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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. A machine learning engineer is working on a multi-GPU workload using Dask and RAPIDS to process a massive dataset efficiently. However, they notice that GPU utilization is not optimal, and data transfer between GPUs is slowing down computation.
What is the best approach to minimize data transfer overhead and maximize parallel efficiency?

A) Load the dataset into a Pandas DataFrame and distribute computations across GPUs manually
B) Use Dask's single-threaded scheduler to avoid multi-threading overhead
C) Transfer data between GPUs using NumPy's .copy() function
D) Use NVLink to enable high-bandwidth inter-GPU communication and dask_cuda.LocalCUDACluster

2. You are designing an accelerated ETL pipeline to process large-scale datasets in a data science workflow.
Which of the following are key considerations when selecting the right tools and methods for implementing this pipeline? (Select two)

A) Relying on traditional single-threaded processing for the extraction phase to reduce complexity.
B) Ensuring the ETL pipeline uses only batch processing for data ingestion.
C) Leveraging parallel processing and distributed computing frameworks like Apache Spark to speed up the transformation phase.
D) Using a single storage location for both raw and transformed data to simplify the workflow.
E) Using GPU-accelerated libraries such as RAPIDS for data transformation to enhance processing speed.

3. You are working on a data science project where you need to process a large dataset containing
500 million records. You want to determine whether GPU acceleration would significantly improve performance.
Which of the following factors best indicates that you should use an accelerated computing solution like RAPIDS?

A) The dataset consists of simple arithmetic operations on a few columns and can be processed using vectorized NumPy operations.
B) The dataset has high-dimensional sparse features and requires complex operations such as nearest neighbor search and clustering.
C) The dataset is a structured table with less than 100,000 records and can be handled efficiently with a Pandas DataFrame.
D) The dataset is heavily structured but mainly requires text-based analysis using regex-based search and manipulation.

4. A machine learning engineer is training a convolutional neural network (CNN) on an NVIDIA GPU and needs to maximize throughput while avoiding OOM errors.
Which of the following techniques is the most effective way to balance memory efficiency and training speed?

A) Allocating a fixed batch size without monitoring memory usage
B) Using a batch size of 1 to minimize memory usage
C) Loading all dataset samples into GPU memory at the start of training
D) Using dynamic batch sizing based on available GPU memory

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

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