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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q211-Q216):

NEW QUESTION # 211
A data engineering team is tasked with processing terabytes of log data every hour using an ETL pipeline. Due to the large data volume, they need a scalable GPU-accelerated solution that can distribute data processing across multiple GPUs.
Which approach best meets their needs?

A. Use Dask-cuDF to distribute cuDF DataFrame operations across multiple GPUs, enabling parallel ETL processing.
B. Process data using Pandas, then export the results to a CSV file for GPU-accelerated analytics.
C. Use NumPy for data transformations before converting the dataset into cuDF for final storage.
D. Use cuDF alone for processing log data, as it provides optimal performance on a single GPU.

Answer: A

NEW QUESTION # 212
You need to train a deep learning model using PyTorch on a dataset too large for a single GPU. You decide to use Dask with NVIDIA GPUs for multi-GPU scaling.
Which approach is the most effective for distributing the workload?

A. Use Dask-CUDA workers with PyTorch's DistributedDataParallel (DDP) for training across multiple GPUs
B. Use Dask.delayed to wrap PyTorch training functions and schedule them across multiple GPUs
C. Use Dask's built-in deep learning API to automatically distribute PyTorch models across GPUs
D. Use Dask Bag to shard the dataset and train separate PyTorch models on each shard

Answer: A

NEW QUESTION # 213
A research team is analyzing large-scale social interactions and wants to identify strongly connected communities within a massive graph dataset using NVIDIA's cuGraph library.
Which method would be the most efficient for this task?

A. Apply cuGraph's Dijkstra's algorithm to find the shortest paths between all nodes and group them into communities.
B. Apply cuGraph's Label Propagation Algorithm (LPA) to divide the graph into communities without predefining the number of clusters.
C. Use cuGraph's Louvain method to detect hierarchical communities based on modularity optimization.
D. Run the cuGraph PageRank algorithm and classify nodes with high scores as community leaders.

Answer: C

NEW QUESTION # 214
You are training a machine learning model using RAPIDS cuML and need to ensure that all numeric features are standardized for better model performance.
Which of the following is the best approach for scaling data using RAPIDS?

A. df_scaled = scaler.fit_transform(df)
B. df_scaled = df / df.max()
C. df_scaled = df.apply(lambda x: x / np.linalg.norm(x))
D. scaler = cuml.preprocessing.StandardScaler()
E. df_scaled = (df - df.min()) / (df.max() - df.min())

Answer: D

NEW QUESTION # 215
You are working with structured tabular data in a cloud-based GPU environment.
Your dataset contains the following columns:
Column Name Example Values Data Type Needed
user_id 15432, 98765, 43210 Integer
purchase_amt 12.99, 35.50, 100.75 Floating Point
category 'Books', 'Electronics' Categorical
Which of the following is the most optimal approach to assign data types to these columns to ensure efficient memory usage and computational performance?

A. 1. df['user_id'] = df['user_id'].astype('float32')
2. df['purchase_amt'] = df['purchase_amt'].astype('float64')
3. df['category'] = df['category'].astype('string')
B. 1. df['user_id'] = df['user_id'].astype('int32')
2. df['purchase_amt'] = df['purchase_amt'].astype('float32')
3. df['category'] = df['category'].astype('category')
C. 1. df['user_id'] = df['user_id'].astype('int64')
2. df['purchase_amt'] = df['purchase_amt'].astype('float64')
3. df['category'] = df['category'].astype('string')
D. 1. df['user_id'] = df['user_id'].astype('int16')
2. df['purchase_amt'] = df['purchase_amt'].astype('float16')
3. df['category'] = df['category'].astype('string')

Answer: B

NEW QUESTION # 216
......

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Latest NCP-ADS Braindumps Sheet & Valid NCP-ADS Test Duration

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Updated Latest NCP-ADS Braindumps Sheet offer you accurate Valid Test Duration | NVIDIA-Certified-Professional Accelerated Data Science

NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q211-Q216):

100% Pass Quiz NVIDIA Marvelous NCP-ADS - Latest NVIDIA-Certified-Professional Accelerated Data Science Braindumps Sheet

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