Microsoft DP-100 Online Practice Questions and Exam Preparation

Microsoft DP-100 Online Questions & Answers

• Question 581:

  HOTSPOT

  You create an Azure Machine Learning compute target named ComputeOne by using the STANDARD_D1 virtual machine image.

  You define a Python variable named was that references the Azure Machine Learning workspace. You run the following Python 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.

  

  

  ---

  Box 1:Yes

  ComputeTargetException class: An exception related to failures when creating, interacting with, or configuring a compute target. This exception is commonly raised for failures attaching a compute target, missing headers, and unsupported configuration values.

  Create(workspace, name, provisioning_configuration)

  Provision a Compute object by specifying a compute type and related configuration.

  This method creates a new compute target rather than attaching an existing one.

  Box 2: Yes

  Box 3: No

  The line before print('Step1') will fail.

  https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.compute.computetarget

• Question 582:

  You are determining if two sets of data are significantly different from one another by using Azure Machine Learning Studio.

  Estimated values in one set of data may be more than or less than reference values in the other set of data. You must produce a distribution that has a constant Type I error as a function of the correlation.

  You need to produce the distribution.

  Which type of distribution should you produce?

  A. Unpaired t-test with a two-tail option
  B. Unpaired t-test with a one-tail option
  C. Paired t-test with a one-tail option
  D. Paired t-test with a two-tail option
  D. Paired t-test with a two-tail option

  ---

  Explanation

  Choose a one-tail or two-tail test. The default is a two-tailed test. This is the most common type of test, in which the expected distribution is symmetric around zero.

  Example: Type I error of unpaired and paired two-sample t-tests as a function of the correlation. The simulated random numbers originate from a bivariate normal distribution with a variance of 1.

  

  https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/test-hypothesis-using-t-

  test

  https://en.wikipedia.org/wiki/Student%27s_t-test

• Question 583:

  DRAG DROP

  You need to define a process for penalty event detection.

  Which three 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:

  

  

• Question 584:

  You plan to use a Python script to run an Azure Machine Learning experiment. The script creates a reference to the experiment run context, loads data from a file, identifies the set of unique values for the label column, and completes the experiment run:

  from azureml.core import Run

  import pandas as pd

  run = Run.get_context()

  data = pd.read_csv('data.csv')

  label_vals = data['label'].unique()

  # Add code to record metrics here run.complete()

  The experiment must record the unique labels in the data as metrics for the run that can be reviewed later.

  You must add code to the script to record the unique label values as run metrics at the point indicated by the comment.

  Solution: Replace the comment with the following code:

  for label_val in label_vals:

  run.

  log('Label Values', label_val) Does the solution meet the goal?

  A. Yes
  B. No
  A. Yes

  ---

  Explanation

  The run_log function is used to log the contents in label_vals:

  for label_val in label_vals:

  run.log('Label Values', label_val)

  https://www.element61.be/en/resource/azure-machine-learning-services-complete-toolbox-ai

• Question 585:

  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 manage an Azure Machine Learning workspace. The Python script named script.py reads an argument named training_data. The training_data argument specifies the path to the training data in a file named dataset1.csv.

  You plan to run the script.py Python script as a command job that trains a machine learning model.

  You need to provide the command to pass the path for the dataset as a parameter value when you submit the script as a training job.

  Solution: python script.py dataset1.

  csv Does the solution meet the goal?

  A. Yes
  B. No
  B. No

• Question 586:

  HOTSPOT

  You manage an Azure OpenAI Service deployment of the gpt-4o-mini base model.

  You plan to fine-tune the deployed model by using OpenAI Python code.

  In the code, you import all required Python libraries and create a sample training dataset.

  You need to complete the next section of the code to estimate the cost of fine-tuning by using the sample training dataset.

  How should you complete the code section? To answer, select the appropriate options in the answer area.

  NOTE: Each correct selection is worth one point.

  

  

• Question 587:

  You have an Azure subscription named Sub1 that contains:

  1. A registered MLflow model named Model1

  2. An online endpoint named Endpoint1

  Outbound network connectivity from Endpoint1 is blocked.

  You need to deploy Model1 to Endpoint1.

  What should you do first?

  A. In Workspace1, create a linked service
  B. In Sub1, create an Azure Machine Learning registry.
  C. In Workspace1, create a package.
  D. In Workspace1, create a package.
  E. In Sub1, create a private endpoint.
  E. In Sub1, create a private endpoint.

• Question 588:

  HOTSPOT

  You design a data processing strategy for a machine learning project.

  The data that must be processed includes unstructured flat files that must be processed in real time.

  The data transformation must be executed on a serverless compute and optimized for big data analytical workloads.

  You need to select the Azure services for the data science team.

  Which storage and data processing service should you use? To answer, select the appropriate option in the answer area.

  NOTE: Each correct selection is worth one point.

  

  

• Question 589:

  You are training machine learning models in Azure Machine Learning. You use Hyperdrive to tune the hyperparameter.

  In previous model training and tuning runs, many models showed similar performance.

  You need to select an early termination policy that meets the following requirements:

  1. accounts for the performance of all previous runs when evaluating the current run

  2. avoids comparing the current run with only the best performing run to date

  Which two early termination policies should you use? Each correct answer presents part of the solution.

  NOTE: Each correct selection is worth one point.

  A. Median stopping
  B. Bandit
  C. Default
  D. Truncation selection
  A. Median stopping
  D. Truncation selection

  ---

  Explanation

  The Median Stopping policy computes running averages across all runs and cancels runs whose best performance is worse than the median of the running averages.

  If no policy is specified, the hyperparameter tuning service will let all training runs execute to completion.

  https://docs.microsoft.com/en-us/python/api/azureml-train-core/azureml.train.hyperdrive.medianstoppingpolicy

  https://docs.microsoft.com/en-us/python/api/azureml-train-core/azureml.train.hyperdrive.truncationselectionpolicy

  https://docs.microsoft.com/en-us/python/api/azureml-train-core/azureml.train.hyperdrive.banditpolicy

• Question 590:

  HOTSPOT

  You are performing sentiment analysis using a CSV file that includes 12.0O0 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 text dictionary from the customer review text and set the maximum n-gram size to trigrams.

  You need to configure the Extract N Gram features from Text module.

  What should you select? To answer, select the appropriate options in the answer area;

  NOTE: Each correct selection is worth one point.