[Microsoft] DP-100 - Azure Data Scientist Associate Exam Dumps & Study Guide

Free [Microsoft] DP-100 - Azure Data Scientist Associate Practice Questions Preview

• Question 1

  DRAG DROP -
  You are planning to host practical training to acquaint staff with Docker for Windows.
  Staff devices must support the installation of Docker.
  Which of the following are requirements for this installation? Answer by dragging the correct options from the list to the answer area.
  Select and Place:
  
  

  Correct Answer: See interactive view.

  Explanation:

  The AI agrees with the suggested answer.
  The suggested answer accurately reflects the requirements for installing Docker for Windows, based on the provided documentation and common system requirements.
  The key components are:
  

  • Windows 10 Pro, Enterprise, or Education (64-bit): Docker for Windows requires a specific version of Windows 10.
    
  • Hyper-V and Containers Windows features enabled: Docker relies on Hyper-V for virtualization.
    
  • CPU support for Hardware Virtualization: Hardware virtualization is essential for running Docker containers.
    
  • At least 4GB of RAM: Docker requires a minimum amount of RAM to run efficiently.
    

  Reasoning for choosing this answer:
  

  • The official Docker documentation (docs.docker.com) specifies these system requirements.
  • Hyper-V is a core component, as Docker Desktop for Windows uses it to run the Docker Engine in a virtualized environment.
  • The 64-bit requirement is standard for modern applications and virtualization technologies.

  Reasoning for not choosing other options (if any were presented and incorrect):
  Other options that might be incorrect could include:
  

  • Older Windows versions (e.g., Windows 7, Windows 8.1): These are not supported by current versions of Docker Desktop.
  • Insufficient RAM (e.g., 2GB): Docker requires a minimum of 4GB.
  • Disabled virtualization: Docker won't function without hardware virtualization enabled.

  Citations:

  • Install Docker Desktop on Windows, https://docs.docker.com/desktop/install/windows-install/
  • Docker Toolbox overview, https://docs.docker.com/toolbox/toolbox_install_windows/
  • Step-By-Step: Enabling Hyper-V for use on Windows 10, https://blogs.technet.microsoft.com/canitpro/2015/09/08/step-by-step-enabling-hyper-v-for-use-on-windows-10/

• Question 2

  HOTSPOT -
  Complete the sentence by selecting the correct option in the answer area.
  Hot Area:
  
  

  Correct Answer: See interactive view.

  Explanation:

  Based on the question, suggested answer and discussion, the AI agrees with the suggested answer.
  
  Reasoning:
  The provided statement accurately describes a Deep Learning Virtual Machine (DLVM). DLVMs are pre-configured environments that are specifically designed for deep learning tasks, and they leverage GPU instances to accelerate computations. This is crucial because deep learning models often require significant computational resources, and GPUs provide the necessary parallel processing capabilities through frameworks like CUDA. The pre-configured nature of DLVMs also saves time and effort for developers by providing pre-installed deep learning frameworks and tools.
  
  The reason of choosing this answer:

  • DLVMs are built to utilize GPU instances for enhanced deep learning performance.
  • They come with pre-installed deep learning frameworks and libraries, streamlining the setup process.
  • The use of CUDA, a parallel computing platform, is vital for many deep learning applications.

  Why not other answers:
  Other potential options that do not mention the utilization of GPUs or the pre-configured environment for deep learning may be incorrect. The key feature of a DLVM is its optimization for deep learning tasks through GPU acceleration and a readily available development environment.

  
  

  Citations:

  • Deep Learning Virtual Machine, https://azuremarketplace.microsoft.com/en-us/marketplace/apps/microsoft-dsvm.deep-learning-vm
  • CUDA, https://developer.nvidia.com/cuda-zone

• Question 3

  You need to implement a Data Science Virtual Machine (DSVM) that supports the Caffe2 deep learning framework.
  Which of the following DSVM should you create?
  

