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NVIDIA NCP-ADS Exam Syllabus Topics:

NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. You are running a data science project on a cloud environment, where you need to optimize the GPU utilization for real-time data processing tasks.
Which of the following practices should you consider to maximize GPU performance? (Select two)

A) Use NVIDIA CUDA libraries optimized for data science tasks to accelerate computations.
B) Prioritize using CPU for computationally expensive tasks and reserve GPU for I/O operations.
C) Scale the cloud infrastructure vertically by increasing the size of the GPU in use.
D) Implement model inference in a batch mode rather than in a real-time streaming mode for better performance.
E) Use data parallelism techniques to split the workload evenly across multiple GPUs.

2. Which NVIDIA technology is specifically designed for accelerating deep learning workloads in the cloud?

A) TensorRT
B) NVIDIA A100
C) NVIDIA Jetson
D) NVIDIA Tesla

3. Which of the following Nvidia technologies is primarily used for performing benchmarking and optimizing GPU-accelerated deep learning workflows, especially focusing on model training performance?

A) Nvidia Nsight Systems
B) Nvidia Triton Inference Server
C) Nvidia DeepStream SDK
D) Nvidia CUDA

4. A data scientist is preprocessing a dataset containing multiple categorical features using NVIDIA RAPIDS to accelerate feature engineering.
The dataset contains:
A low-cardinality categorical feature (Product Type) with 10 unique values.
A high-cardinality categorical feature (User ID) with 100,000 unique values.
A numerical feature (Price) that requires transformation.
Which of the following feature engineering approaches will be the most efficient for GPU acceleration?

A) Frequency encoding for User ID is an efficient alternative to one-hot encoding, as it replaces each category with its frequency in the dataset, reducing dimensionality while preserving useful information.
B) Convert both Product Type and User ID to int64 and use standardization (mean normalization) on Price.
C) Store both Product Type and User ID as string data types in cuDF to maintain raw categorical information.
D) Apply one-hot encoding to both Product Type and User ID, and scale Price using float64 precision.
E) Using float32 for Price is optimal for GPU-based ML models, balancing precision and computational efficiency.
F) Convert Product Type to integers using label encoding, use frequency encoding for User ID, and normalize Price using float32.

5. A machine learning engineer wants to deploy a GPU-accelerated inference model in a containerized environment while ensuring compatibility with NVIDIA libraries.
Which of the following is the best approach for managing dependencies?

A) Use the official NVIDIA Docker base images (nvidia/cuda) and install RAPIDS AI libraries within the container to ensure GPU compatibility.
B) Run Docker containers without any special configurations, as Docker automatically detects and utilizes GPUs.
C) Install GPU drivers directly inside the container instead of on the host system to avoid dependency conflicts.
D) Disable the --gpus flag when running Docker containers, as RAPIDS AI libraries do not require explicit GPU selection.

Solutions: