Microsoft DP-100 Online Practice Questions and Exam Preparation

Microsoft DP-100 Online Questions & Answers

• Question 541:

  HOTSPOT

  You create an Azure Data Lake Storage Gen2 stowage account named storage1 containing a file system named fsi and a folder named folder1.

  The contents of folder1 must be accessible from jobs on compute targets in the Azure Machine Learning workspace.

  You need to construct a URl to reference folder1.

  How should you construct the URI? To answer, select the appropriate options in the answer area.

  NOTE: Each correct selection is worth one point.

  

  

  ---

  
  

• Question 542:

  You are planning to make use of Azure Machine Learning designer to train models.

  You need choose a suitable compute type.

  Recommendation: You choose Attached compute.

  Will the requirements be satisfied?

  A. Yes
  B. No
  B. No

  ---

  Explanation

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

• Question 543:

  You make use of Azure Machine Learning Studio to create a binary classification model.

  You are preparing to carry out a parameter sweep of the model to tune hyperparameters. You have to make sure that the sweep allows for every possible combination of hyperparameters to be iterated. Also, the computing resources needed to carry out the sweep must be reduced.

  Which of the following actions should you take?

  A. You should consider making use of the Selective grid sweep mode.
  B. You should consider making use of the Measured grid sweep mode.
  C. You should consider making use of the Entire grid sweep mode.
  D. You should consider making use of the Random grid sweep mode.
  D. You should consider making use of the Random grid sweep mode.

  ---

  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.

  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 544:

  DRAG DROP

  You manage an Azure Machine Learning workspace named workspace1 and a Data Science Virtual Machine (DSVM) named DSMV1.

  You must run an experiment on DSMV1 by using a Jupyter notebook and Python SDK v2 code. You must store metrics and artifacts in workspace1. You start by creating Python SDK v2 code to import all required packages.

  You need to implement the Python SDK v2 code to store metrics and artifacts in workspace1.

  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:

  

  

  ---

  Explanation:

  Step 1: Retrieve the tracking URI of workspace1

  Configure MLflow tracking URI

  To connect MLflow to an Azure Machine Learning workspace, you need the tracking URI for the workspace. Each workspace has its own tracking URI and it has the protocol azureml://.

  1. Get the tracking URI for your workspace (Step 1) a. Login and configure your workspace b. You can get the tracking URI using the az ml workspace command:

  az ml workspace show --query mlflow_tracking_uri

  2. Configuring the tracking URI: (Step 2)

  Then the method set_tracking_uri() points the MLflow tracking URI to that URI.

  mlflow.set_tracking_uri(mlflow_tracking_uri)

  Step 2: Set the MLflow tracking URI.

  Track DSVM runs

  Add the following code to your notebook (or script) to set the Azure Machine Learning workspace object.

  import mlflow from azureml.core import Workspace

  ws = Workspace.from_config() mlflow.set_tracking_uri(ws.get_mlflow_tracking_uri())

  Step 3: Set the URI parameter of the mlflow.projects.run method.

  Now that your environment is connected to your workspace in Azure Machine Learning, you can start to work with it.

  You can run any project from a Git URI or from a local directory using the mlflow run command-line tool, or the mlflow.projects.run() Python API. These APIs also allow submitting the project for remote execution on Databricks and Kubernetes.

  Running Projects MLflow provides two ways to run projects: the mlflow run command-line tool, or the mlflow.projects.run() Python API. Both tools take the following parameters:

  * Project URI

  A directory on the local file system or a Git repository path, specified as a URI of the form

  https://<repo>

  (to use HTTPS) or user@host:path (to use Git over SSH). To run against an MLproject file located in a subdirectory of the project, add a '#' to the end of the URI argument, followed by the relative path from the project's root directory to the subdirectory containing the desired project.

  Note: Track ML experiments and models with MLflow

  Tracking refers to process of saving all experiment's related information that you may find relevant for every experiment you run.

  Why MLflow

  Azure Machine Learning workspaces are MLflow-compatible, which means you can use MLflow to track runs, metrics, parameters, and artifacts with your Azure Machine Learning workspaces.

  https://mlflow.org/docs/latest/projects.html

  https://learn.microsoft.com/en-us/azure/machine-learning/how-to-use-mlflow-configure-tracking

  https://learn.microsoft.com/en-us/azure/machine-learning/data-science-virtual-machine/how-to-track-experiments

  https://learn.microsoft.com/en-us/azure/machine-learning/how-to-use-mlflow-cli-runs

• Question 545:

  You are planning to make use of Azure Machine Learning designer to train models.

  You need choose a suitable compute type.

  Recommendation: You choose Compute cluster.

  Will the requirements be satisfied?

  A. Yes
  B. No
  A. Yes

  ---

  Explanation

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

• Question 546:

  HOTSPOT

  You are performing a classification task in Azure Machine Learning Studio.

