Microsoft DP-100 Online Practice Questions and Exam Preparation

Microsoft DP-100 Online Questions & Answers

• Question 561:

  HOTSPOT

  You are preparing to build a deep learning convolutional neural network model for image classification. You create a script to train the model using CUDA devices.

  You must submit an experiment that runs this script in the Azure Machine Learning workspace.

  The following compute resources are available:

  1. a Microsoft Surface device on which Microsoft Office has been installed. Corporate IT policies prevent the installation of additional software

  2. a Compute Instance named ds-workstation in the workspace with 2 CPUs and 8 GB of memory

  3. an Azure Machine Learning compute target named cpu-cluster with eight CPU-based nodes

  4. an Azure Machine Learning compute target named gpu-cluster with four CPU and GPU-based nodes

  You need to specify the compute resources to be used for running the code to submit the experiment, and for running the script in order to minimize model training time.

  Which resources should the data scientist use? To answer, select the appropriate options in the answer area.

  NOTE: Each correct selection is worth one point.

  

  

  ---

  Box 1: the ds-workstation notebook VM

  Box 2: the gpu-compute target

  Just as GPUs revolutionized deep learning through unprecedented training and inferencing performance, RAPIDS enables traditional machine learning practitioners to unlock game-changing performance with GPUs. With RAPIDS on Azure Machine Learning service, users can accelerate the entire machine learning pipeline, including data processing, training and inferencing, with GPUs from the NC_v3, NC_v2, ND or ND_v2 families. Users can unlock performance gains of more than 20X (with 4 GPUs), slashing training times from hours to minutes and dramatically reducing time-to-insight.

  https://azure.microsoft.com/sv-se/blog/azure-machine-learning-service-now-supports-nvidia-s-rapids/

• Question 562:

  You are creating a compute target to train a machine learning experiment.

  The compute target must support automated machine learning, machine learning pipelines, and Azure Machine Learning designer training.

  You need to configure the compute target.

  Which option should you use?

  A. Azure HDInsight
  B. Azure Machine Learning compute cluster
  C. Azure Batch
  D. Remote VM
  B. Azure Machine Learning compute cluster

  ---

  
  

• Question 563:

  HOTSPOT

  You use an Azure Machine Learning workspace. The default datastore contains comma-separated values (CSV) files.

  The CSV files must be made available for use in experiments and data processing pipelines. The files must be loaded directly into pandas dataframes.

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

  NOTE: Each correct selection is worth one point.

  

  

  ---

  Explanation:

  Box 1: Dataset

  Create a TabularDataset

  The following code creates an unregistered TabularDataset from a web url.

  from azureml.core.dataset import Dataset web_path ='

  https://dprepdata.blob.core.windows.net/demo/Titanic.csv'

  titanic_ds = Dataset.Tabular.from_delimited_files(path=web_path)

  TabularDataset objects provide the ability to load the data in your TabularDataset into a pandas or Spark DataFrame so that you can work with familiar data preparation and training libraries without having to leave your notebook.

  Box 2: from_delimited_files

  The from_delimited_files method creates a TabularDataset to represent tabular data in delimited files (e.g. CSV and TSV).

  Note: A TabularDataset can be created from CSV, TSV, Parquet files, or SQL query using the from_* methods of the TabularDatasetFactory class.

  TabularDataset is created using methods like from_delimited_files from the TabularDatasetFactory class.

  TabularDatasetFactory Class contains methods to create a tabular dataset for Azure Machine Learning.

  A TabularDataset is created using the from_* methods in this class, for example, the method from_delimited_files.

  Incorrect:

  * Dataset.Tabular.from_pandas_dataframe register_pandas_dataframe Create a dataset from pandas dataframe.

  https://learn.microsoft.com/en-us/azure/machine-learning/v1/how-to-train-with-datasets

  https://learn.microsoft.com/en-us/python/api/azureml-core/azureml.data.dataset_factory.tabulardatasetfactory

• Question 564:

  You manage an Azure Machine Learning workspace.

  You must set up an event-driven process to trigger a retraining pipeline.

  You need to configure an Azure service that will trigger a retraining pipeline in response to data drift in Azure Machine Learning datasets.

  Which Azure service should you use?

  A. Event Grid
  B. Azure Functions
  C. Event Hubs
  D. Logic Apps
  A. Event Grid

• Question 565:

  You develop and train a machine learning model to predict fraudulent transactions for a hotel booking website.

  Traffic to the site varies considerably. The site experiences heavy traffic on Monday and Friday and much lower traffic on other days. Holidays are also high web traffic days.

  You need to deploy the model as an Azure Machine Learning real-time web service endpoint on compute that can dynamically scale up and down to support demand.

  Which deployment compute option should you use?

