Microsoft DP-100 Online Practice Questions and Exam Preparation

Microsoft DP-100 Online Questions & Answers

• Question 321:

  You create an Azure Machine Learning managed compute resource. The compute resource is configured as follows:

  Minimum nodes: 2

  Maximum nodes: 4

  You must decrease the minimum number of nodes and increase the maximum number of nodes to the following values:

  Minimum nodes: 0

  Maximum nodes: 8

  You need to reconfigure the compute resource.

  Which three methods can you use? Each correct answer presents a complete solution.

  NOTE: Each correct selection is worth one point.

  A. Azure Machine Learning designer
  B. MLClient class in Python SDK v2
  C. Azure Machine Learning studio
  D. Azure CLI ml extension v2
  E. BuildContext class in Python SDK v2
  B. MLClient class in Python SDK v2
  C. Azure Machine Learning studio
  D. Azure CLI ml extension v2

  ---

  Explanation

  https://github.com/MicrosoftDocs/azure-docs/blob/master/articles/machine-learning/how-to-setup-authentication.md

  https://learn.microsoft.com/en-us/python/api/azure-ai-ml/azure.ai.ml.entities.buildcontext

• Question 322:

  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 have a Python script named train.py in a local folder named scripts. The script trains a regression model by using scikit-learn. The script includes code to load a training data file which is also located in the scripts folder.

  You must run the script as an Azure ML experiment on a compute cluster named aml-compute.

  You need to configure the run to ensure that the environment includes the required packages for model training. You have instantiated a variable named aml-compute that references the target compute cluster.

  Solution: Run the following code:

  

  Does the solution meet the goal?

  A. Yes
  B. No
  A. Yes

• Question 323:

  HOTSPOT

  You manage an Azure Machine Learning workspace.

  You plan to train a natural language processing (NLP) model that will assign labels for designated tokens in unstructured text.

  You need to configure the NLP task by using automated machine learning.

  Which configuration values should you use? To answer, select the appropriate options in the answer area.

  NOTE: Each correct selection is worth one point.

  

  

• Question 324:

  You manage an Azure Machine Learning workspace named workspaces

  You must develop Python SDK v2 code to attach an Azure Synapse Spark pool as a compute target in workspaces.

  The code must invoke the constructor of the SynapseSparkCompute class.

  You need to invoke the constructor.

  What should you use?

  A. Synapse workspace web URL and Spark pool name
  B. resource ID of the Synapse Spark pool and a user-defined name
  C. pool URL of the Synapse Spark pool and a system-assigned name
  D. Synapse workspace name and workspace web URL
  B. resource ID of the Synapse Spark pool and a user-defined name

• Question 325:

  HOTSPOT

  You are implementing hyperparameter tuning for a model training from a notebook. The notebook is in an Azure Machine Learning workspace. You add code that imports all relevant Python libraries.

  You must configure Bayesian sampling over the search space for the num_hidden_layers and batch_size hyperparameters.

  You need to complete the following Python code to configure Bayesian sampling.

  Which code segments should you use? To answer, select the appropriate options in the answer area.

  NOTE: Each correct selection is worth one point.

  

  

  ---

  Explanation:

  Box 1: choice

  Discrete hyperparameters

  Discrete hyperparameters are specified as a Choice among discrete values. Choice can be:

  one or more comma-separated values a range object any arbitrary list object

  Example:

  from azure.ai.ml.sweep import Choice command_job_for_sweep = command_job( batch_size=Choice(values=[16, 32, 64, 128]), number_of_hidden_layers=Choice(values=range(1,5)), )

  In this case, batch_size one of the values [16, 32, 64, 128] and number_of_hidden_layers takes one of the values [1, 2, 3, 4].

  Bayesian sampling only supports choice, uniform, and quniform distributions over the search space.

  Note: The following advanced discrete hyperparameters can also be specified using a distribution:

  QUniform(min_value, max_value, q) - Returns a value like round(Uniform(min_value, max_value) / q) * q

  QLogUniform(min_value, max_value, q) - Returns a value like round(exp(Uniform(min_value, max_value)) / q) * q QNormal(mu, sigma, q) - Returns a value like round(Normal(mu, sigma) / q) * q

  QLogNormal(mu, sigma, q) - Returns a value like round(exp(Normal(mu, sigma)) / q) * q

  Box 2: range

  Incorrect:

  Continuous hyperparameters

  The Continuous hyperparameters are specified as a distribution over a continuous range of values:

  Uniform(min_value, max_value) - Returns a value uniformly distributed between min_value and max_value

  LogUniform(min_value, max_value) - Returns a value drawn according to exp(Uniform(min_value, max_value)) so that the logarithm of the return value is uniformly distributed

  LogNormal(mu, sigma) - Returns a value drawn according to exp(Normal(mu, sigma)) so that the logarithm of the return value is normally distributed

  https://learn.microsoft.com/en-us/azure/machine-learning/how-to-tune-hyperparameters

• Question 326:

  You are performing clustering by using the K-means algorithm.

  You need to define the possible termination conditions.

  Which three conditions can you use? Each correct answer presents a complete solution.

  NOTE: Each correct selection is worth one point.

  A. Centroids do not change between iterations.
  B. The residual sum of squares (RSS) rises above a threshold.
  C. The residual sum of squares (RSS) falls below a threshold.
  D. A fixed number of iterations is executed.
  E. The sum of distances between centroids reaches a maximum.
  A. Centroids do not change between iterations.
  C. The residual sum of squares (RSS) falls below a threshold.
  D. A fixed number of iterations is executed.

  ---

  Explanation

  AD: The algorithm terminates when the centroids stabilize or when a specified number of iterations are completed.

  C: A measure of how well the centroids represent the members of their clusters is the residual sum of squares or RSS, the squared distance of each vector from its centroid summed over all vectors. RSS is the objective function and our goal is to minimize it.

  https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/k-means-clustering

  https://nlp.stanford.edu/IR-book/html/htmledition/k-means-1.html

• Question 327:

  DRAG DROP

  You create a training pipeline using the Azure Machine Learning designer. You upload a CSV file that contains the data from which you want to train your model.

  You need to use the designer to create a pipeline that includes steps to perform the following tasks:

  1. Select the training features using the pandas filter method.

  2. Train a model based on the naive_bayes.GaussianNB algorithm.

  3. Return only the Scored Labels column by using the query <pdf2txt-u>SELECT [Scored Labels] FROM t1</pdf2txt-u>;

  Which modules should you use? To answer, drag the appropriate modules to the appropriate locations. Each module name 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.

  Select and Place:

  

  

• Question 328:

  You are analyzing a dataset containing historical data from a local taxi company. You are developing a regression model.

  You must predict the fare of a taxi trip.

  You need to select performance metrics to correctly evaluate the regression model.

  Which two metrics can you use? Each correct answer presents a complete solution?

  NOTE: Each correct selection is worth one point.

  A. a Root Mean Square Error value that is low
  B. an R-Squared value close to 0
  C. an F1 score that is low
  D. an R-Squared value close to 1
  E. an F1 score that is high
  F. a Root Mean Square Error value that is high
  A. a Root Mean Square Error value that is low
  D. an R-Squared value close to 1

  ---

  Explanation

  RMSE and R2 are both metrics for regression models.

  A: Root mean squared error (RMSE) creates a single value that summarizes the error in the model. By squaring the difference, the metric disregards the difference between over-prediction and under-prediction.

  D: Coefficient of determination, often referred to as R2, represents the predictive power of the model as a value between 0 and 1. Zero means the model is random (explains nothing); 1 means there is a perfect fit. However, caution should be used in interpreting R2 values, as low values can be entirely normal and high values can be suspect.

  Incorrect Answers:

  C, E: F-score is used for classification models, not for regression models.

  https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/evaluate-model

• Question 329:

  You have an Azure Machine Learning workspace named Workspace1. Workspace1 has a registered Mlflow model named model1 with PyFunc flavor.

  You plan to deploy model1 to an online endpoint named endpoint1 without egress connectivity by using Azure Machine Learning Python SDK v2.

  You have the following code:

  

  You need to add a parameter to the ManagedOnlineDeployment object to ensure the model deploys successfully.

  Solution: Add the environment parameter.

  Does the solution meet the goal?

  A. Yes
  B. No
  B. No

• Question 330:

  HOTSPOT

  You create an Azure Machine Learning workspace. You use the Azure Machine Learning SDK for Python.

  You must create a dataset from remote paths. The dataset must be reusable within the workspace.

  You need to create the dataset.

  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.

  

  

  ---

  Explanation:

  Box 1: Tabular.from_delimited_files

  A TabularDataset can be created from CSV, TSV, Parquet files, or SQL query using the from_* methods of the TabularDatasetFactory class.

  Use the from_delimited_files() method on the TabularDatasetFactory class to read files in .csv or .tsv format, and to create an unregistered TabularDataset. To read in files from .parquet format, use the from_parquet_files() method.

  Box 2: register

  Register datasets

  To complete the creation process, register your datasets with a workspace. Use the register() method to register datasets with your workspace in order to share them with others and reuse them across experiments in your workspace:

  titanic_ds = titanic_ds.register(workspace=workspace, name='titanic_ds', description='titanic training data')

  https://learn.microsoft.com/en-us/python/api/azureml-core/azureml.data.tabular_dataset.tabulardataset

  https://learn.microsoft.com/en-us/azure/machine-learning/v1/how-to-create-register-datasets