DRAG DROP (Drag and Drop is not supported)You create a multi-class image classification deep learning model.The model must be retrained monthly with the new image data fetched from a public web portal. You create an Azure Machine Learning pipeline to fetch new data, standardize the size of images, and retrain the model.You need to use the Azure Machine Learning SDK to configure the schedule for the pipeline.Which four actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.Select and Place:
Answer(s): A
Step 1: Publish the pipeline.To schedule a pipeline, you'll need a reference to your workspace, the identifier of your published pipeline, and the name of the experiment in which you wish to create the schedule.Step 2: Retrieve the pipeline ID.Needed for the schedule.Step 3: Create a ScheduleRecurrence..To run a pipeline on a recurring basis, you'll create a schedule. A Schedule associates a pipeline, an experiment, and a trigger.First create a schedule. Example: Create a Schedule that begins a run every 15 minutes: recurrence = ScheduleRecurrence(frequency="Minute", interval=15)Step 4: Define an Azure Machine Learning pipeline schedule..Example, continued:recurring_schedule = Schedule.create(ws, name="MyRecurringSchedule", description="Based on time", pipeline_id=pipeline_id, experiment_name=experiment_name, recurrence=recurrence)
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-schedule-pipelines
HOTSPOT (Drag and Drop is not supported)You create a script for training a machine learning model in Azure Machine Learning service.You create an estimator by running the following code:For each of the following statements, select Yes if the statement is true. Otherwise, select No.NOTE: Each correct selection is worth one point.Hot Area:
Box 1: Yes Parameter source_directory is a local directory containing experiment configuration and code files needed for a training job.Box 2: Yes script_params is a dictionary of command-line arguments to pass to the training script specified in entry_script.Box 3: No Box 4: Yes The conda_packages parameter is a list of strings representing conda packages to be added to the Python environment for the experiment.
HOTSPOT (Drag and Drop is not supported)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.Hot Area:
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, optionalColumn(s) to use as identifier variables.Box 3: ['2017','2018']value_vars : tuple, list, or ndarray, optionalColumn(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 11 b B 32 c B 53 a C 24 b C 45 c C 6
https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.melt.html
HOTSPOT (Drag and Drop is not supported)You are working on a classification task. You have a dataset indicating whether a student would like to play soccer and associated attributes. The dataset includes the following columns:You need to classify variables by type.Which variable should you add to each category? To answer, select the appropriate options in the answer area.NOTE: Each correct selection is worth one point.Hot Area:
https://www.edureka.co/blog/classification-algorithms/
HOTSPOT (Drag and Drop is not supported)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.Hot Area:
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 formBox 3: Remove special charactersRemove special characters: Use this option to replace any non-alphanumeric special characters with the pipe | character.
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/preprocess-text
Post your Comments and Discuss Microsoft DP-100 exam prep with other Community members:
Our website is free, but we have to fight against AI bots and content theft. We're sorry for the inconvenience caused by these security measures. You can access the rest of the DP-100 content, but please register or login to continue.