Download Microsoft.DP-100.PracticeTest.2019-09-05.38q.tqb
| Vendor: | Microsoft |
| Exam Code: | DP-100 |
| Exam Name: | Designing and Implementing a Data Science Solution on Azure |
| Date: | Sep 05, 2019 |
| File Size: | 646 KB |
Demo Questions
Question 1
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?
- Build the environment in Apache Hive for HDInsight and use Azure Data Factory for orchestration.
- Build the environment in Azure Databricks and use Azure Data Factory for orchestration.
- Build the environment in Apache Spark for HDInsight and use Azure Container Instances for orchestration.
- Build the environment in Azure Databricks and use Azure Container Instances for orchestration.
Correct answer: B
Explanation:
In Azure Databricks, we can create two different types of clusters. Standard, these are the default clusters and can be used with Python, R, Scala and SQL High-concurrency Azure Databricks is fully integrated with Azure Data Factory. Incorrect Answers:D: Azure Container Instances is good for development or testing. Not suitable for production workloads.References:https://docs.microsoft.com/en-us/azure/architecture/data-guide/technology-choices/data-science-and-machine-learning In Azure Databricks, we can create two different types of clusters.
- Standard, these are the default clusters and can be used with Python, R, Scala and SQL
- High-concurrency
Azure Databricks is fully integrated with Azure Data Factory.
Incorrect Answers:
D: Azure Container Instances is good for development or testing. Not suitable for production workloads.
References:
https://docs.microsoft.com/en-us/azure/architecture/data-guide/technology-choices/data-science-and-machine-learning
Question 2
You plan to build a team data science environment. Data for training models in machine learning pipelines will be over 20 GB in size.
You have the following requirements:
- Models must be built using Caffe2 or Chainer frameworks.
- Data scientists must be able to use a data science environment to build the machine learning pipelines and train models on their personal devices in both connected and disconnected network environments.
Personal devices must support updating machine learning pipelines when connected to a network.
You need to select a data science environment.
Which environment should you use?
- Azure Machine Learning Service
- Azure Machine Learning Studio
- Azure Databricks
- Azure Kubernetes Service (AKS)
Correct answer: A
Explanation:
The Data Science Virtual Machine (DSVM) is a customized VM image on Microsoft’s Azure cloud built specifically for doing data science. Caffe2 and Chainer are supported by DSVM. DSVM integrates with Azure Machine Learning. Incorrect Answers:B: Use Machine Learning Studio when you want to experiment with machine learning models quickly and easily, and the built-in machine learning algorithms are sufficient for your solutions. References:https://docs.microsoft.com/en-us/azure/machine-learning/data-science-virtual-machine/overview The Data Science Virtual Machine (DSVM) is a customized VM image on Microsoft’s Azure cloud built specifically for doing data science. Caffe2 and Chainer are supported by DSVM.
DSVM integrates with Azure Machine Learning.
Incorrect Answers:
B: Use Machine Learning Studio when you want to experiment with machine learning models quickly and easily, and the built-in machine learning algorithms are sufficient for your solutions.
References:
https://docs.microsoft.com/en-us/azure/machine-learning/data-science-virtual-machine/overview
Question 3
You are implementing a machine learning model to predict stock prices.
The model uses a PostgreSQL database and requires GPU processing.
You need to create a virtual machine that is pre-configured with the required tools.
What should you do?
- Create a Data Science Virtual Machine (DSVM) Windows edition.
- Create a Geo Al Data Science Virtual Machine (Geo-DSVM) Windows edition.
- Create a Deep Learning Virtual Machine (DLVM) Linux edition.
- Create a Deep Learning Virtual Machine (DLVM) Windows edition.
- Create a Data Science Virtual Machine (DSVM) Linux edition.
Correct answer: E
Explanation:
Incorrect Answers:A, C: PostgreSQL (CentOS) is only available in the Linux Edition.B: The Azure Geo AI Data Science VM (Geo-DSVM) delivers geospatial analytics capabilities from Microsoft's Data Science VM. Specifically, this VM extends theAI and data science toolkits in the Data Science VM by adding ESRI's market-leading ArcGIS Pro Geographic Information System. D: DLVM is a template on top of DSVM image. In terms of the packages, GPU drivers etc are all there in the DSVM image. Mostly it is for convenience during creation where we only allow DLVM to be created on GPU VM instances on Azure. References:https://docs.microsoft.com/en-us/azure/machine-learning/data-science-virtual-machine/overview Incorrect Answers:
A, C: PostgreSQL (CentOS) is only available in the Linux Edition.
B: The Azure Geo AI Data Science VM (Geo-DSVM) delivers geospatial analytics capabilities from Microsoft's Data Science VM. Specifically, this VM extends the
AI and data science toolkits in the Data Science VM by adding ESRI's market-leading ArcGIS Pro Geographic Information System.
D: DLVM is a template on top of DSVM image. In terms of the packages, GPU drivers etc are all there in the DSVM image. Mostly it is for convenience during creation where we only allow DLVM to be created on GPU VM instances on Azure.
References:
https://docs.microsoft.com/en-us/azure/machine-learning/data-science-virtual-machine/overview
