DP-100 Practice Test

DP-100 Practice Test practice questions, page 9 of 21, with answer review and explanations.

Question 41

This question is included in a number of questions that depicts the identical set-up. However, every question has a distinctive result. Establish if the recommendation satisfies the requirements. 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?

  1. Yes
  2. No

Question 42

You are making use of the Azure Machine Learning to designer construct an experiment. After dividing a dataset into training and testing sets, you configure the algorithm to be Two-Class Boosted Decision Tree. You are preparing to ascertain the Area Under the Curve (AUC). Which of the following is a sequential combination of the models required to achieve your goal?

  1. Train, Score, Evaluate.
  2. Score, Evaluate, Train.
  3. Evaluate, Export Data, Train.
  4. Train, Score, Export Data.

Question 43

You create an Azure Machine Learning workspace. You must implement dedicated compute for model training in the workspace by using Azure Synapse compute resources. The solution must attach the dedicated compute and start an Azure Synapse session. You need to implement the computer resources. 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.

Question 44

You are developing a hands-on workshop to introduce Docker for Windows to attendees. You need to ensure that workshop attendees can install Docker on their devices. Which two prerequisite components should attendees install on the devices? Each correct answer presents part of the solution. NOTE: Each correct selection is worth one point.

  1. Microsoft Hardware-Assisted Virtualization Detection Tool
  2. Kitematic
  3. BIOS-enabled virtualization
  4. VirtualBox
  5. Windows 10 64-bit Professional

Question 45

Your team is building a data engineering and data science development environment. The environment must support the following requirements: ✑ support Python and Scala ✑ compose data storage, movement, and processing services into automated data pipelines ✑ the same tool should be used for the orchestration of both data engineering and data science ✑ support workload isolation and interactive workloads ✑ enable scaling across a cluster of machines You need to create the environment. What should you do?

  1. Build the environment in Apache Hive for HDInsight and use Azure Data Factory for orchestration.
  2. Build the environment in Azure Databricks and use Azure Data Factory for orchestration.
  3. Build the environment in Apache Spark for HDInsight and use Azure Container Instances for orchestration.
  4. Build the environment in Azure Databricks and use Azure Container Instances for orchestration.