  A. attached Azure Databricks cluster
  B. Azure Container Instance (ACI)
  C. Azure Kubernetes Service (AKS) inference cluster
  D. Azure Machine Learning Compute Instance
  E. attached virtual machine in a different region
  C. Azure Kubernetes Service (AKS) inference cluster

  ---

  Explanation

  Azure Machine Learning compute cluster is a managed-compute infrastructure that allows you to easily create a single or multi-node compute. The compute is created within your workspace region as a resource that can be shared with other users in your workspace. The compute scales up automatically when a job is submitted, and can be put in an Azure Virtual Network.

  https://docs.microsoft.com/en-us/azure/machine-learning/how-to-create-attach-compute-sdk

• Question 566:

  HOTSPOT

  You are evaluating a Python NumPy array that contains six data points defined as follows:

  <pdf2txt-u>data = [10, 20, 30, 40, 50, 60]</pdf2txt-u>

  You must generate the following output by using the k-fold algorithm implantation in the Python Scikit-learn machine learning library:

  <pdf2txt-u>train: [10 40 50 60], test: [20 30]</pdf2txt-u> <pdf2txt-u>train: [20 30 40 60], test: [10 50]</pdf2txt-u> <pdf2txt-u>train: [10 20 30 50], test: [40 60]</pdf2txt-u>

  You need to implement a cross-validation to generate the output.

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

  NOTE: Each correct selection is worth one point.

  

  

  ---

  Box 1: k-fold

  Box 2: 3

  K-Folds cross-validator provides train/test indices to split data in train/test sets. Split dataset into k consecutive folds (without shuffling by default).

  The parameter n_splits ( int, default=3) is the number of folds. Must be at least 2.

  Box 3: data

  Example:

  Example:

  >>>

  >>> from sklearn.model_selection import KFold

  >>> X = np.array([[1, 2], [3, 4], [1, 2], [3, 4]])

  >>> y = np.array([1, 2, 3, 4])

  >>> kf = KFold(n_splits=2)

  >>> kf.get_n_splits(X)

  2

  >>> print(kf) KFold(n_splits=2, random_state=None, shuffle=False)

  >>> for train_index, test_index in kf.split(X):

  ... print("TRAIN:", train_index, "TEST:", test_index)... X_train, X_test = X[train_index], X[test_index]

  ... y_train, y_test = y[train_index], y[test_index] TRAIN: [2 3]

  TEST: [0 1]

  TRAIN: [0 1]

  TEST: [2 3]

  References:

  https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.KFold.html

• Question 567:

  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 have an Azure Machine Learning workspace that includes an AmlCompute cluster and a batch endpoint.

  You clone a repository that contains an MLflow model to your local computer.

  You need to ensure that you can deploy the model to the batch endpoint.

  Solution: Register the model in the workspace.

  Does the solution meet the goal?

  A. Yes
  B. No
  A. Yes

• Question 568:

  DRAG DROP

  You create a multi-class image classification deep learning model.

  The model must be retrained monthly with the new image data fetched from a public web portal. You create an Azure Machine Learning pipeline to fetch new data, standardize the size of images, and retrain the model.

  You need to use the Azure Machine Learning Python SDK v2 to configure the schedule for the pipeline. The schedule should be defined by using the frequency and interval properties, with frequency set to "month" and interval set to "1".

  Which three classes should you instantiate 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:

  

  

  ---

  Explanation:

  Step 1: PipelineJob

  Schedule machine learning pipeline jobs

  To run a pipeline job on a recurring basis, you'll need to create a schedule. A Schedule associates a job, and a trigger. The trigger can either be cron that use cron expression to describe the wait between runs or recurrence that specify using what frequency to trigger job. In each case, you need to define a pipeline job first.

  Step 2: RecurrenceTrigger

  Step 3: JobSchedule

  Create a schedule

  Create a time-based schedule with recurrence pattern

  Example:

  schedule_name = "simple_sdk_create_schedule_recurrence" schedule_start_time = datetime.utcnow()

  recurrence_trigger = RecurrenceTrigger( frequency="day", interval=1, schedule=RecurrencePattern(hours=10, minutes=[0, 1]), start_time=schedule_start_time, time_zone=TimeZone.UTC, ) job_schedule = JobSchedule( name=schedule_name, trigger=recurrence_trigger, create_job=pipeline_job )

  https://learn.microsoft.com/en-us/azure/machine-learning/how-to-schedule-pipeline-job

• Question 569:

  You are creating a new experiment in Azure Machine Learning Studio. You have a small dataset that has missing values in many columns. The data does not require the application of predictors for each column.

  You plan to use the Clean Missing Data.

  You need to select a data cleaning method.

  Which method should you use?

  A. Replace using Probabilistic PCA
  B. Normalization
  C. Synthetic Minority Oversampling Technique (SMOTE)
  D. Replace using MICE
  A. Replace using Probabilistic PCA

  ---

  Explanation

  Replace using Probabilistic PCA: Compared to other options, such as Multiple Imputation using Chained Equations (MICE), this option has the advantage of not requiring the application of predictors for each column. Instead, it approximates the covariance for the full dataset. Therefore, it might offer better performance for datasets that have missing values in many columns.

  References:

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

• Question 570:

  You run an automated machine learning experiment in an Azure Machine Learning workspace. Information about the run is listed in the table below:

  

  You need to write a script that uses the Azure Machine Learning SDK to retrieve the best iteration of the experiment run.

  Which Python code segment should you use?

  

  A. Option A
  B. Option B
  C. Option C
  D. Option D
  E. Option E
  D. Option D

  ---

  Explanation

  The get_output method on automl_classifier returns the best run and the fitted model for the last invocation. Overloads on get_output allow you to retrieve the best run and fitted model for any logged metric or for a particular iteration.

  In [ ]:

  best_run, fitted_model = local_run.get_output()

  https://notebooks.azure.com/azureml/projects/azureml-getting-started/html/how-to-use-azureml/automated-machine-learning/classification-with-deployment/auto-ml-classification-with-deployment.ipynb