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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. You are working on a machine learning problem that involves training a deep learning model on a dataset with billions of records. The dataset is stored in a distributed cloud storage system.
Given the need for acceleration, which is the most effective approach?

A) Reduce the dataset to a small representative sample to avoid the need for specialized acceleration.
B) Load the entire dataset into RAM on a single powerful CPU-based machine before starting model training.
C) Store the dataset in a relational database and query it sequentially using SQL before training the model.
D) Use GPU acceleration with libraries like RAPIDS AI or TensorFlow to leverage parallel processing.

2. You are deploying an NVIDIA GPU-accelerated machine learning model in a Docker container and want to ensure that your application can leverage the GPU efficiently.
What is the best way to manage CUDA dependencies and avoid compatibility issues inside your Docker container?

A) Use a base Ubuntu image and install TensorFlow, PyTorch, and CUDA using pip install inside the container.
B) Use NVIDIA's official Docker images from NVIDIA GPU Cloud (NGC), which come with pre-installed CUDA and AI frameworks.
C) Manually install CUDA and cuDNN inside the container by downloading them from NVIDIA's website and setting environment variables.
D) Disable GPU acceleration in Docker and force computations on the CPU to avoid CUDA compatibility issues.

3. You are working on a predictive maintenance model for industrial equipment. The dataset includes various sensor readings, categorical metadata, and timestamped events.
Which data type is the best choice for a feature representing the operating status of a machine, which has three possible states: "Idle", "Running", and "Error"?

A) Text (storing states as raw strings like "Idle", "Running", "Error").
B) Floating-point representation (e.g., 0.1 for Idle, 0.5 for Running, 0.9 for Error).
C) Categorical encoding (one-hot encoding or ordinal encoding).
D) Integer (0, 1, 2) to represent each state numerically.

4. A data scientist is working with a large dataset for a machine learning model and wants to accelerate feature engineering using a GPU.
Which of the following approaches will provide the most significant performance boost when using GPU acceleration?

A) Using a single-threaded feature extraction approach to avoid overhead from parallelization.
B) Reducing dataset size by randomly removing data points without considering class balance.
C) Using RAPIDS cuDF and cuML libraries to perform feature transformations on a GPU.
D) Using traditional pandas DataFrames and NumPy operations optimized for CPU processing.

5. You are tasked with comparing the performance of different GPU-accelerated frameworks for a deep learning model. The frameworks you are considering are TensorFlow, PyTorch, and CUDA. To evaluate the performance, you decide to implement a benchmark that measures GPU efficiency, memory usage, and speed.
Which of the following actions should you take to design an effective benchmark? (Select two)

A) Use CPU-based implementations of the same frameworks for comparison.
B) Use a common dataset for all frameworks to ensure comparability.
C) Benchmark only the training phase of the deep learning model.
D) Use a batch size that is optimal for each framework's memory management.
E) Measure GPU utilization and memory usage, but ignore the network and disk I/O.

Solutions: