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[General] DP-100 Simulation Questions, DP-100 Most Reliable Questions

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【General】 DP-100 Simulation Questions, DP-100 Most Reliable Questions

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Earning the DP-100 certification can help you advance your career in data science and machine learning. It demonstrates to employers that you have the knowledge and skills to design and implement data-driven solutions using Azure services. Additionally, Microsoft offers a variety of other Azure certifications that can complement the DP-100 and help you build a well-rounded skillset in cloud computing.
To prepare for the DP-100 exam, candidates must have a solid understanding of data science and machine learning concepts, as well as experience working with Azure data services. They should be familiar with Azure Machine Learning Studio, Azure Databricks, Azure HDInsight, and other relevant Azure technologies. DP-100 exam consists of multiple-choice questions and performance-based tasks that require candidates to demonstrate their ability to design and implement data science solutions in real-world scenarios. The DP-100 Certification is a valuable credential for professionals seeking to advance their careers in data science and machine learning on the Azure platform.
The DP-100 exam is intended for data scientists who have experience working with data and developing machine learning models. It focuses on the practical application of data science techniques using Azure tools and services. Candidates will need to demonstrate their ability to use Azure Machine Learning to prepare data, train models, and deploy solutions. They will also need to know how to work with other Azure services such as Azure Databricks, Azure Stream Analytics, and Azure Synapse Analytics.
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Microsoft Designing and Implementing a Data Science Solution on Azure Sample Questions (Q47-Q52):NEW QUESTION # 47
You need to configure the Edit Metadata module so that the structure of the datasets match.
Which configuration options should you select? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:
Explanation:

Explanation:

Box 1: Floating point
Need floating point for Median values.
Scenario: An initial investigation shows that the datasets are identical in structure apart from the MedianValue column. The smaller Paris dataset contains the MedianValue in text format, whereas the larger London dataset contains the MedianValue in numerical format.
Box 2: Unchanged
Note: Select the Categorical option to specify that the values in the selected columns should be treated as categories.
For example, you might have a column that contains the numbers 0,1 and 2, but know that the numbers actually mean "Smoker", "Non smoker" and "Unknown". In that case, by flagging the column as categorical you can ensure that the values are not used in numeric calculations, only to group data.

NEW QUESTION # 48
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 create an Azure Machine Learning service datastore in a workspace. The datastore contains the following files:
* /data/2018/Q1.csv
* /data/2018/Q2.csv
* /data/2018/Q3.csv
* /data/2018/Q4.csv
* /data/2019/Q1.csv
All files store data in the following format:
id,f1,f2,I
1,1,2,0
2,1,1,1
3,2,1,0
4,2,2,1
You run the following code:

You need to create a dataset named training_data and load the data from all files into a single data frame by using the following code:

Solution: Run the following code:

Does the solution meet the goal?
  • A. Yes
  • B. No
Answer: B
Explanation:
Use two file paths.
Use Dataset.Tabular_from_delimeted, instead of Dataset.File.from_files as the data isn't cleansed.
Note:
A FileDataset references single or multiple files in your datastores or public URLs. If your data is already cleansed, and ready to use in training experiments, you can download or mount the files to your compute as a FileDataset object.
A TabularDataset represents data in a tabular format by parsing the provided file or list of files. This provides you with the ability to materialize the data into a pandas or Spark DataFrame so you can work with familiar data preparation and training libraries without having to leave your notebook. You can create a TabularDataset object from .csv, .tsv, .parquet, .jsonl files, and from SQL query results.
Reference:
https://docs.microsoft.com/en-us ... e-register-datasets

NEW QUESTION # 49
Hotspot Question
You have a multi-class image classification deep learning model that uses a set of labeled photographs. You create the following code to select hyperparameter values when training the model.

For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.

Answer:
Explanation:

Explanation:
Box 1: Yes
Hyperparameters are adjustable parameters you choose to train a model that govern the training process itself. Azure Machine Learning allows you to automate hyperparameter exploration in an efficient manner, saving you significant time and resources. You specify the range of hyperparameter values and a maximum number of training runs. The system then automatically launches multiple simultaneous runs with different parameter configurations and finds the configuration that results in the best performance, measured by the metric you choose. Poorly performing training runs are automatically early terminated, reducing wastage of compute resources. These resources are instead used to explore other hyperparameter configurations.
Box 2: Yes
uniform(low, high) - Returns a value uniformly distributed between low and high Box 3: No Bayesian sampling does not currently support any early termination policy.
Reference:
https://docs.microsoft.com/en-us ... une-hyperparameters

NEW QUESTION # 50
You are evaluating a Python NumPy array that contains six data points defined as follows:
data = [10, 20, 30, 40, 50, 60]
You must generate the following output by using the k-fold algorithm implantation in the Python Scikit-learn machine learning library:
train: [10 40 50 60], test: [20 30]
train: [20 30 40 60], test: [10 50]
train: [10 20 30 50], test: [40 60]
You need to implement a cross-validation to generate the output.
How should you complete the code segment? To answer, select the appropriate code segment in the dialog box in the answer area.
NOTE: Each correct selection is worth one point.

Answer:
Explanation:

Explanation

Box 1: k-fold
Box 2: 3
K-Folds cross-validator provides train/test indices to split data in train/test sets. Split dataset into k consecutive folds (without shuffling by default).
The parameter n_splits ( int, default=3) is the number of folds. Must be at least 2.
Box 3: data
Example: Example:
>>>
>>> from sklearn.model_selection import KFold
>>> X = np.array([[1, 2], [3, 4], [1, 2], [3, 4]])
>>> y = np.array([1, 2, 3, 4])
>>> kf = KFold(n_splits=2)
>>> kf.get_n_splits(X)
2
>>> print(kf)
KFold(n_splits=2, random_state=None, shuffle=False)
>>> for train_index, test_index in kf.split(X):
print("TRAIN:", train_index, "TEST:", test_index)
X_train, X_test = X[train_index], X[test_index]
y_train, y_test = y[train_index], y[test_index]
TRAIN: [2 3] TEST: [0 1]
TRAIN: [0 1] TEST: [2 3]
References:
https://scikit-learn.org/stable/ ... election.KFold.html

NEW QUESTION # 51
You need to configure the Permutation Feature Importance module for the model training requirements.
What should you do? To answer, select the appropriate options in the dialog box in the answer area.
NOTE: Each correct selection is worth one point.

Answer:
Explanation:

Reference:
https://docs.microsoft.com/en-us ... -feature-importance

NEW QUESTION # 52
......
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