Microsoft DP-100 Online Practice Questions and Exam Preparation

Microsoft DP-100 Online Questions & Answers

• Question 241:

  HOTSPOT

  The finance team asks you to train a model using data in an Azure Storage blob container named finance-data.

  You need to register the container as a datastore in an Azure Machine Learning workspace and ensure that an error will be raised if the container does not exist.

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

  NOTE: Each correct selection is worth one point.

  

  

  ---

  Box 1: register_azure_blob_container

  Register an Azure Blob Container to the datastore.

  Box 2: create_if_not_exists = False

  Create the file share if it does not exists, defaults to False.

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

• Question 242:

  You have been tasked with employing a machine learning model, which makes use of a PostgreSQL database and needs GPU processing, to forecast prices.

  You are preparing to create a virtual machine that has the necessary tools built into it.

  You need to make use of the correct virtual machine type.

  Recommendation: You make use of a Data Science Virtual Machine (DSVM) Windows edition.

  Will the requirements be satisfied?

  A. Yes
  B. No
  B. No

  ---

  Explanation

  In the DSVM, your training models can use deep learning algorithms on hardware that's based on graphics processing units (GPUs).

  PostgreSQL is available for the following operating systems: Linux (all recent distributions), 64-bit installers available for macOS (OS X) version 10.6 and newer ?Windows (with installers available for 64-bit version; tested on latest versions and back to Windows 2012 R2. Reference:

  https://docs.microsoft.com/en-us/azure/machine-learning/data-science-virtual-machine/overview

• Question 243:

  You create an Azure Machine Learning workspace.

  You must use the Python SDK v2 to implement an experiment from a Jupyter notebook in the workspace. The experiment must log string metrics.

  You need to implement the method to log the string metrics.

  Which method should you use?

  A. mlflow.log_artifact()
  B. mlflow.log.dict()
  C. mlflow.log_metric()
  D. mlflow.log_text()
  D. mlflow.log_text()

  ---

  Explanation

  Logging files

  In general, files in MLflow are called artifacts. You can log artifacts in multiple ways in Mlflow:

  * Log text in a text file mlflow.log_text("text string", "notes.txt")

  Text is persisted inside of the run in a text file with name notes.txt.

  Incorrect:

  * mlflow.log.dict

  Log dictionaries as JSON and YAML files mlflow.log_dict(dictionary, "file.yaml" dictionary is a dictionary object containing all the structure that you want to persist as JSON or YAML file.

  * mlflow.log_artifact

  Log a trivial file already existing mlflow.log_artifact("path/to/file.pkl")

  Files are always logged in the root of the run. If artifact_path is provided, then the file is logged in a folder as indicated in that parameter.

  * Log all the artifacts in an existing folder mlflow.log_artifacts("path/to/folder")

  Folder structure is copied to the run, but the root folder indicated is not included.

  https://learn.microsoft.com/en-us/azure/machine-learning/how-to-log-view-metrics

• Question 244:

  HOTSPOT

  You plan to implement a two-step pipeline by using the Azure Machine Learning SDK for Python.

  The pipeline will pass temporary data from the first step to the second step.

  You need to identify the class and the corresponding method that should be used in the second step to access temporary data generated by the first step in the pipeline.

  Which class and method should you identify? To answer, select the appropriate options in the answer area.

  NOTE: Each correct selection is worth one point

  

  

• Question 245:

  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.

  Solution: Run the following code:

  

  Does the solution meet the goal?

  A. Yes
  B. No
  A. Yes

• Question 246:

  HOTSPOT

  You are running a training experiment on remote compute in Azure Machine Learning (ML) by using Azure ML SDK v2 for Python.

  The experiment is configured to use a conda environment that includes all required packages.

  You must track metrics generated in the experiment.

  You need to complete the script for the experiment.

  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: import mlflow

  Log metrics, parameters and files with MLflow APPLIES TO: Python SDK azure-ai-ml v2 (current)

  Azure Machine Learning supports logging and tracking experiments using MLflow Tracking. You can log models, metrics, parameters, and artifacts with MLflow as it supports local mode to cloud portability.

  Prerequisites include:

  To log metrics, parameters, artifacts and models in your experiments in Azure Machine Learning using MLflow, just import MLflow in your script:

  import mlflow Box 2: mlflow.start_run

  When training interactively, such as in a Jupyter Notebook, use the following pattern:

  Create or set the active experiment.

  Start the job.

  Use logging methods to log metrics and other information.

  End the job.

  For example, the following code snippet demonstrates configuring the experiment, and then logging during a job:

  Python:

  import mlflow

  # Set the experiment mlflow.set_experiment("mlflow-experiment")

  # Start the run mlflow_run = mlflow.start_run()

  # Log metrics or other information mlflow.log_metric('mymetric', 1)

  # End run

  mlflow.end_run() Box 3: mlflow.log_metric('reg_rate',no.float(reg_rate))

  Box 4: mlflow.end_run()

  https://learn.microsoft.com/en-us/azure/machine-learning/how-to-log-view-metrics

• Question 247:

  You use Azure Machine Learning designer to load the following datasets into an experiment:

  Dataset1

  

  Dataset2

  

  You need to create a dataset that has the same columns and header row as the input datasets and contains all rows from both input datasets.

  Solution: Use the Apply Transformation module.

  Does the solution meet the goal?

  A. Yes
  B. No
  B. No

  ---

  Explanation

  Instead use solution: Use the Add Rows module.

  Note: Add Rows component

  Use this component to concatenate two datasets. In concatenation, the rows of the second dataset are added to the end of the first dataset.

  Concatenation of rows is useful in scenarios such as these:

  You have generated a series of evaluation statistics, and you want to combine them into one table for easier reporting.

  You have been working with different datasets, and you want to combine the datasets to create a final dataset.

  https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/add-rows

• Question 248:

  You use Azure Machine Learning Studio to build a machine learning experiment.

  You need to divide data into two distinct datasets.

  Which module should you use?

  A. Split Data
  B. Load Trained Model
  C. Assign Data to Clusters
  D. Group Data into Bins
  A. Split Data

• Question 249:

  DRAG DROP

  You need to define a process for penalty event detection.

  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:

  

  

• Question 250:

  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

  ---

  Explanation

  Use two file paths.

  Use Dataset.Tabular_from_delimeted as the data isn't cleansed.

  Note:

  A TabularDataset represents data in a tabular format by parsing the provided file or list of files. This provides you with the ability to materialize the data into a pandas or Spark DataFrame so you can work with familiar data preparation and training libraries without having to leave your notebook. You can create a TabularDataset object from .csv, .tsv, .parquet, .jsonl files, and from SQL query results.

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