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NVIDIA NCP-ADS Exam Syllabus Topics:

NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. You are building an MLOps pipeline for a predictive model that uses tabular data with both categorical and numerical features.
To ensure efficient data processing and optimal model training on an NVIDIA GPU, which of the following data types would be most suitable for a categorical feature representing different product categories?

A) Float64
B) Int32
C) Int64
D) String

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

3. You are managing a data processing pipeline that utilizes NVIDIA RAPIDS on GPUs for accelerated data transformations. During execution, you notice that the pipeline is not achieving expected performance gains.
What is the most effective approach to monitor and diagnose bottlenecks in this pipeline using NVIDIA technologies?

A) Use NVIDIA Nsight Systems to profile kernel execution times and memory transfers.
B) Enable RAPIDS memory pool logging to check for memory fragmentation and out-of-memory errors.
C) Run the pipeline on CPU instead of GPU to compare execution times.
D) Reduce the dataset size and rerun the pipeline without profiling tools to check for performance improvements.

4. Which of the following tools in the NVIDIA AI stack is specifically designed to accelerate the deployment of machine learning models for production by optimizing inference performance?

A) cuBLAS
B) cuML
C) Triton Inference Server
D) DLA (Deep Learning Accelerator)

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

Solutions: