EXAM DP-100 ASSESSMENT - DP-100 FREE EXAM QUESTIONS

Exam DP-100 Assessment - DP-100 Free Exam Questions

Exam DP-100 Assessment - DP-100 Free Exam Questions

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Our practice exams are designed solely to help you get your Microsoft DP-100 certification on your first try. A Microsoft DP-100 practice test will help you understand the exam inside out and you will get better marks overall. It is only because you have practical experience of the exam even before the exam itself. ExamBoosts offers authentic and up-to-date study material that every candidate can rely on for good preparation. Our top priority is to help you pass the Designing and Implementing a Data Science Solution on Azure (DP-100) exam on the first try.

How to study the DP-100 Exam

ExamBoosts expert team recommends you to prepare some notes on these topics along with it don't forget to practice Microsoft DP-100 Exam exam dumps which been written by our expert team, Both these will help you a lot to clear this exam with good marks.

Microsoft DP-100 certification exam is designed for data scientists who want to validate their skills in designing and implementing data science solutions on the Azure platform. DP-100 Exam is ideal for professionals who have experience in programming languages such as Python and R, and are familiar with Azure Machine Learning, Azure Databricks, and other Azure services. The DP-100 exam is a great way to showcase your expertise in the field of data science and gain recognition as a certified data scientist.

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Unique, Full Length Exams - New Microsoft DP-100 Pratice Exam

As you know, many exam and tests depend on the skills as well as knowledge, our DP-100 practice materials are perfectly and exclusively devised for the exam and can satisfy your demands both. There are free demos for your reference with brief catalogue and outlines in them. Free demos are understandable materials as well as the newest information for your practice. Under coordinated synergy of all staff, our DP-100 practice materials achieved a higher level of perfection by keeping close attention with the trend of dynamic market.

Microsoft Designing and Implementing a Data Science Solution on Azure Sample Questions (Q170-Q175):

NEW QUESTION # 170
You create a multi-class image classification deep learning experiment by using the PyTorch framework. You plan to run the experiment on an Azure Compute cluster that has nodes with GPU's.
You need to define an Azure Machine Learning service pipeline to perform the monthly retraining of the image classification model. The pipeline must run with minimal cost and minimize the time required to train the model.
Which three pipeline steps should you run in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.

Answer:

Explanation:

Explanation

Step 1: Configure a DataTransferStep() to fetch new image data...
Step 2: Configure a PythonScriptStep() to run image_resize.y on the cpu-compute compute target.
Step 3: Configure the EstimatorStep() to run training script on the gpu_compute computer target.
The PyTorch estimator provides a simple way of launching a PyTorch training job on a compute target.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-train-pytorch


NEW QUESTION # 171
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:

Reference:
https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.KFold.html


NEW QUESTION # 172
You collect data from a nearby weather station. You have a pandas dataframe named weather_df that includes the following data:

The data is collected every 12 hours: noon and midnight.
You plan to use automated machine learning to create a time-series model that predicts temperature over the next seven days. For the initial round of training, you want to train a maximum of 50 different models.
You must use the Azure Machine Learning SDK to run an automated machine learning experiment to train these models.
You need to configure the automated machine learning run.
How should you complete the AutoMLConfig definition? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:
Explanation


Box 1: forcasting
Task: The type of task to run. Values can be 'classification', 'regression', or 'forecasting' depending on the type of automated ML problem to solve.
Box 2: temperature
The training data to be used within the experiment. It should contain both training features and a label column (optionally a sample weights column).
Box 3: observation_time
time_column_name: The name of the time column. This parameter is required when forecasting to specify the datetime column in the input data used for building the time series and inferring its frequency. This setting is being deprecated. Please use forecasting_parameters instead.
Box 4: 7
"predicts temperature over the next seven days"
max_horizon: The desired maximum forecast horizon in units of time-series frequency. The default value is 1.
Units are based on the time interval of your training data, e.g., monthly, weekly that the forecaster should predict out. When task type is forecasting, this parameter is required.
Box 5: 50
"For the initial round of training, you want to train a maximum of 50 different models." Iterations: The total number of different algorithm and parameter combinations to test during an automated ML experiment.
Reference:
https://docs.microsoft.com/en-us/python/api/azureml-train-automl-client/azureml.train.automl.automlconfig.auto


NEW QUESTION # 173
You use Azure Machine Learning to implement hyperparameter tuning with a Bandit early termination policy.
The policy uses a slack_factor set to 01. an evaluation interval set to 1, and an evaluation delay set to b.
You need to evaluate the outcome of the early termination policy
What should you evaluate? To answer, select the appropriate options m the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation


NEW QUESTION # 174
You manage an Azure Machine Learning workspace named workspace1 by using the Python SDK v2.
You must register datastores in workspace1 for Azure Blob and Azure Data Lake Gen2 storage to meet the following requirements:
* Data scientists accessing the datastore must have the same level of access.
* Access must be restricted to specified containers or folders.
You need to configure a security access method used to register the Azure Blob and Azure Data lake Gen?
storage in workspace1. Which security access method should you configure? To answer, select the appropriate options in the answers area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation:


NEW QUESTION # 175
......

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