Microsoft DP-100 Online Practice Questions and Exam Preparation

Microsoft DP-100 Online Questions & Answers

• Question 201:

  You have been tasked with employing a machine learning model, which makes use of a PostgreSQL database and needs GPU processing, to forecast prices.

  You are preparing to create a virtual machine that has the necessary tools built into it.

  You need to make use of the correct virtual machine type.

  Recommendation: You make use of a Deep Learning Virtual Machine (DLVM) Windows edition.

  Will the requirements be satisfied?

  A. Yes
  B. No
  A. Yes

  ---

  Explanation

  DLVM is a template on top of DSVM image. In terms of the packages, GPU drivers etc are all there in the DSVM image. Mostly it is for convenience during creation where we only allow DLVM to be created on GPU VM instances on Azure.

  https://docs.microsoft.com/en-us/azure/machine-learning/data-science-virtual-machine/overview

• Question 202:

  HOTSPOT

  You are tuning a hyperparameter for an algorithm. The following table shows a data set with different hyperparameter, training error, and validation errors.

  

  Use the drop-down menus to select the answer choice that answers each question based on the information presented in the graphic.

  

  

  ---

  Box 1: 4

  Choose the one which has lower training and validation error and also the closest match.

  Minimize variance (difference between validation error and train error).

  Box 2: 5

  Minimize variance (difference between validation error and train error).

  https://medium.com/comet-ml/organizing-machine-learning-projects-project-management-guidelines-2d2b85651bbd

• Question 203:

  You are in the process of creating a machine learning model. Your dataset includes rows with null and missing values.

  You plan to make use of the Clean Missing Data module in Azure Machine Learning Studio to detect and fix the null and missing values in the dataset.

  Recommendation: You make use of the Remove entire row option.

  Will the requirements be satisfied?

  A. Yes
  B. No
  A. Yes

  ---

  Explanation

  Remove entire row: Completely removes any row in the dataset that has one or more missing values. This is useful if the missing value can be considered randomly missing.

  https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/clean-missing-data

• Question 204:

  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 205:

  HOTSPOT

  You create a list of movie descriptions in text data format.

  You must analyze the movie descriptions with automated machine learning.

  You need to use the Azure Machine Learning for Python SDK v1 to configure a job with the specific natural language processing (NLP) task function for AutoML jobs.

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

  NOTE: Each correct selection is worth one point.

  

  

  ---

  Explanation:

  Box 1: text_classification()

  Select your NLP task

  Determine what NLP task you want to accomplish. Currently, automated ML supports the follow deep neural network NLP tasks.

  * Multi-class text classification

  CLI v2: text_classification

  SDK v2: text_classification()

  There are multiple possible classes and each sample can be classified as exactly one class. The task is to predict the correct class for each sample.

  For example, classifying a movie script as "Comedy" or "Romantic".

  Box 2: text_classification_multilabel()

  * Multi-label text classification

  CLI v2: text_classification_multilabel SDK v2: text_classification_multilabel()

  There are multiple possible classes and each sample can be assigned any number of classes. The task is to predict all the classes for each sample

  For example, classifying a movie script as "Comedy", or "Romantic", or "Comedy and Romantic".

  Box 3: text_ner()

  * Named Entity Recognition (NER)

  LI v2:text_ner

  SDK v2: text_ner()

  There are multiple possible tags for tokens in sequences. The task is to predict the tags for all the tokens for each sequence.

  For example, extracting domain-specific entities from unstructured text, such as contracts or financial documents.

  https://learn.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-nlp-models

• Question 206:

  You create an Azure Machine Learning workspace named workspace1. The workspace contains a Python SDK v2 notebook that uses MLflow to collect model training metrics and artifacts from your local computer.

  You must reuse the notebook to run on Azure Machine Learning compute instance in workspace1.

  You need to continue to log metrics and artifacts from your data science code.

  What should you do?

  A. Instantiate the job class.
  B. Instantiate the MLCIient class.
  C. Log in to workspace1.
  D. Configure the tracking URL.
  D. Configure the tracking URL.

  ---

  Explanation

  Track runs from your local machine or remote compute.

  Tracking using MLflow with Azure Machine Learning lets you store the logged metrics and artifacts runs that were executed on your local machine into your Azure Machine Learning workspace.

  Set up tracking environment

  To track a run that is not running on Azure Machine Learning compute (from now on referred to as "local compute"), you need to point your local compute to the Azure Machine Learning MLflow Tracking URI.

  Note:

  You can get the Azure Machine Learning MLflow tracking URI using the Azure Machine Learning SDK v1 for Python. Ensure you have the library azureml-sdk installed in the cluster you are using. The following sample gets the unique MLFLow tracking URI associated with your workspace. Then the method set_tracking_uri() points the MLflow tracking URI to that URI.

  1. Using the workspace configuration file:

  ws = Workspace.from_config() mlflow.set_tracking_uri(ws.get_mlflow_tracking_uri())

  2. Using the subscription ID, resource group name and workspace name:

  from azureml.core import Workspace

  import mlflow

  #Enter details of your Azure Machine Learning workspace subscription_id = '<SUBSCRIPTION_ID>' resource_group = '<RESOURCE_GROUP>' workspace_name = '<AZUREML_WORKSPACE_NAME>'

  ws = Workspace.get(name=workspace_name, subscription_id=subscription_id, resource_group=resource_group) mlflow.set_tracking_uri(ws.get_mlflow_tracking_uri())

  https://learn.microsoft.com/en-us/azure/machine-learning/v1/how-to-use-mlflow

• Question 207:

  DRAG DROP

  You are building an intelligent solution using machine learning models.

  The environment must support the following requirements:

  Data scientists must build notebooks in a cloud environment

  Data scientists must use automatic feature engineering and model building in machine learning pipelines.

  Notebooks must be deployed to retrain using Spark instances with dynamic worker allocation.

  Notebooks must be exportable to be version controlled locally.

  You need to create the environment.

  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:

  

  

  ---

  Step 1: Create an Azure HDInsight cluster to include the Apache Spark Mlib library

  Step 2: Install Microsot Machine Learning for Apache Spark

  You install AzureML on your Azure HDInsight cluster.

  Microsoft Machine Learning for Apache Spark (MMLSpark) provides a number of deep learning and data science tools for Apache Spark, including seamless integration of Spark Machine Learning pipelines with Microsoft Cognitive Toolkit (CNTK) and OpenCV, enabling you to quickly create powerful, highly-scalable predictive and analytical models for large image and text datasets.

  Step 3: Create and execute the Zeppelin notebooks on the cluster

  Step 4: When the cluster is ready, export Zeppelin notebooks to a local environment.

  Notebooks must be exportable to be version controlled locally.

  References:

  https://docs.microsoft.com/en-us/azure/hdinsight/spark/apache-spark-zeppelin-notebook

  https://azuremlbuild.blob.core.windows.net/pysparkapi/intro.html

• Question 208:

  You need to evaluate the potential risk of exposing personal information based on the values of epsilon and delta for differential privacy. You create a privacy report.

  What does an epsilon value greater than one represent?

  A. The privacy of data is preserved and there is limited impact on data accuracy.
  B. There is a high risk of exposing the actual data that is uses to generate the report.
  C. The data used in the report is very noisy.
  B. There is a high risk of exposing the actual data that is uses to generate the report.

  ---

  Explanation

  Epsilon: Put simplistically, epsilon is a non-negative value that provides an inverse measure of the amount of noise added to the data. A low epsilon results in a dataset with a greater level of privacy, while a high epsilon results in a dataset that is closer to the original data. Generally, you should use epsilon values between 0 and 1. Epsilon is correlated with another value named delta, that indicates the probability that a report generated by an analysis is not fully private.

  https://github.com/MicrosoftLearning/mslearn-dp100/blob/main/13%20-%20Explore%20Differential%20Privacy.ipynb

• Question 209:

  HOTSPOT

  You build a data pipeline in an Azure Machine Learning workspace by using the Azure Machine Learning SDK for Python. You create a data preparation step in the data pipeline.

  You create the following code fragment in Python:

  

  For each of the following statements, select Yes if the statement is true. Otherwise, select No.

  NOTE: Each correct selection is worth one point.

  

  

  ---

  Explanation:

  Box 1: Yes

  The PythonScriptStep Class creates an Azure ML Pipeline step that runs Python script.

  Constructor: PythonScriptStep

  Parameters include:

  * compute_target

  The compute target to use. If unspecified, the target from the runconfig will be used. This parameter may be specified as a compute target object or the string name of a compute target on the workspace.

  * runconfig

  The optional RunConfiguration to use. A RunConfiguration can be used to specify additional requirements for the run, such as conda dependencies and a docker image. If unspecified, a default runconfig will be created.

  Box 2: No

  We see allow_reuse=True.

  Note: allow_reuse bool default value: True

  Indicates whether the step should reuse previous results when re-run with the same settings. Reuse is enabled by default. If the step contents (scripts/dependencies) as well as inputs and parameters remain unchanged, the output from the previous run of this step is reused. When reusing the step, instead of submitting the job to compute, the results from the previous run are immediately made available to any subsequent steps. If you use Azure Machine Learning datasets as inputs, reuse is determined by whether the dataset's definition has changed, not by whether the underlying data has changed.

  Box 3: Yes

  We have arguments="--input," .. , "--output"

  Arguments list[str] default value: None

  Command line arguments for the Python script file.

  https://learn.microsoft.com/en-us/python/api/azureml-pipeline-steps/azureml.pipeline.steps.python_script_step.pythonscriptstep?view=azure-ml-py

• Question 210:

  You are reviewing model benchmarks in Azure AI Foundry.

  You must use an embedding model that can assess rank-order relevance based on cosine similarity.

  You need to select the applicable embedding model metric.

  Which metric should you focus on?

  A. V-measure
  B. Mean average precision
  C. F1 score
  D. Spearman correlation
  B. Mean average precision