Microsoft DP-100 Online Practice Questions and Exam Preparation

Microsoft DP-100 Online Questions & Answers

• Question 61:

  You are implementing hyperparameter tuning by using Bayesian sampling for a model training from a notebook. The notebook is in an Azure Machine Learning workspace that uses a compute cluster with 20 nodes.

  The code implements Bandit termination policy with slack factor set to 0.2 and the HyperDriveConfig class instance with max_concurrent_runs set to 10.

  You must increase effectiveness of the tuning process by improving sampling convergence.

  You need to select which sampling convergence to use.

  What should you select?

  A. Set the value of slack factor of early_termination_policy to 0.9.
  B. Set the value of max_concurrent_runs of HyperDriveConfig to 4.
  C. Set the value of slack factor of early_termination_policy to 0.1.
  D. Set the value of max_concurrent_runs of HyperDriveConfig to 20.
  B. Set the value of max_concurrent_runs of HyperDriveConfig to 4.

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  Explanation

  Bayesian sampling

  Bayesian sampling is based on the Bayesian optimization algorithm. It picks samples based on how previous samples did, so that new samples improve the primary metric.

  The number of concurrent jobs has an impact on the effectiveness of the tuning process. A smaller number of concurrent jobs may lead to better sampling convergence, since the smaller degree of parallelism increases the number of jobs that benefit from previously completed jobs.

  https://learn.microsoft.com/en-us/azure/machine-learning/how-to-tune-hyperparameters

• Question 62:

  HOTSPOT

  You have an Azure Machine Learning workspace.

  You plan to set up logging and tracking experiments by using MLflow Tracking.

  You need to log the accuracy as a numerical value and the training loss as a plot.

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

  NOTE: Each correct selection is worth one point.

  

  

• Question 63:

  You are a data scientist working for a hotel booking website company. You use the Azure Machine Learning service to train a model that identifies fraudulent transactions. You must deploy the model as an Azure Machine Learning online endpoint by using the Azure Machine Learning Python SDK v2. The deployed model must return real-time predictions of fraud based on transaction data input.

  You need to create the script that is specified as the scoring_script parameter for the CodeConfiguration class used to deploy the model.

  What should the entry script do?

  A. Register the model with appropriate tags and properties.
  B. Create a Conda environment for the online endpoint compute and install the necessary Python packages.
  C. Load the model and use it to predict labels from input data.
  D. Start a node on the inference cluster where the model is deployed.
  E. Specify the number of cores and the amount of memory required for the online endpoint compute.
  C. Load the model and use it to predict labels from input data.

• Question 64:

  DRAG DROP

  You have an Azure AI Foundry project with a connected Azure OpenAI Service model.

  You have a set of text files stored locally.

  You must set up a flow that generates responses based on the content of your local files.

  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:

  

  

• Question 65:

  You have the following code. The code prepares an experiment to run a script:

  A. run = script_experiment.start_logging()
  B. run = Run(experiment=script_experiment)
  C. ws.get_run(run_id=experiment.id)
  D. run = script_experiment.submit(config=script_config)
  D. run = script_experiment.submit(config=script_config)

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  Explanation

  The experiment class submit method submits an experiment and return the active created run.

  Syntax: submit(config, tags=None, **kwargs)

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

• Question 66:

  You use the following Python code in a notebook to deploy a model as a web service:

  from azureml.core.webservice import AciWebservice from azureml.core.model import InferenceConfig

  inference_config = InferenceConfig(runtime='python', source_directory='model_files', entry_script='score.py', conda_file='env.yml') deployment_config = AciWebservice.deploy_configuration(cpu_cores=1, memory_gb=1) service = Model.deploy(ws, 'my-service', [model], inference_config, deployment_config) service.wait_for_deployment(True)

  The deployment fails.

  You need to use the Python SDK in the notebook to determine the events that occurred during service deployment an initialization.

  Which code segment should you use?

  A. service.state
  B. service.get_logs()
  C. service.serialize()
  D. service.environment
  B. service.get_logs()

  ---

  Explanation

  The first step in debugging errors is to get your deployment logs.

  In Python: service.get_logs()

  https://docs.microsoft.com/en-us/azure/machine-learning/how-to-troubleshoot-deployment

• Question 67:

  HOTSPOT

  You have an Azure Machine Learning workspace.

  You plan to use Azure Machine Learning Python SDK v2 to define a pipeline component that trains an image classification model.

  The execution logic of the component is contained in the train() function in the file named model_train.py.

  You write code to import all required libraries and store it as train_component.py in the same folder that contains model_train.py..

  You need to complete the remaining code in train_component.py.

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

  NOTE: Each correct selection is worth one point.

  

  

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• Question 68:

  You are designing an Azure Machine Learning solution for traffic optimization.

  The model must be deployed as a web service on serverless compute and provide real-time predictions.

  Which compute should you use?

  A. Azure Machine Learning batch endpoint
  B. Azure Machine Learning online endpoint
  C. Azure Machine Learning Kubernetes online endpoints
  D. Azure Machine Learning serverless compute
  B. Azure Machine Learning online endpoint

• Question 69:

  HOTSPOT

  You create an Azure Machine Learning dataset containing automobile price data. The dataset includes 10.000 rows and 10 columns. You use the Azure Machine Learning designer to transform the dataset by using an Execute Python Script component and custom code.

  The code must combine three columns to create a new column.

  You need to configure the code function.

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

  NOTE: Each correct selection is worth one point.

  

  

• Question 70:

  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 create an Azure Machine Learning service datastore in a workspace. The datastore contains the following files:

  1. /data/2018/Q1.csv

  2. /data/2018/Q2.csv

  3. /data/2018/Q3.csv

  4. /data/2018/Q4.csv

  5. /data/2019/Q1.csv

  All files store data in the following format:

  id,f1,f2,I

  1,1,2,0

  2,1,1,1

  3,2,1,0

  4,2,2,1

  You run the following code:

  

  You need to create a dataset named training_data and load the data from all files into a single data frame by using the following code:

  

  Solution: Run the following code:

  

  Does the solution meet the goal?

  A. Yes
  B. No
  A. Yes

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  Explanation

  https://docs.microsoft.com/en-us/azure/machine-learning/how-to-create-register-datasets