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DP-100 Microsoft Practice Test Questions and Exam Dumps

Question No 1:

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

Answer: C

Explanation:

The Data Science Virtual Machine (DSVM) is a curated virtual machine offered by Microsoft Azure, pre-configured with popular data science and deep learning tools. When selecting a DSVM to support a specific framework like Caffe2, it’s crucial to consider compatibility with the operating system, dependencies, and the pre-installed software environment that best supports that framework.

Caffe2 is a lightweight, modular deep learning framework developed by Facebook. While it has since been merged into PyTorch, there are still scenarios where direct use or legacy support for Caffe2 is needed. In terms of DSVM support, Caffe2 was available and supported primarily in the Ubuntu-based Data Science Virtual Machines.

Let’s evaluate the options:

Option A – Windows Server 2012 DSVM:
This is not a recommended or supported environment for Caffe2. Windows Server 2012 is outdated and lacks many of the modern dependencies and GPU support necessary for deep learning frameworks like Caffe2. Microsoft has also deprecated many tools and services for this OS. Thus, A is not suitable.

Option B – Windows Server 2016 DSVM:
While Windows Server 2016 is more modern and does support many data science tools, it is not the preferred or most stable environment for Caffe2. Deep learning frameworks, especially Caffe2, often depend on Linux-based toolchains and libraries (e.g., CUDA, cuDNN, Python packages) that are better supported on Linux distributions. So, although possible, B is not the ideal or recommended platform.

Option C – Ubuntu 16.04 DSVM:
This is the correct answer. Ubuntu 16.04 is the officially supported and most commonly used Linux distribution for many deep learning frameworks, including Caffe2. Microsoft's Ubuntu-based DSVM includes pre-installed support for many AI/ML frameworks (such as TensorFlow, PyTorch, Caffe, Caffe2, CNTK) and is optimized for GPU use and performance. Ubuntu also has excellent support for package managers and system libraries that are essential for deep learning workloads.

Option D – CentOS 7.4 DSVM:
While CentOS is a stable Linux distribution, it is less commonly used in the machine learning and deep learning communities compared to Ubuntu. CentOS-based DSVMs are not the primary recommendation from Microsoft for deep learning tasks involving frameworks like Caffe2. Moreover, fewer pre-built binaries and community support exist for Caffe2 on CentOS compared to Ubuntu. So, D is not the best choice.

In summary, Ubuntu 16.04 DSVM is the most appropriate option because it offers the best support for the Caffe2 framework in terms of compatibility, pre-installed tooling, and system libraries. Microsoft Azure's Ubuntu-based DSVM is specifically designed to handle deep learning workloads, making C the correct answer.

Question No 2:

You are tasked with deploying a machine learning model that requires GPU processing and uses a PostgreSQL database to forecast prices. You plan to create a virtual machine with all the necessary tools pre-installed. Recommendation: 

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

A Yes
B No

Correct Answer: B

Explanation:

To determine whether the Geo AI Data Science Virtual Machine (Geo-DSVM) Windows edition satisfies the requirements, we must evaluate the key components of the scenario:

1. Requirement for GPU Processing:
   The model explicitly requires GPU processing to handle computationally intensive machine learning tasks. The availability of a GPU-enabled environment is critical for training or running models that leverage hardware acceleration (e.g., deep learning models using TensorFlow or PyTorch).

2. Use of PostgreSQL Database:
   PostgreSQL is an open-source relational database commonly used in data science workflows. While this requirement is relatively straightforward and can be met by most VMs (since PostgreSQL can be installed on various environments), it still must be supported or installable on the selected VM.

3. Forecasting Prices (Machine Learning):
   This implies the need for a data science environment preconfigured with appropriate tools, such as Python, R, Jupyter notebooks, machine learning libraries, and data connectivity tools.

Now, let’s consider what the Geo AI Data Science Virtual Machine is designed for:

• The Geo AI DSVM is tailored for geospatial analytics, including tools like ArcGIS, GDAL, and other geographic libraries.

• It includes various Python and R tools for spatial data processing and analysis.

• However, it is not specifically optimized or provisioned for GPU workloads, especially not on the Windows edition. While it may support some data science tasks, it is not a GPU-enabled VM by default.

• Moreover, GPU support is typically more robust and better integrated in the Linux-based DSVMs. For tasks requiring GPU processing, a Data Science Virtual Machine (DSVM) with GPU capabilities, such as one based on Linux and using an NC-series or ND-series Azure VM, would be more suitable.

Hence, the Geo AI DSVM (Windows) is not intended for general-purpose machine learning requiring GPU acceleration. It is a specialized image for geographic data processing, not optimized for performance-intensive modeling.

Conclusion: While the Geo AI DSVM may handle PostgreSQL and has data science tools, it does not meet the critical requirement of GPU support, and therefore does not satisfy the full set of requirements for this scenario. The correct virtual machine should be a GPU-enabled Data Science Virtual Machine, ideally based on Linux for better compatibility with machine learning libraries and GPU drivers.

Therefore, the answer is B, the recommendation does not satisfy the requirements.

Question No 3:

You are deploying a machine learning model that requires GPU processing and connects to a PostgreSQL database to forecast prices. You plan to create a virtual machine with the necessary tools pre-installed. 

You consider using a Deep Learning Virtual Machine (DLVM) Windows edition. Will this recommendation meet the requirements?

A. Yes
B. No

Answer: B

Explanation:

In this scenario, the goal is to deploy a machine learning model that relies on GPU processing and uses a PostgreSQL database. You need to select a virtual machine configuration that meets both the hardware (GPU) and software (machine learning frameworks and database integration) requirements.

The recommendation in question is to use a Deep Learning Virtual Machine (DLVM) Windows edition. While DLVMs are specialized virtual machines optimized for machine learning and deep learning workloads, including support for GPU acceleration and preinstalled frameworks like TensorFlow, PyTorch, and others, the Windows edition has some critical limitations that make it an unsuitable choice in this case.

Here’s why:

• GPU Support: While GPU acceleration is available on both Linux and Windows DLVMs, Linux-based DLVMs have broader and more stable support for machine learning libraries and GPU drivers. Most deep learning libraries are natively developed for and best supported on Linux, which offers better compatibility and performance for production-grade workloads involving GPUs.

• Machine Learning Frameworks: Although some machine learning libraries are available on Windows, many advanced tools, packages, and ML pipeline components have limited or no support on Windows, or require extra configuration. Most official documentation and community support for frameworks like TensorFlow, PyTorch, and CUDA assume a Linux environment.

• PostgreSQL Integration: PostgreSQL can run on both Windows and Linux. However, integration with machine learning workflows (e.g., data ingestion, preprocessing, and feature engineering) is generally more seamless on Linux, especially when using open-source tools and scripts.

• Administrative Overhead: Using Windows DLVMs for deep learning often requires additional setup and troubleshooting to achieve the same performance and compatibility that a Linux DLVM offers by default.

In summary, while the Windows edition of a DLVM might provide some tools, it does not optimally meet the requirements for GPU-intensive machine learning tasks and tight PostgreSQL integration. A Linux-based DLVM would be the more appropriate recommendation, offering better performance, compatibility, and ease of integration with common machine learning toolsets.

Therefore, the correct answer is B, because the Windows edition of the Deep Learning VM does not fully satisfy the operational and compatibility needs of a GPU-based ML model deployment.

Question No 4:

You have been assigned to deploy a machine learning model that uses a PostgreSQL database and requires GPU processing to forecast prices. You are about to provision a virtual machine that includes the necessary tools. 

You decide to use a Data Science Virtual Machine (DSVM) Windows edition. Will this choice meet the requirements?

