for individuals to distinguish themselves in a competitive job market.

Microsoft DP-100 certification exam is designed to test the skills and knowledge required to design and implement a data science solution on Azure. DP-100 exam is aimed at professionals who are involved in building and implementing data science solutions, including data scientists, data engineers, and machine learning engineers. Passing the exam demonstrates that the candidate has the skills and knowledge required to design and implement a data science solution on Azure.

To prepare for the DP-100 Exam, candidates can take instructor-led training courses, self-paced online courses, and practice tests. Microsoft offers several training courses that cover the exam objectives, including "Designing and Implementing a Data Science Solution on Azure" and "Implementing an Azure Data Solution." Candidates can also access online resources such as Microsoft documentation and GitHub repositories to learn about Azure technologies and data science concepts.

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Microsoft Designing and Implementing a Data Science Solution on Azure Sample Questions (Q369-Q374):

NEW QUESTION # 369
You are performing sentiment analysis using a CSV file that includes 12,000 customer reviews written in a short sentence format. You add the CSV file to Azure Machine Learning Studio and configure it as the starting point dataset of an experiment. You add the Extract N-Gram Features from Text module to the experiment to extract key phrases from the customer review column in the dataset.
You must create a new n-gram dictionary from the customer review text and set the maximum n-gram size to trigrams.
What should you select? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Vocabulary mode: Create
For Vocabulary mode, select Create to indicate that you are creating a new list of n-gram features.
N-Grams size: 3
For N-Grams size, type a number that indicates the maximum size of the n-grams to extract and store. For example, if you type 3, unigrams, bigrams, and trigrams will be created.
Weighting function: Leave blank
The option, Weighting function, is required only if you merge or update vocabularies. It specifies how terms in the two vocabularies and their scores should be weighted against each other.
References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/extract-n-gram-features- from-text

NEW QUESTION # 370
Hotspot Question
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

NEW QUESTION # 371
You are building a regression model tot estimating the number of calls during an event.
You need to determine whether the feature values achieve the conditions to build a Poisson regression model.
Which two conditions must the feature set contain? I ach correct answer presents part of the solution. NOTE:
Each correct selection is worth one point.

A. The data must be whole numbers.
B. The label data must be a negative value.
C. The label data must be non discrete.
D. The label data can be positive or negative,
E. The label data mull be a positive value

Answer: C,E

NEW QUESTION # 372
You manage an Azure Machine Learning workspace named workspace1by using the Python SDK v2.
You must register datastores in workspace 1 for Azure Blot storage and Azure Fetes storage to meet the following requirements.
* Azure Active Directory (Azure AD) authentication must be used for access to storage when possible.
* Credentials and secrets steed in workspace1 must be valid lot a specified time period when accessing Azure Files storage.
You need to configure a security access method used to register the Azure Blob and azure files storage in workspace1.
Which security access method should you configure? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

NEW QUESTION # 373
Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.
After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.
You are using Azure Machine Learning to run an experiment that trains a classification model.
You want to use Hyperdrive to find parameters that optimize the AUC metric for the model. You configure a HyperDriveConfig for the experiment by running the following code:

You plan to use this configuration to run a script that trains a random forest model and then tests it with validation data. The label values for the validation data are stored in a variable named y_test variable, and the predicted probabilities from the model are stored in a variable named y_predicted.
You need to add logging to the script to allow Hyperdrive to optimize hyperparameters for the AUC metric.
Solution: Run the following code:

Does the solution meet the goal?

A. Yes
B. No

Answer: B

Explanation:
Explanation
Use a solution with logging.info(message) instead.
Note: Python printing/logging example:
logging.info(message)
Destination: Driver logs, Azure Machine Learning designer
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-debug-pipelines

NEW QUESTION # 374
......

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