Practice NCP-ADS Exam Pdf - NCP-ADS Official Cert Guide

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q91-Q96):

NEW QUESTION # 91
You are selecting a dataset for GPU-accelerated data science using NVIDIA RAPIDS.
Which dataset characteristic is most suitable for taking advantage of GPU acceleration?

A. A large, structured dataset stored in a Parquet format
B. A dataset consisting of unstructured text stored as plain .txt files
C. A dataset stored as an SQLite database file
D. A small dataset with fewer than 1,000 rows stored in an Excel file

Answer: A

NEW QUESTION # 92
You are tasked with processing a large dataset using multiple GPUs to accelerate computation. You decide to use Dask to implement data parallelism with NVIDIA's RAPIDS framework to maximize GPU utilization.
Which of the following steps is essential for efficiently distributing the workload across multiple GPUs in Dask?

A. Manually allocate GPU memory using cupy for each worker instead of using Dask's scheduler.
B. Set up a single Dask dataframe without partitioning and rely on automatic workload balancing.
C. Use dask_cuda.LocalCUDACluster() to create a multi-GPU cluster and dask.distributed.Client() to manage execution.
D. Use dask.dataframe.repartition() to distribute data evenly across multiple GPUs.

Answer: C

NEW QUESTION # 93
Which of the following statements best describes the role of GPUs in accelerating data science workloads?

A. GPUs are designed primarily for rendering graphics and have limited utility in machine learning and deep learning applications.
B. GPUs are only effective for acceleration when used in conjunction with Tensor Processing Units (TPUs), as they cannot train deep learning models independently.
C. GPUs use thousands of smaller cores that can execute many parallel computations simultaneously, making them ideal for large-scale matrix operations.
D. GPUs are optimized for sequential data processing tasks, making them more efficient than CPUs for database operations.

Answer: C

NEW QUESTION # 94
You are processing a large-scale transportation network graph using NVIDIA cuGraph. The graph is extremely large, consuming almost all available GPU memory. Performance is deteriorating, and some computations fail due to memory exhaustion.
What is the best approach to efficiently handle this large graph while keeping computations on the GPU?

A. Manually split the graph into chunks and process each chunk separately without any coordination.
B. Store the graph as a large Python dictionary and use cuGraph only for specific queries.
C. Use cuGraph's multi-GPU support via Dask-cuGraph to distribute the graph across multiple GPUs.
D. Convert the graph into a NetworkX graph and process it on the CPU to reduce GPU memory usage.

Answer: C

NEW QUESTION # 95
A data scientist is training a deep learning model and wants to find the best learning rate to optimize convergence speed and generalization. The scientist tests different values: A very small learning rate (0.00001) results in slow convergence.
A very large learning rate (10) causes the model loss to fluctuate wildly and not converge.
Which of the following strategies is the most effective way to optimize the learning rate dynamically during training?

A. Use learning rate warm-up followed by decay
B. Decrease the learning rate to zero at the end of training (learning rate scheduling)
C. Use a fixed learning rate chosen through trial and error
D. Use the same learning rate for all layers in a deep neural network

Answer: A

NEW QUESTION # 96
......

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Practice NCP-ADS Exam Pdf - NCP-ADS Official Cert Guide

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NCP-ADS Official Cert Guide | Study NCP-ADS Material

NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q91-Q96):

Updated Practice NCP-ADS Exam Pdf – Pass NCP-ADS First Attempt

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