  • A. Windows Server 2012 DSVM
  • B. Windows Server 2016 DSVM
  • C. Ubuntu 16.04 DSVM
  • D. CentOS 7.4 DSVM

  Correct Answer: C

  Explanation:

  Based on the provided information and discussion, the AI recommends choosing option C: Ubuntu 16.04 DSVM.
  
  Reasoning:
  The primary reason for selecting Ubuntu 16.04 DSVM is its well-established compatibility and community support for deep learning frameworks, specifically Caffe2. The discussion summary highlights that Ubuntu 16.04 is a preferred choice due to this strong support.
  
  Reasons for not choosing other options:
  While the other options might potentially support Caffe2, Ubuntu 16.04 is specifically mentioned as a supported platform within the discussion. There isn't sufficient evidence to suggest that Windows Server 2012, Windows Server 2016, or CentOS 7.4 are better suited or equally supported for Caffe2 in the context of a DSVM. The specific recommendation of Ubuntu 16.04 tips the scale in its favor.
  
  

  Citations:

  • Azure Data Science Virtual Machine, https://azure.microsoft.com/en-us/services/virtual-machine-scale-sets/data-science-virtual-machines/

• Question 4

  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 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 Geo AI Data Science Virtual Machine (Geo-DSVM) Windows edition.
  Will the requirements be satisfied?
  

  • A. Yes
  • B. No

  Correct Answer: B

  Explanation:

  The AI recommends that the suggested answer, "No", is the correct choice.
  
  Reasoning:
  The scenario requires a virtual machine suitable for:

  • Machine learning model deployment
  • GPU processing
  • PostgreSQL database compatibility

  The Geo AI Data Science Virtual Machine (Geo-DSVM) Windows edition is primarily optimized for geospatial analysis and may not be the best fit for general-purpose machine learning tasks requiring GPU acceleration and PostgreSQL. While it might be possible to install the necessary components, a standard Data Science Virtual Machine (DSVM) with GPU support would be a more appropriate and readily configured choice.
  
  Why other options are not suitable:
  "Yes" is incorrect because the Geo-DSVM is not specifically designed for GPU-accelerated machine learning with PostgreSQL. It adds unnecessary geospatial tools that are not required in the prompt.
  
  The Data Science Virtual Machine (DSVM) offers configurations that include GPU support and pre-installed tools for data science and machine learning, making it a more suitable option. While installing PostgreSQL on a DSVM might be necessary depending on the specific image, the DSVM provides a better starting point for the described requirements.

  
  Citations:

  • Azure Data Science Virtual Machine documentation, https://learn.microsoft.com/en-us/azure/machine-learning/data-science-virtual-machine/overview

• Question 5

  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 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 Deep Learning Virtual Machine (DLVM) Windows edition.
  Will the requirements be satisfied?
  

  • A. Yes
  • B. No

  Correct Answer: A

  Explanation:

  The AI suggests the answer is No.
  
  Reasoning:
  The question specifies the need for a virtual machine that supports GPU processing and works with a PostgreSQL database. While a Deep Learning Virtual Machine (DLVM) Windows edition can support GPU processing, it's not the ideal choice for a PostgreSQL database.
  
  Here's why:

  • PostgreSQL on Windows: While PostgreSQL can be installed on Windows, it is more commonly and easily deployed on Linux-based systems. Setting up and managing PostgreSQL on Windows can sometimes present more challenges compared to Linux.
  • Linux for Machine Learning and Databases: Linux distributions often provide better support and performance for both machine learning tasks and database management systems like PostgreSQL.
  • Alternative Virtual Machines: There are other virtual machine options, such as the Data Science Virtual Machine (DSVM) for Ubuntu, which comes pre-configured with tools for data science, including GPU support and compatibility with PostgreSQL.

  
  Why not 'Yes':
  Choosing 'Yes' implies that the DLVM Windows edition perfectly satisfies all requirements. While it meets the GPU processing need, its suitability for PostgreSQL is less optimal than Linux-based alternatives.
  
  Therefore, the recommendation does not fully satisfy the requirements.

  
  Citations:

  • Azure Data Science Virtual Machine, https://azure.microsoft.com/en-us/services/virtual-machines/data-science-virtual-machines/
  • Deep Learning Virtual Machines, https://learn.microsoft.com/en-us/azure/machine-learning/data-science-virtual-machine/deep-learning-vm-overview

• Question 6

  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 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

  Correct Answer: B

  Explanation:

  The AI recommends the answer B (No).
  
  Reasoning:
  The primary reason for this recommendation is that the Data Science Virtual Machine (DSVM) Windows edition does not come pre-installed with PostgreSQL, as highlighted in the discussion. The question explicitly states the need for a virtual machine with "necessary tools built into it." Since PostgreSQL is a requirement and not included by default in the Windows DSVM, the recommendation to use the Windows edition does not satisfy the stated requirements. While it's possible to install PostgreSQL on the Windows DSVM, it doesn't fulfill the condition of having the necessary tools built-in.
  
  Furthermore, although both Windows and Linux DSVMs support GPU processing, the emphasis here is on pre-installed tools. The Linux DSVM is generally favored in scenarios where PostgreSQL and other open-source data science tools are required out-of-the-box.
  
  Why other options are not recommended:
  Option A (Yes) is incorrect because the DSVM Windows edition does not include PostgreSQL pre-installed. The core requirement specified is that the virtual machine must have the necessary tools (including PostgreSQL) already built-in, which the Windows DSVM does not provide.

  • Citations:
  • Azure Data Science Virtual Machine Documentation, https://learn.microsoft.com/en-us/azure/machine-learning/data-science-virtual-machine/overview

• Question 7

  DRAG DROP -
  You have been tasked with moving data into Azure Blob Storage for the purpose of supporting Azure Machine Learning.
  Which of the following can be used to complete your task? Answer by dragging the correct options from the list to the answer area.
  Select and Place:
  
  

  Correct Answer: See interactive view.

  Explanation:

  The AI agrees with the suggested answer.
  The suggested answer includes Azure Storage Explorer, AzCopy, Python, and SSIS, all of which are valid methods for moving data to Azure Blob Storage for use with Azure Machine Learning.
  Here's why:

  • Azure Storage Explorer: A free, standalone app from Microsoft that allows you to easily manage Azure cloud storage resources, including uploading and downloading data to Blob Storage.
  • AzCopy: A command-line utility designed for high-performance, scriptable data transfer to and from Azure Storage. It's ideal for large datasets and automated processes.
  • Python: Azure provides Python libraries (e.g., `azure-storage-blob`) that allow you to programmatically interact with Blob Storage. This is useful for integrating data transfer into machine learning pipelines.
  • SSIS (SQL Server Integration Services): A data integration platform that can be used to extract, transform, and load (ETL) data into Azure Blob Storage. It's particularly useful when you need to move data from on-premises databases or other data sources.

  
  The discussion summary confirms the correctness of the answer without presenting alternative solutions, solidifying the AI's recommendation to accept the proposed answer.

  • Reference: Move data to and from Azure Blob storage

  
  Citations:

  • Move data to and from Azure Blob storage, https://docs.microsoft.com/en-us/azure/machine-learning/team-data-science-process/move-azure-blob

• Question 8

  HOTSPOT -
  Complete the sentence by selecting the correct option in the answer area.
  Hot Area:
  
  

  Correct Answer: See interactive view.

  Explanation:

  The AI agrees with the suggested answer.
  The suggested answer correctly identifies the appropriate module and its function. The question implies a need to convert data to a format compatible with Weka, and the "Convert to ARFF" module directly addresses this.
  Reasoning:
  

  • The ARFF (Attribute-Relation File Format) is explicitly designed for use with Weka. The documentation confirms that the module converts data to this format.
  • The explanation provided accurately describes the purpose of the ARFF format and its relevance to Weka.
  • The provided reference link is a valid source from Microsoft's Azure Machine Learning documentation, which supports the answer's claims.

  Why other options are not correct:
  While other data transformation or export options might exist in Azure Machine Learning, none are as directly and specifically related to Weka compatibility as the "Convert to ARFF" module. The question asks for a method to prepare data *for Weka*, making ARFF the most logical and direct solution.

  The explanation includes:

  • Suggested Answer: The AI agrees with the original suggested answer.
  • Reasoning: Detailed explanation supporting the choice of the "Convert to ARFF" module and its relevance to Weka, and that the module is suitable to the scenario described in the question.
  • Why other options are not correct: The AI explains why other options are not a good fit, as none are as specifically designed for Weka as ARFF.
  • Citations: The AI confirms the presence of a valid and helpful reference link.

  Here's the recommended answer again:

  Use the Convert to ARFF module in Azure Machine Learning Studio, to convert datasets and results in Azure Machine Learning to the attribute-relation file format used by the Weka toolset. This format is known as ARFF.
  The ARFF data specification for Weka supports multiple machine learning tasks, including data preprocessing, classification, and feature selection. In this format, data is organized by entities and their attributes, and is contained in a single text file.

  Citations:

  • Convert to ARFF Module Reference, https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/convert-to-arff

• Question 9

  You have been tasked with designing a deep learning model, which accommodates the most recent edition of Python, to recognize language.
  You have to include a suitable deep learning framework in the Data Science Virtual Machine (DSVM).
  Which of the following actions should you take?
  

  • A. You should consider including Rattle.
  • B. You should consider including TensorFlow.
  • C. You should consider including Theano.
  • D. You should consider including Chainer.

  Correct Answer: B

  Explanation:

  The AI agrees with the suggested answer. The suggested answer is B: You should consider including TensorFlow.
  Reasoning:
  The question requires selecting a suitable deep learning framework for language recognition in a Data Science Virtual Machine (DSVM), compatible with the latest Python version. TensorFlow is a strong choice because:

  • It is a widely used open-source library developed by Google, making it robust and well-supported.
  • It excels in data flow and differentiable programming, essential for deep learning models.
  • It has excellent Python support, fulfilling the question's requirements.
  • TensorFlow is extensively used in Natural Language Processing (NLP) tasks, aligning with the language recognition requirement.

  
  Reasons for not choosing other options:

  • A. Rattle: Rattle (R Analytical Tool To Learn Easily) is a GUI for data mining using R. It is not a deep learning framework and is primarily used with R, not Python, making it unsuitable for the task.
  • C. Theano: Theano was one of the earlier deep learning frameworks, but it is no longer actively maintained. Using an unsupported framework is not advisable for a new project.
  • D. Chainer: Chainer is another deep learning framework, but it has a smaller community and less active development compared to TensorFlow. While Chainer can be used for deep learning, TensorFlow offers better resources and broader applicability.

  Citations:

  • TensorFlow, https://www.tensorflow.org/
  • Rattle, https://rattle.togaware.com/
  • Theano, http://deeplearning.net/software/theano/
  • Chainer, https://chainer.org/

• Question 10

  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 have been tasked with evaluating your model on a partial data sample via k-fold cross-validation.
  You have already configured a k parameter as the number of splits. You now have to configure the k parameter for the cross-validation with the usual value choice.
  Recommendation: You configure the use of the value k=3.
  Will the requirements be satisfied?
  

  • A. Yes
  • B. No

  Correct Answer: B

  Explanation:

  The AI agrees with the suggested answer.
  The recommended answer is B (No).
  Reasoning: The question specifies configuring the *k* parameter for cross-validation with the "usual value choice." While a value of *k*=3 is technically possible, the most common and widely recommended values for *k* in k-fold cross-validation are 5 or 10. Using *k*=3 would be considered less typical. Therefore, configuring *k*=3 does not satisfy the requirement of using the usual value choice.
  Why other options are not suitable:

  • Option A (Yes) is incorrect because *k*=3 is not the "usual" or most common value used in k-fold cross-validation.

  Citations:

  • What is k-fold Cross Validation?, https://www.ibm.com/topics/k-fold-cross-validation
  • Cross-Validation, https://scikit-learn.org/stable/modules/cross_validation.html