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NVIDIA NCP-ADS Exam Syllabus Topics:

NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. You are working with a large dataset on an NVIDIA GPU, where optimizing memory usage is a priority. Your dataset contains a column, transaction_id, which stores unique integer values ranging between 0 and 100,000.
Which of the following data types is the most memory-efficient choice for this column in cuDF?

A) df['transaction_id'] = df['transaction_id'].astype('int32')
B) df['transaction_id'] = df['transaction_id'].astype('float32')
C) df['transaction_id'] = df['transaction_id'].astype('int64')
D) df['transaction_id'] = df['transaction_id'].astype('int8')

2. You are working with a dataset containing hundreds of millions of records, and you need to perform ETL operations such as filtering, joins, and aggregations. Given the dataset size, which NVIDIA- accelerated library should you use to achieve optimal performance?

A) NumPy, because it is optimized for numerical computing and offers better performance for handling tabular data.
B) Pandas, as it is widely used and supports all common DataFrame operations, even for very large datasets.
C) cuPy, because it provides GPU-accelerated array operations, making it the best option for processing tabular data.
D) cuDF, as it provides GPU-accelerated DataFrame operations similar to Pandas, allowing for efficient processing of large datasets.

3. Which of the following best describes a key advantage of using cloud-based GPU instances for machine learning model training?

A) Cloud GPU instances cannot support containerized workloads, limiting their applicability for MLOps and CI/CD pipelines.
B) Cloud GPUs are always more cost-effective than on-premise GPUs, as they do not incur long-term usage costs.
C) Cloud-based GPU instances offer lower latency and better network performance compared to on- premise deployments, regardless of geographical location.
D) Cloud GPUs provide dynamically scalable resources, allowing users to increase or decrease compute power based on demand without upfront hardware investment.

4. You are processing a dataset with billions of records and want to encode a categorical column efficiently using NVIDIA RAPIDS.
Which of the following methods correctly encodes categorical data using cuDF?

A) df['category_column'] = df['category_column'].one_hot_encode()
B) df['category_column'] = df['category_column'].apply(lambda x: hash(x) % 1000)
C) df['category_column'] = LabelEncoder().fit_transform(df['category_column'])
D) df['category_column'] = df['category_column'].astype('category')

5. You are working with a large-scale social network dataset and need to analyze relationships between users to detect communities using the Louvain algorithm. Given the benefits of GPU acceleration, you decide to use cuGraph for this task.
Which of the following statements best describes why cuGraph is beneficial for this workload?

A) cuGraph primarily accelerates deep learning tasks and is not optimized for graph-based algorithms.
B) Running graph algorithms on a GPU leads to higher latency due to the overhead of data movement between CPU and GPU.
C) cuGraph does not support weighted graphs, making it less useful for real-world social network analysis.
D) cuGraph enables parallel computation on the GPU, significantly speeding up community detection tasks compared to traditional CPU-based implementations.

Solutions: