Try and practice the latest NVIDIA : NCP-ADS real questions & answers

Failing the NCP-ADS exam means paying the registration fee all over again. A set of 303 practice questions from Actual4Cert costs far less than a retake of NVIDIA-Certified-Professional Accelerated Data Science.

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

Question 1

You are a data scientist analyzing a social media network with NVIDIA cuGraph to identify the most influential users using the PageRank algorithm.
Which option best describes how cuGraph PageRank operates on a directed graph?

A. PageRank in cuGraph is a label propagation algorithm that clusters nodes into communities rather than ranking their importance.
B. PageRank in cuGraph operates only on undirected graphs and cannot be applied to networks where edges have a direction.
C. PageRank assigns equal importance to all nodes in the graph initially and updates values only based on outgoing edges, ignoring incoming edges.
D. PageRank in cuGraph uses an iterative power method to update node importance values based on incoming edges, incorporating a damping factor to handle random jumps.

Question 2

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

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

Question 4

A data scientist is working with an imbalanced dataset in a fraud detection project. The dataset contains 1 million transactions, but only 2% of them are labeled as fraudulent. To improve the performance of the model, the scientist decides to generate synthetic data using NVIDIA RAPIDS cuDF.
Which of the following approaches is the best way to generate synthetic samples while preserving data characteristics?

A. Use cudf.DataFrame.interpolate(method='linear') to create new fraudulent samples by interpolating between existing ones.
B. Use cudf.DataFrame.append(cudf.DataFrame.random()) to create new fraudulent transactions.
C. Use cudf.DataFrame.sample(frac=0.5, replace=True) to oversample the minority class.
D. Apply cuML.SMOTE() to generate synthetic samples based on the minority class distribution.

Question 5

You are working on an MLOps pipeline that involves loading a large dataset for training a deep learning model on an NVIDIA GPU. Before training, you need to ensure that the dataset fits within the available GPU memory.
Which of the following commands in Python using the pandas and numpy libraries can correctly determine the memory size of a dataset?

A. sys.getsizeof(df)
B. df.memory_usage(deep=True).sum()
C. df.info(memory_usage='deep')
D. np.array(df).nbytes

Solutions: