Microsoft DP-100 Online Practice Questions and Exam Preparation

Microsoft DP-100 Online Questions & Answers

• Question 221:

  You are making use of the Azure Machine Learning to designer construct an experiment.

  After dividing a dataset into training and testing sets, you configure the algorithm to be Two-Class Boosted Decision Tree.

  You are preparing to ascertain the Area Under the Curve (AUC).

  Which of the following is a sequential combination of the models required to achieve your goal?

  A. Train, Score, Evaluate.
  B. Score, Evaluate, Train.
  C. Evaluate, Export Data, Train.
  D. Train, Score, Export Data.
  A. Train, Score, Evaluate.

• Question 222:

  HOTSPOT

  You manage an Azure Machine Learning workspace. You configure an automated machine learning regression training job by using the Azure Machine Learning Python SDK v2.

  You configure the regression job by using the following script:

  

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

  

  

  ---

  Explanation:

  Box 1: No

  enable_early_termination is set to TRUE, but max_trials is not defined.

  Exit criteria

  There are a few options you can define in the set_limits() function to end your experiment prior to job completion.

  * enable_early_termination

  Whether to end the job if the score is not improving in the short term.

  * max_trials

  The maximum number of trials/runs each with a different combination of algorithm and hyperparameters to try during an AutoML job. If not specified, the default is 1000 trials. If using enable_early_termination the number of trials used can be smaller.

  Box 2: Yes

  * max_concurrent_trials is here set to 5.

  Represents the maximum number of trials (children jobs) that would be executed in parallel. It's a good practice to match this number with the number of nodes your cluster

  Box 3: Yes

  * trial_timeout_minutes is here set to 60.

  Maximum time in minutes that each trial (child job) can run for before it terminates. If not specified, a value of 1 month or 43200 minutes is used

  Box 4: No

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

• Question 223:

  You are reviewing model benchmarks in Azure Al Foundry.

  You must use a large language model based on the proficiency of the model to generate the most linguistically correct text.

  You need to select the model benchmark.

  Which benchmark metric should you focus on?

  A. fluency
  B. coherence
  C. precision
  D. accuracy
  A. fluency

  ---

  
  

• Question 224:

  DRAG DROP

  You have an Azure Machine Learning workspace that contains a CPU-based compute cluster and an Azure Kubernetes Services (AKS) inference cluster. You create a tabular dataset containing data that you plan to use to create a classification model.

  You need to use the Azure Machine Learning designer to create a web service through which client applications can consume the classification model by submitting new data and getting an immediate prediction as a response.

  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: Create and start a Compute Instance

  To train and deploy models using Azure Machine Learning designer, you need compute on which to run the training process, test the model, and host the model in a deployed service.

  There are four kinds of compute resource you can create:

  Compute Instances: Development workstations that data scientists can use to work with data and models.

  Compute Clusters: Scalable clusters of virtual machines for on-demand processing of experiment code.

  Inference Clusters: Deployment targets for predictive services that use your trained models.

  Attached Compute: Links to existing Azure compute resources, such as Virtual Machines or Azure Databricks clusters.

  Step 2: Create and run a training pipeline..

  After you've used data transformations to prepare the data, you can use it to train a machine learning model. Create and run a training pipeline

  Step 3: Create and run a real-time inference pipeline

  After creating and running a pipeline to train the model, you need a second pipeline that performs the same data transformations for new data, and then uses the trained model to inference (in other words, predict) label values based on its features. This pipeline will form the basis for a predictive service that you can publish for applications to use.

  https://docs.microsoft.com/en-us/learn/modules/create-classification-model-azure-machine-learning-designer/

• Question 225:

  You use the Two-Class Neural Network module in Azure Machine Learning Studio to build a binary classification model. You use the Tune Model Hyperparameters module to tune accuracy for the model.

  You need to configure the Tune Model Hyperparameters module.

  Which two values should you use? Each correct answer presents part of the solution.

  NOTE: Each correct selection is worth one point.

  A. Number of hidden nodes
  B. Learning Rate
  C. The type of the normalizer
  D. Number of learning iterations
  E. Hidden layer specification
  B. Learning Rate
  D. Number of learning iterations

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  Explanation

  https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/two-class-neural-

  network

• Question 226:

  You plan to create a speech recognition deep learning model.

  The model must support the latest version of Python.

  You need to recommend a deep learning framework for speech recognition to include in the Data Science Virtual Machine (DSVM).

  What should you recommend?

  A. Rattle
  B. TensorFlow
  C. Weka
  D. Scikit-learn
  B. TensorFlow

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  Explanation

  TensorFlow is an open source library for numerical computation and large-scale machine learning. It uses Python to provide a convenient front-end API for building applications with the framework TensorFlow can train and run deep neural networks for handwritten digit classification, image recognition, word embeddings, recurrent neural networks, sequence-to-sequence models for machine translation, natural language processing, and PDE (partial differential equation) based simulations.

  Incorrect Answers:

  A: Rattle is the R analytical tool that gets you started with data analytics and machine learning.

  C: Weka is used for visual data mining and machine learning software in Java.

  D: Scikit-learn is one of the most useful library for machine learning in Python. It is on NumPy, SciPy and matplotlib, this library contains a lot of effiecient tools for machine learning and statistical modeling including classification, regression, clustering and dimensionality reduction.

  https://www.infoworld.com/article/3278008/what-is-tensorflow-the-machine-learning-library-explained.html

• Question 227:

  HOTSPOT

  You manage an Azure AI Foundry project.

  You plan to create a search index by using the Azure AI Foundry SDK.

  You need to configure the Content field as a prioritized field for semantic ranking. The field is already set to be searchable.

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

  NOTE: Each correct selection is worth one point.

  

  

• Question 228:

  You use the Azure Machine Learning designer to create and run a training pipeline. You then create a real-time inference pipeline.

  You must deploy the real-time inference pipeline as a web service.

  What must you do before you deploy the real-time inference pipeline?

  A. Run the real-time inference pipeline.
  B. Create a batch inference pipeline.
  C. Clone the training pipeline.
  D. Create an Azure Machine Learning compute cluster.
  A. Run the real-time inference pipeline.

• Question 229:

  HOTSPOT

  You train a machine learning model by using Azure Machine Learning.

  You use the following training script in Python to log an accuracy value:

  

  You must use a Python script to define a sweep job.

  You need to provide the primary metric and goal you want hyperparameter tuning to optimize.

  How should you complete the Python script? To answer, select the appropriate options in the answer area.

  NOTE: Each correct selection is worth one point.

  

  

• Question 230:

  You train and register an Azure Machine Learning model.

  You plan to deploy the model to an online endpoint.

  You need to ensure that applications will be able to use the authentication method with a non-expiring artifact to access the model.

  Solution: Create a Kubernetes online endpoint and set the value of its auth_mode parameter to aml_token. Deploy the model to the online endpoint.

  Does the solution meet the goal?

  A. Yes
  B. No
  B. No

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  Explanation

  Correct Solution: Create a managed online endpoint and set the value of its auth_mode parameter to key. Deploy the model to the online endpoint.

  Authentication mode: The authentication method for the endpoint. Choose between key-based authentication and Azure Machine Learning token-based authentication. A key doesn't expire, but a token does expire.

  https://learn.microsoft.com/en-us/azure/machine-learning/how-to-deploy-online-endpoints