Microsoft DP-100 Online Practice Questions and Exam Preparation

Microsoft DP-100 Online Questions & Answers

• Question 481:

  You have been tasked with creating a new Azure pipeline via the Machine Learning designer.

  You have to makes sure that the pipeline trains a model using data in a comma-separated values (CSV) file that is published on a website. A dataset for the file for this file does not exist.

  Data from the CSV file must be ingested into the designer pipeline with the least amount of administrative effort as possible.

  Which of the following actions should you take?

  A. You should make use of the Convert to TXT module.
  B. You should add the Copy Data object to the pipeline.
  C. You should add the Import Data object to the pipeline.
  D. You should add the Dataset object to the pipeline.
  C. You should add the Import Data object to the pipeline.

• Question 482:

  HOTSPOT

  You have a feature set containing the following numerical features: X, Y, and Z.

  The Poisson correlation coefficient (r-value) of X, Y, and Z features is shown in the following image:

  

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

  NOTE: Each correct selection is worth one point.

  

  

  ---

  Box 1: 0.859122

  Box 2: a positively linear relationship

  +1 indicates a strong positive linear relationship

  -1 indicates a strong negative linear correlation 0 denotes no linear relationship between the two variables.

  References:

  https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/compute-linear-correlation

• Question 483:

  You are building a machine learning model for translating English language textual content into French language textual content.

  You need to build and train the machine learning model to learn the sequence of the textual content.

  Which type of neural network should you use?

  A. Multilayer Perceptions (MLPs)
  B. Convolutional Neural Networks (CNNs)
  C. Recurrent Neural Networks (RNNs)
  D. Generative Adversarial Networks (GANs)
  C. Recurrent Neural Networks (RNNs)

  ---

  Explanation

  To translate a corpus of English text to French, we need to build a recurrent neural network (RNN).

  Note: RNNs are designed to take sequences of text as inputs or return sequences of text as outputs, or both. They're called recurrent because the network's hidden layers have a loop in which the output and cell state from each time step become inputs at the next time step. This recurrence serves as a form of memory. It allows contextual information to flow through the network so that relevant outputs from previous time steps can be applied to network operations at the current time step.

  https://towardsdatascience.com/language-translation-with-rnns-d84d43b40571

• Question 484:

  You have an Azure Machine Learning workspace and a serverless Spark compute resource.

  You plan to run the same Spark session every 40 minutes. Each session runs for 15 minutes.

  During testing, you observe a 15-minute delay at the start of the Spark sessions.

  You need to reduce the delay to less than 1 minute.

  What should you do?

  A. Configure the session timeout to be 25 minutes.
  B. Increase the number of nodes to 16.
  C. Enable dynamically allocated executors.
  D. Enable an isolated compute.
  A. Configure the session timeout to be 25 minutes.

• Question 485:

  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.

  

  

• Question 486:

  You manage an Azure AI Foundry project.

  You need to develop a solution that uses an Azure OpenAI Service model designed to support reasoning and problem solving.

  Which model should you use?

  A. o1-mini
  B. Whisper
  C. GPT-4
  D. Embeddings
  C. GPT-4

• Question 487:

  DRAG DROP

  You need to correct the model fit issue.

  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: Augment the data

  Scenario: Columns in each dataset contain missing and null values. The datasets also contain many outliers.

  Step 2: Add the Bayesian Linear Regression module.

  Scenario: You produce a regression model to predict property prices by using the Linear Regression and Bayesian Linear Regression modules.

  Step 3: Configure the regularization weight.

  Regularization typically is used to avoid overfitting. For example, in L2 regularization weight, type the value to use as the weight for L2 regularization. We recommend that you use a non-zero value to avoid overfitting.

  Scenario:

  Model fit: The model shows signs of overfitting. You need to produce a more refined regression model that reduces the overfitting.

  Incorrect Answers:

  Multiclass Decision Jungle module:

  Decision jungles are a recent extension to decision forests. A decision jungle consists of an ensemble of decision directed acyclic graphs (DAGs).

  L-BFGS:

  L-BFGS stands for "limited memory Broyden-Fletcher-Goldfarb-Shanno". It can be found in the wwo-Class Logistic Regression module, which is used to create a logistic regression model that can be used to predict two (and only two) outcomes.

  References:

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

  ession

• Question 488:

  You are creating a machine learning model. You have a dataset that contains null rows.

  You need to use the Clean Missing Data module in Azure Machine Learning Studio to identify and resolve the null and missing data in the dataset.

  Which parameter should you use?

  A. Replace with mean
  B. Remove entire column
  C. Remove entire row
  D. Hot Deck
  C. Remove entire row

  ---

  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.

  References:

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

• Question 489:

  You manage an Azure Machine Learning workspace. You build a model for which you must configure a Responsible AI dashboard.

  Based on what you learn from the dashboard, you must perform the following activities:

  1. Determine what must be done to get a desirable outcome from the model.

  2. Identify the features that have the most direct effect on your outcome of interest.

  You need to select the components to use for the Responsible AI dashboard configuration.

  Which two components should you add? Each correct answer presents part of the solution.

  NOTE: Each correct selection is worth one point.

  A. error analysis
  B. counterfactuals
  C. explanation
  D. causal
  B. counterfactuals
  D. causal

• Question 490:

  HOTSPOT

  You create an Azure Machine Learning workspace and install the MLflow library.

  You need to tog different types of data by using the MLflow library.

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

  NOTE: Each correct selection is worth one point.