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Related DP-100 Exams - DP-100 PDF Dumps Files
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2026 Latest Actual4Exams DP-100 PDF Dumps and DP-100 Exam Engine Free Share: https://drive.google.com/open?id=1STfq2wQSbwM6wqkbb6iIMrLZFKsQLZYA
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Microsoft Designing and Implementing a Data Science Solution on Azure Sample Questions (Q455-Q460):NEW QUESTION # 455
You use Azure Machine Learning to deploy a model as a real-time web service.
You need to create an entry script for the service that ensures that the model is loaded when the service starts and is used to score new data as it is received.
Which functions should you include in the script? To answer, drag the appropriate functions to the correct actions. Each function 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: init()
The entry script has only two required functions, init() and run(data). These functions are used to initialize the service at startup and run the model using request data passed in by a client. The rest of the script handles loading and running the model(s).
Box 2: run()
Reference:
https://docs.microsoft.com/en-us ... ploy-existing-model
NEW QUESTION # 456
You must use in Azure Data Science Virtual Machine (DSVM) as a compute target.
You need to attach an existing DSVM to the workspace by using the Azure Machine Learning SDK for Python.
How should you complete the following code segment? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
NEW QUESTION # 457
You are building a binary classification model by using a supplied training set.
The training set is imbalanced between two classes.
You need to resolve the data imbalance.
What are three possible ways to achieve this goal? Each correct answer presents a complete solution NOTE: Each correct selection is worth one point.
- A. Normalize the training feature set.
- B. Penalize the classification
- C. Generate synthetic samples in the minority class.
- D. Use accuracy as the evaluation metric of the model.
- E. Resample the data set using under sampling or oversampling
Answer: B,D,E
Explanation:
References:
https://machinelearningmastery.c ... e-learning-dataset/
NEW QUESTION # 458
You plan to preprocess text from CSV files. You load the Azure Machine Learning Studio default stop words list.
You need to configure the Preprocess Text module to meet the following requirements:
* Ensure that multiple related words from a single canonical form.
* Remove pipe characters from text.
* Remove words to optimize information retrieval.
Which three 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: Remove stop words
Remove words to optimize information retrieval.
Remove stop words: Select this option if you want to apply a predefined stopword list to the text column. Stop word removal is performed before any other processes.
Box 2: Lemmatization
Ensure that multiple related words from a single canonical form.
Lemmatization converts multiple related words to a single canonical form Box 3: Remove special characters Remove special characters: Use this option to replace any non-alphanumeric special characters with the pipe | character.
References:
https://docs.microsoft.com/en-us ... nce/preprocess-text
NEW QUESTION # 459
You previously deployed a model that was trained using a tabular dataset named training-dataset, which is based on a folder of CSV files.
Over time, you have collected the features and predicted labels generated by the model in a folder containing a CSV file for each month. You have created two tabular datasets based on the folder containing the inference data: one named predictions-dataset with a schema that matches the training data exactly, including the predicted label; and another named features-dataset with a schema containing all of the feature columns and a timestamp column based on the filename, which includes the day, month, and year.
You need to create a data drift monitor to identify any changing trends in the feature data since the model was trained. To accomplish this, you must define the required datasets for the data drift monitor.
Which datasets should you use to configure the data drift monitor? To answer, drag the appropriate datasets to the correct data drift monitor options. Each source 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: training-dataset
Baseline dataset - usually the training dataset for a model.
Box 2: predictions-dataset
Target dataset - usually model input data - is compared over time to your baseline dataset. This comparison means that your target dataset must have a timestamp column specified.
The monitor will compare the baseline and target datasets.
Reference:
https://docs.microsoft.com/en-us ... to-monitor-datasets
NEW QUESTION # 460
......
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