Microsoft DP-100 Online Practice Questions and Exam Preparation

Microsoft DP-100 Online Questions & Answers

• Question 491:

  You create a binary classification model by using Azure Machine Learning Studio.

  You must tune hyperparameters by performing a parameter sweep of the model. The parameter sweep must meet the following requirements:

  1. iterate all possible combinations of hyperparameters

  2. minimize computing resources required to perform the sweep

  You need to perform a parameter sweep of the model.

  Which parameter sweep mode should you use?

  A. Random sweep
  B. Sweep clustering
  C. Entire grid
  D. Random grid
  D. Random grid

  ---

  Explanation

  Maximum number of runs on random grid: This option also controls the number of iterations over a random sampling of parameter values, but the values are not generated randomly from the specified range; instead, a matrix is created of all possible combinations of parameter values and a random sampling is taken over the matrix. This method is more efficient and less prone to regional oversampling or undersampling.

  If you are training a model that supports an integrated parameter sweep, you can also set a range of seed values to use and iterate over the random seeds as well. This is optional, but can be useful for avoiding bias introduced by seed selection.

  Incorrect Answers:

  B: If you are building a clustering model, use Sweep Clustering to automatically determine the optimum number of clusters and other parameters.

  C: Entire grid: When you select this option, the module loops over a grid predefined by the system, to try different combinations and identify the best learner. This option is useful for cases where you don't know what the best parameter settings might be and want to try all possible combination of values.

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

  hyperparameters

• Question 492:

  HOTSPOT

  You manage an Azure AI Foundry project.

  You are evaluating two RAG solutions.

  When generating answers, the solutions display the following results:

  1. The first solution displays low completeness and low utilization.

  2. The second solution displays low completeness and high utilization.

  You need to address the issues found during evaluation.

  Which action should you perform first for each issue? To answer, select the appropriate options in the answer area.

  NOTE: Each correct selection is worth one point.

  

  

• Question 493:

  Your team is building a data engineering and data science development environment. The environment must support the following requirements:

  1. support Python and Scala

  2. compose data storage, movement, and processing services into automated data pipelines

  3. the same tool should be used for the orchestration of both data engineering and data science

  4. support workload isolation and interactive workloads

  5. enable scaling across a cluster of machines

  You need to create the environment.

  What should you do?

  A. Build the environment in Apache Hive for HDInsight and use Azure Data Factory for orchestration.
  B. Build the environment in Azure Databricks and use Azure Data Factory for orchestration.
  C. Build the environment in Apache Spark for HDInsight and use Azure Container Instances for orchestration.
  D. Build the environment in Azure Databricks and use Azure Container Instances for orchestration.
  B. Build the environment in Azure Databricks and use Azure Data Factory for orchestration.

  ---

  Explanation

  In Azure Databricks, we can create two different types of clusters.

  1. Standard, these are the default clusters and can be used with Python, R, Scala and SQL

  2. High-concurrency

  Azure Databricks is fully integrated with Azure Data Factory.

  Incorrect Answers:

  D: Azure Container Instances is good for development or testing. Not suitable for production workloads.

  References:

  https://docs.microsoft.com/en-us/azure/architecture/data-guide/technology-choices/data-science-and-machine-learning

• Question 494:

  You create an Azure Machine Learning pipeline named pipeline1 with two steps that contain Python scripts. Data processed by the first step is passed to the second step.

  You must update the content of the downstream data source of pipeline1 and run the pipeline again.

  You need to ensure the new run of pipeline1 fully processes the updated content.

  Solution: Change the value of the compute_target parameter of the PythonScriptStep object in the two steps.

  Does the solution meet the goal?

  A. Yes
  B. No
  B. No

• Question 495:

  DRAG DROP

  You create a multi-class image classification deep learning experiment by using the PyTorch framework. You plan to run the experiment on an Azure Compute cluster that has nodes with GPU's.

  You need to define an Azure Machine Learning service pipeline to perform the monthly retraining of the image classification model. The pipeline must run with minimal cost and minimize the time required to train the model.

  Which three pipeline steps should you run 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: Configure a DataTransferStep() to fetch new image data...

  Step 2: Configure a PythonScriptStep() to run image_resize.y on the cpu-compute compute target.

  Step 3: Configure the EstimatorStep() to run training script on the gpu_compute computer target.

  The PyTorch estimator provides a simple way of launching a PyTorch training job on a compute target.

  https://docs.microsoft.com/en-us/azure/machine-learning/how-to-train-pytorch

• Question 496:

  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 managed online endpoint with the default authentication settings. Deploy the model to the online endpoint.

  Does the solution meet the goal?

  A. Yes
  B. No
  A. Yes

• Question 497:

  HOTSPOT

  You have an Azure Machine Learning workspace.

  You run the following code in a Python environment in which the configuration file for your workspace has been downloaded.

  

  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.

  

  

• Question 498:

  You are planning to register a trained model in an Azure Machine Learning workspace.

  You must store additional metadata about the model in a key-value format. You must be able to add new metadata and modify or delete metadata after creation.

  You need to register the model.

  Which parameter should you use?

  A. description
  B. model_framework
  C. tags
  D. properties
  D. properties

  ---

  Explanation

  azureml.core.Model.properties:

  Dictionary of key value properties for the Model. These properties cannot be changed after registration, however new key value pairs can be added.

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

• Question 499:

  You plan to use automated machine learning to train a regression model. You have data that has features which have missing values, and categorical features with few distinct values.

  You need to configure automated machine learning to automatically impute missing values and encode categorical features as part of the training task.

  Which parameter and value pair should you use in the AutoMLConfig class?

  A. featurization = 'auto'
  B. enable_voting_ensemble = True
  C. task = 'classification'
  D. exclude_nan_labels = True
  E. enable_tf = True
  A. featurization = 'auto'

  ---

  Explanation

  Featurization str or FeaturizationConfig

  Values: 'auto' / 'off' / FeaturizationConfig

  Indicator for whether featurization step should be done automatically or not, or whether customized featurization should be used.

  Column type is automatically detected. Based on the detected column type preprocessing/featurization is done as follows:

  Categorical: Target encoding, one hot encoding, drop high cardinality categories, impute missing values.

  Numeric: Impute missing values, cluster distance, weight of evidence.

  DateTime: Several features such as day, seconds, minutes, hours etc.

  Text: Bag of words, pre-trained Word embedding, text target encoding.

  https://docs.microsoft.com/en-us/python/api/azureml-train-automl-client/azureml.train.automl.automlconfig.automlconfig

• Question 500:

  HOTSPOT

  You manage an Azure AI Foundry project.

  You plan to build a RAG solution. The solution must include two models:

  1. One for text output, named Model1. This model must resemble human language and read naturally.

  2. One for creating embeddings, named Model2. This model must maximize retrieval of relevant results (high recall).

  You need to compare different models by using benchmarking metrics to select the appropriate models for Model1 and Model2.