  You must prepare balanced testing and training samples based on a provided data set.

  You need to split the data with a 0.75:0.25 ratio.

  Which value should you use for each parameter? To answer, select the appropriate options in the answer area.

  NOTE: Each correct selection is worth one point.

  

  

  ---

  Box 1: Split rows

  Use the Split Rows option if you just want to divide the data into two parts. You can specify the percentage of data to put in each split, but by default, the data is divided 50-50.

  You can also randomize the selection of rows in each group, and use stratified sampling. In stratified sampling, you must select a single column of data for which you want values to be apportioned equally among the two result datasets.

  Box 2: 0.75

  If you specify a number as a percentage, or if you use a string that contains the "%" character, the value is interpreted as a percentage. All percentage values must be within the range (0, 100), not including the values 0 and 100.

  Box 3: Yes

  To ensure splits are balanced.

  Box 4: No

  If you use the option for a stratified split, the output datasets can be further divided by subgroups, by selecting a strata column.

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

• Question 547:

  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 are using Azure Machine Learning to run an experiment that trains a classification model.

  You want to use Hyperdrive to find parameters that optimize the AUC metric for the model. You configure a HyperDriveConfig for the experiment by running the following code:

  

  You plan to use this configuration to run a script that trains a random forest model and then tests it with validation data. The label values for the validation data are stored in a variable named y_test variable, and the predicted probabilities from the model are stored in a variable named y_predicted.

  You need to add logging to the script to allow Hyperdrive to optimize hyperparameters for the AUC metric.

  Solution: Run the following code:

  

  Does the solution meet the goal?

  A. Yes
  B. No
  B. No

  ---

  Explanation

  Use a solution with logging.info(message) instead.

  Note: Python printing/logging example:

  logging.info(message) Destination: Driver logs, Azure Machine Learning designer

  https://docs.microsoft.com/en-us/azure/machine-learning/how-to-debug-pipelines

• Question 548:

  You are creating a binary classification by using a two-class logistic regression model.

  You need to evaluate the model results for imbalance.

  Which evaluation metric should you use?

  A. Relative Absolute Error
  B. AUC Curve
  C. Mean Absolute Error
  D. Relative Squared Error
  E. Accuracy
  F. Root Mean Square Error
  B. AUC Curve

  ---

  Explanation

  One can inspect the true positive rate vs. the false positive rate in the Receiver Operating Characteristic (ROC) curve and the corresponding Area Under the Curve (AUC) value. The closer this curve is to the upper left corner, the better the classifier's performance is (that is maximizing the true positive rate while minimizing the false positive rate). Curves that are close to the diagonal of the plot, result from classifiers that tend to make predictions that are close to random guessing.

  https://docs.microsoft.com/en-us/azure/machine-learning/studio/evaluate-model-performance#evaluating-a-binary-classification-model

• Question 549:

  HOTSPOT

  You are hired as a data scientist at a winery. The previous data scientist used Azure Machine Learning.

  You need to review the models and explain how each model makes decisions.

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

  NOTE: Each correct selection is worth one point.

  

  

  ---

  Meta explainers automatically select a suitable direct explainer and generate the best explanation info based on the given model and data sets. The meta explainers leverage all the libraries (SHAP, LIME, Mimic, etc.) that we have integrated or developed. The following are the meta explainers available in the SDK:

  Tabular Explainer: Used with tabular datasets.

  Text Explainer: Used with text datasets.

  Image Explainer: Used with image datasets.

  Box 1: Tabular

  Box 2: Text

  Box 3: Image

  Incorrect Answers:

  Hierarchical Attention Network (HAN)

  HAN was proposed by Yang et al. in 2016. Key features of HAN that differentiates itself from existing approaches to document classification are (1) it exploits the hierarchical nature of text data and (2) attention mechanism is adapted for document classification.

  https://medium.com/microsoftazure/automated-and-interpretable-machine-learning-d07975741298

• Question 550:

  You use Azure Machine Learning to train a model.

  You must use Bayesian sampling to tune hyperparameters.

  You need to select a learning_rate parameter distribution.

  Which two distributions can you use? Each correct answer presents a complete solution.

  NOTE: Each correct selection is worth one point.

  A. Uniform
  B. Choice
  C. QNormal
  D. Normal
  E. LogUniform
  A. Uniform
  B. Choice

  ---

  Explanation

  Bayesian sampling only supports choice , uniform , and quniform distributions over the search space.

  from azureml.train.hyperdrive import BayesianParameterSampling from azureml.train.hyperdrive import uniform, choice

  param_sampling = BayesianParameterSampling( {

  "learning_rate": uniform(0.05, 0.1),

  "batch_size": choice(16, 32, 64, 128)

  }

  ) Reference:

  https://learn.microsoft.com/en-us/azure/machine-learning/v1/how-to-tune-hyperparameters-v1