NVIDIA-Certified-Professional Accelerated Data Science : NCP-ADS exam

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NVIDIA NCP-ADS Exam Syllabus Topics:

NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. You are processing a multi-terabyte dataset in CuDF and want to optimize query performance and storage efficiency.
Which approach should you follow to ensure that the dataset remains efficiently partitioned and easily accessible?

A) Convert all numeric columns to float64 for higher precision, even if float32 is sufficient.
B) Store the entire dataset in a single large file to minimize the number of files in the directory.
C) Split the dataset into multiple files based on a logical partitioning key, such as a date column.
D) Use pandas for large-scale processing instead of CuDF since pandas is more stable for big data processing.

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

3. You have a multi-GPU cluster running RAPIDS with Dask to process a large dataset stored in Apache Parquet format. During execution, you notice some GPUs are underutilized, while others are overloaded, leading to uneven processing times.
What is the most effective way to balance the workload across GPUs?

A) Use Dask's adaptive scaling to dynamically adjust the number of GPU workers
B) Switch to a CPU-based framework like Spark to distribute the load evenly
C) Increase the number of worker threads per GPU manually
D) Split the dataset into smaller chunks manually and assign them to GPUs

4. A machine learning engineer is tasked with optimizing an image classification model on a cloud platform. The engineer must select a GPU-accelerated instance that balances cost and performance while ensuring compatibility with frameworks like TensorFlow and PyTorch.
Which instance configuration is the most appropriate choice?

A) A CPU-only instance with 128 GB of RAM for increased data processing speed.
B) A cloud instance with NVIDIA A100 GPUs and NVLink support.
C) A single-core CPU instance with high disk I/O throughput for faster data loading.
D) A cloud instance with integrated graphics rather than dedicated NVIDIA GPUs.

5. You are working with a large dataset in a cloud environment for a deep learning model. The dataset consists of several features including numerical values, categorical data, and timestamps.
Which of the following choices would result in the most efficient use of GPU and cloud resources when determining the optimal data type for each feature? (Select three)

A) Use datetime64[ns] for timestamp features to ensure high precision.
B) Use float64 for all numerical features to ensure maximum precision.
C) Use int8 for categorical features where there are fewer than 256 categories.
D) Use object data types for categorical features to avoid type conversion overhead.
E) Use int32 for all numerical features to save memory.

Solutions: