Microsoft DP-100 Online Practice Questions and Exam Preparation

Microsoft DP-100 Online Questions & Answers

• Question 341:

  You use the Azure Machine Learning Python SDK to create a batch inference pipeline.

  You must publish the batch inference pipeline so that business groups in your organization can use the pipeline. Each business group must be able to specify a different location for the data that the pipeline submits to the model for scoring.

  You need to publish the pipeline.

  What should you do?

  A. Create multiple endpoints for the published pipeline service and have each business group submit jobs to its own endpoint.
  B. Define a PipelineParameter object for the pipeline and use it to specify the business group-specific input dataset for each pipeline run.
  C. Define a OutputFileDatasetConfig object for the pipeline and use the object to specify the business group-specific input dataset for each pipeline run.
  D. Have each business group run the pipeline on local compute and use a local file for the input data.
  B. Define a PipelineParameter object for the pipeline and use it to specify the business group-specific input dataset for each pipeline run.

• Question 342:

  HOTSPOT

  You load data from a notebook in an Azure Machine Learning workspace into a pandas dataframe. The data contains 10,000 records. Each record consists of 10 columns.

  You must identify the number of missing values in each of the columns.

  You need to complete the Python code that will return the number of missing values in each of the columns.

  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: values

  pandas.DataFrame.values

  property DataFrame.values[source]

  Return a Numpy representation of the DataFrame.

  Warning

  We recommend using DataFrame.to_numpy() instead.

  Only the values in the DataFrame will be returned, the axes labels will be removed.

  Incorrect:

  * index

  pandas.DataFrame.index

  DataFrame.index

  The index (row labels) of the DataFrame.

  * shape

  pandas.DataFrame.shape

  property DataFrame.shape[source]

  Return a tuple representing the dimensionality of the DataFrame.

  See also

  ndarray.shape

  Tuple of array dimensions.

  Box 2: 10

  pandas.DataFrame.count

  DataFrame.count(axis=0, numeric_only=False)[source] Count non-NA cells for each column or row.

  The values None, NaN, NaT, and optionally numpy.inf (depending on pandas.options.mode.use_inf_as_na) are considered NA.

  https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.values.html

• Question 343:

  You are designing a training job in an Azure Machine Learning workspace by using Automated ML.

  During training, the compute resource must scale up to handle larger datasets.

  You need to select the compute resource that has a multi-node cluster that automatically scales.

  Which Azure Machine Learning compute target should you use?

  A. Compute instance
  B. Endpoints
  C. Kubernetes cluster
  D. Serverless compute
  D. Serverless compute

• Question 344:

  You manage an Azure AI Foundry project.

  You develop a Prompt flow that includes a large language model (LLM) node and an upstream node with a single output.

  You need to link the LLM node input with the output of the upstream node by using a YAML flow configuration.

  Which flow configuration should you use?

  A. $(upstream_node_name.output)
  B. <#upstream_node_name.output#>
  C. (% upstream_node_name.output %)
  D. {{upstream.node.name.output}}
  B. <#upstream_node_name.output#>

• Question 345:

  DRAG DROP

  You are producing a multiple linear regression model in Azure Machine Learning Studio.

  Several independent variables are highly correlated.

  You need to select appropriate methods for conducting effective feature engineering on all the data.

  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:

  

  

  ---

  Step 1: Use the Filter Based Feature Selection module

  Filter Based Feature Selection identifies the features in a dataset with the greatest predictive power.

  The module outputs a dataset that contains the best feature columns, as ranked by predictive power. It also outputs the names of the features and their scores from the selected metric.

  Step 2: Build a counting transform

  A counting transform creates a transformation that turns count tables into features, so that you can apply the transformation to multiple datasets.

  Step 3: Test the hypothesis using t-Test

  References:

  https://docs.microsoft.com/bs-latn-ba/azure/machine-learning/studio-module-reference/filter-based-feature-selection

  https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/build-counting-transform

• Question 346:

  You have been tasked with constructing a machine learning model that translates language text into a different language text.

  The machine learning model must be constructed and trained to learn the sequence of the.

  Recommendation: You make use of Convolutional Neural Networks (CNNs).

  Will the requirements be satisfied?

  A. Yes
  B. No
  B. No

• Question 347:

  HOTSPOT

  You manage an Azure Machine Learning workspace.

  You must define the execution environments for your jobs and encapsulate the dependencies for your code.

  You need to configure the environment from a Docker build context.

  How should you complete the code segment? To answer, select the appropriate option in the answer area.

  NOTE: Each correct selection is worth one point.

  

  

  ---

  Explanation:

  Manage Azure Machine Learning environments with the CLI & SDK (v2)

  Azure Machine Learning environments define the execution environments for your jobs or deployments and encapsulate the dependencies for your code. Azure Machine Learning uses the environment specification to create the Docker container that your training or scoring code runs in on the specified compute target. You can define an environment from a conda specification, Docker image, or Docker build context.

  Box 1: Environment

  Python SDK

  In the following example, the local path to the build context folder is specified in the path' parameter. Azure Machine Learning will look for a Dockerfile named Dockerfile` at the root of the build context.

  env_docker_context = Environment( build=BuildContext(path="docker-contexts/python-and-pip"), name="docker-context-example", description="Environment created from a Docker context.", ) ml_client.environments.create_or_update(env_docker_context)

  Azure Machine Learning will start building the image from the build context when the environment is created. You can monitor the status of the build and view the build logs in the studio UI.

  Box 2: build

  https://learn.microsoft.com/en-us/azure/machine-learning/how-to-manage-environments-v2

• Question 348:

  You are authoring a notebook in Azure Machine Learning studio.

  You must install packages from the notebook into the currently running kernel. The installation must be limited to the currently running kernel only.

  You need to install the packages.

  Which magic function should you use?

  A. !pip
  B. %pip
  C. !conda
  D. %load
  B. %pip

  ---

  Explanation

  Manage packages

  Since your compute instance has multiple kernels, make sure use %pip or %conda magic functions, which install packages into the currently running kernel. Don't use !pip or !conda, which refers to all packages (including packages outside the currently running kernel).

  https://learn.microsoft.com/en-us/azure/machine-learning/how-to-run-jupyter-notebooks

• Question 349:

  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 the following Azure subscriptions and Azure Machine Learning service workspaces:

  

  You need to obtain a reference to the ml-project workspace.

  Solution: Run the following Python code:

  

  Does the solution meet the goal?

  A. Yes
  B. No
  B. No

• Question 350:

  HOTSPOT

  You create an Azure Databricks workspace and a linked Azure Machine Learning workspace.

  You have the following Python code segment in the Azure Machine Learning workspace:

  import mlflow

  import mlflow.azureml

  import azureml.mlflow

  import azureml.core

  from azureml.core import Workspace

  subscription_id = 'subscription_id'

  resourse_group = 'resource_group_name'

  workspace_name = 'workspace_name'

  ws = Workspace.get(name=workspace_name,

      subscription_id=subscription_id,

      resource_group=resource_group)

  experimentName = "/Users/{user_name}/{experiment_folder}/{experiment_name}"

  mlflow.set_experiment(experimentName)

  uri = ws.get_mlflow_tracking_uri()

  mlflow.set_tracking_uri(uri)

  Instructions: 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: No

  The Workspace.get method loads an existing workspace without using configuration files.

  ws = Workspace.get(name="myworkspace", subscription_id='<azure-subscription-id>', resource_group='myresourcegroup')

  Box 2: Yes

  MLflow Tracking with Azure Machine Learning lets you store the logged metrics and artifacts from your local runs into your Azure Machine Learning workspace.

  The get_mlflow_tracking_uri() method assigns a unique tracking URI address to the workspace, ws, and set_tracking_uri() points the MLflow tracking URI to that address.

  Box 3: Yes

  Note: In Deep Learning, epoch means the total dataset is passed forward and backward in a neural network once.

  
  

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

  https://docs.microsoft.com/en-us/azure/machine-learning/how-to-use-mlflow