A Yes
B No

Correct answer: B

Explanation:

In this scenario, you are tasked with setting up a virtual machine (VM) to support a machine learning model that depends on two key components: GPU processing and a PostgreSQL database. The recommendation provided is to use the Data Science Virtual Machine (DSVM) – Windows edition. To determine if this recommendation satisfies the requirements, we need to examine what the DSVM offers and whether it aligns with the specific needs.

The Data Science Virtual Machine (DSVM) is a curated VM image offered by Microsoft on Azure. It comes preinstalled with a wide range of data science tools and environments, such as Python, R, Jupyter notebooks, machine learning libraries, and more. It is available in both Windows and Linux editions.

Let’s now evaluate the key requirements:

1. GPU Processing:
   While DSVM supports GPU-enabled VM sizes, not all configurations (especially Windows-based images) are optimized or certified for advanced GPU use. In contrast, the Linux-based DSVM is more commonly used and better supported for deep learning and GPU-intensive workloads, due to the availability of native NVIDIA CUDA drivers and compatibility with frameworks like TensorFlow, PyTorch, etc.
   Windows-based DSVMs may have limited or less optimal support for deep learning tools requiring GPUs, and configuring GPU drivers can be more complex or error-prone in Windows environments for such tasks.

2. PostgreSQL Support:
   PostgreSQL is an open-source relational database that can run on Windows. While the Windows DSVM can support PostgreSQL (since it can be installed manually), it does not come preconfigured with PostgreSQL. On the other hand, Linux-based DSVMs are more commonly used in environments that rely on open-source databases like PostgreSQL and are often better suited for integrating such tools in machine learning workflows.

Based on these factors, using the Windows edition of the DSVM may not fully satisfy the GPU processing requirement effectively or offer the most seamless integration with PostgreSQL, especially when high-performance deep learning tasks are involved. The Linux-based DSVM is typically preferred for these kinds of scenarios due to better GPU support and native compatibility with open-source tools.

Hence, while a Windows DSVM might work for some aspects, it does not sufficiently satisfy all the requirements—particularly the GPU processing aspect and smooth PostgreSQL integration.

The correct answer is B, as the recommendation does not fully meet the specified requirements.

Question No 5:

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:

When designing a deep learning model for language recognition, especially with the latest version of Python, choosing a modern, well-supported, and powerful deep learning framework is critical. Among the options provided, TensorFlow stands out as the most appropriate and practical choice for the given scenario.

Why TensorFlow is the Best Choice:

TensorFlow is an open-source deep learning and machine learning framework developed by Google. It is one of the most widely adopted frameworks in both academic and industry settings. TensorFlow supports a wide variety of tasks, including natural language processing (NLP), image recognition, time series prediction, and more. Here are the main reasons it is the most suitable choice:

• Compatibility with Python: TensorFlow is continuously updated to support the latest versions of Python. This is especially important when working in a modern development environment or using the latest version of the Data Science Virtual Machine (DSVM), which comes with the newest tools and language support.

• Strong NLP Capabilities: TensorFlow integrates well with TensorFlow Hub, TensorFlow Text, and models like BERT, making it powerful for language recognition and understanding tasks.

• Active Development and Community Support: TensorFlow has an active community and strong support from Google, ensuring that it receives regular updates, bug fixes, and feature enhancements.

• Easy Integration with DSVM: Microsoft’s DSVM (Data Science Virtual Machine) is a Windows or Linux-based virtual machine image pre-installed with many data science and machine learning tools, including TensorFlow. This makes it easy to integrate and deploy TensorFlow-based models.

Why the Other Options Are Less Suitable:

• A. Rattle:
  Rattle is a GUI-based data mining tool built on R, not Python. It is used for building decision trees, clustering, and other traditional machine learning models. It is not suitable for deep learning, especially in a Python-based workflow or for NLP tasks.

• C. Theano:
  Theano was one of the earlier deep learning frameworks, but it has been deprecated and is no longer actively maintained. While it historically supported Python, it does not work reliably with the latest Python versions, and most modern projects have moved to TensorFlow or PyTorch.

• D. Chainer:
  Chainer was an early deep learning framework that emphasized flexibility and ease of use. However, it has also been discontinued and merged into PyTorch development by its creators. For new projects, especially those needing long-term support and compatibility with current Python releases, Chainer is not recommended.

Considering the requirements — deep learning for language recognition, compatibility with the latest Python version, and integration into the DSVM — the most logical and future-proof choice is TensorFlow. Therefore, the correct answer is B.

Question No 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 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

Answer: B

Explanation:

In the context of k-fold cross-validation, the parameter k refers to the number of equally (or nearly equally) sized folds that the dataset is split into for training and validation. The model is trained k times, each time using k - 1 folds for training and 1 fold for validation. The overall performance is then averaged across all k trials to assess the model’s generalization ability.

When determining whether a specific value of k like k = 3 is appropriate, we must consider two things: the size of the data sample and the usual or standard practice in machine learning model evaluation.

The usual value choice for k in k-fold cross-validation is commonly k = 5 or k = 10. These values are popular because they provide a good trade-off between bias and variance in model evaluation:

• A lower value of k (like 2 or 3) leads to higher bias, as each training set is much smaller.

• A higher value of k (e.g., 10) leads to lower bias, but may also increase variance and computational cost.

Using k = 3 is generally not considered a standard or usual practice. While technically valid, it is less commonly used because it divides the dataset into very few folds, meaning that each training iteration uses a relatively small portion of the data. This can result in less reliable estimates of model performance due to higher variance and insufficient representation in each fold.

Furthermore, the prompt specifically mentions evaluating the model on a partial data sample, which makes using a low k value like 3 even more questionable. With limited data, a low k means even less training data per fold, which reduces the evaluation quality. The intent of k-fold cross-validation is to maximize the use of limited data by rotating the validation and training sets efficiently. Using k = 5 or 10 is generally better for this purpose.

Therefore, while k = 3 is technically valid in some contexts, it does not satisfy the requirement of using the usual value choice for k in k-fold cross-validation. As such, the recommendation fails to meet the stated requirement.

The correct answer is B.

Question No 7:

You are designing a data science solution on Azure that involves predicting customer churn based on a dataset. You need to ensure that your model’s performance is evaluated and that it will be properly optimized before deployment. 

Which of the following actions should you take to accomplish this?

A Use the cross-validation technique to evaluate the model's performance and tune hyperparameters using grid search.
B Deploy the model directly to the production environment without evaluation.
C Perform hyperparameter tuning with a random search method on the model's parameters without validating its performance.
D Use the default model parameters without modifying or tuning them for optimization.

Answer: A

Explanation:

Ensuring that a machine learning model is both effective and optimized is a critical part of the data science workflow, especially when predicting customer churn or any other business-critical task. To ensure that the model performs well and generalizes to unseen data, specific techniques need to be used during development and before deployment. Let's explore each option:

Option A: Use the cross-validation technique to evaluate the model's performance and tune hyperparameters using grid search.

This is the correct approach. Cross-validation is an essential technique in machine learning to assess a model’s performance reliably. It involves splitting the data into multiple subsets (or folds), training the model on some folds, and testing it on others. This ensures that the model is evaluated on all available data and helps prevent overfitting to a particular split of the dataset.
Additionally, hyperparameter tuning is a vital process to improve model performance, and grid search is a common method used to find the best set of parameters. Grid search evaluates all possible combinations of hyperparameters, ensuring that the best possible configuration is chosen.
This combination of cross-validation for evaluation and grid search for hyperparameter optimization is a well-established best practice in the field of machine learning, ensuring that the model is robust, generalized, and optimized before deployment.

Option B: Deploy the model directly to the production environment without evaluation.

This is not advisable. Deploying a model without evaluating its performance is risky because the model might not perform well, may introduce biases, or could make incorrect predictions. Evaluation techniques like cross-validation are crucial to understanding how well the model generalizes to unseen data and ensuring it meets the required performance metrics. Deploying an untested model may lead to significant business issues and a poor customer experience.

Option C: Perform hyperparameter tuning with a random search method on the model's parameters without validating its performance.

While random search can be a good technique for hyperparameter tuning (and is generally faster than grid search), it is essential to validate the performance of the model after tuning. Without proper validation, you cannot be sure if the hyperparameter changes have improved the model's performance or just resulted in overfitting. Cross-validation ensures that the hyperparameters are not only tuned but also that the model performs well on different subsets of the data, thus reducing the risk of overfitting.

Option D: Use the default model parameters without modifying or tuning them for optimization.

Using default model parameters is often not optimal. Default settings are designed to work reasonably well in general cases, but they are not tailored to the specific characteristics of your data or problem. Tuning the model's hyperparameters is crucial for achieving the best performance for a given task, especially in complex tasks like predicting customer churn, where the default settings may not be sufficient.

To ensure that the model is properly evaluated and optimized, Option A is the correct answer. Cross-validation helps in assessing the model’s ability to generalize, and grid search ensures the best hyperparameter settings. These steps are critical for ensuring that the model is both reliable and optimized before it is deployed to production.

Question No 8:

You are tasked with designing a machine learning model to predict customer churn using Azure Machine Learning. To ensure the model is both effective and optimized before deployment, 

Which of the following strategies should you implement?

A. Perform cross-validation to evaluate the model’s performance, then use grid search for hyperparameter tuning.

B. Deploy the model directly to production without any evaluation to save time.

C. Use random search to tune the hyperparameters but skip performance validation.

D. Use the default hyperparameters provided by the model without any tuning.

Answer:
A. Perform cross-validation to evaluate the model’s performance, then use grid search for hyperparameter tuning.

Explanation:

When building machine learning models, especially in a production environment like Azure, it’s crucial to ensure the model is both accurate and efficient before deployment. This involves a few key steps: evaluating the model’s performance and tuning its parameters. Let’s examine why Option A is the best approach and why the other options are less optimal.

1. Option A: Cross-validation and grid search
   Cross-validation is a powerful technique used to assess the model’s performance. In this approach, the dataset is divided into multiple subsets (folds), and the model is trained on some of these folds while being tested on the remaining ones. This helps reduce the risk of overfitting and gives a more generalized assessment of how the model might perform on unseen data. Grid search is then used for hyperparameter tuning, systematically trying different combinations of model parameters to find the optimal configuration. By combining cross-validation with grid search, you ensure that the model is both well-evaluated and tuned for the best performance, making it ready for deployment.

2. Option B: Deploying without evaluation
   Skipping evaluation before deployment is risky and could lead to poor model performance. A model that hasn't been properly tested might not generalize well, potentially making inaccurate predictions in a live environment. This could hurt business outcomes and lead to wasted resources.

3. Option C: Random search without validation
   While random search can help in tuning hyperparameters quickly, it doesn’t provide the same level of certainty as cross-validation regarding how the model will perform in different situations. Tuning parameters without validating the model could lead to overfitting or poor generalization, which compromises the model’s effectiveness.

4. Option D: Using default hyperparameters
   Relying on default hyperparameters is often insufficient for complex tasks like predicting customer churn. Models typically need to be customized and fine-tuned to the specific dataset to achieve optimal performance. Using default settings without tuning can lead to subpar model performance.

By following the best practices of cross-validation and hyperparameter tuning through grid search (as described in Option A), you ensure the model is thoroughly evaluated and optimized before it is deployed. This process is facilitated by tools like Azure Machine Learning, which offers automated machine learning and other advanced features for model optimization, making it easier to achieve high-performance models in a production setting.