Online Access Free NCP-ADS Exam Questions

You are using cuGraph to run the PageRank algorithm on a directed web graph. The dataset is large, and you want to ensure an accurate and efficient computation while optimizing GPU performance.
Which of the following configurations is the best approach for running PageRank in cuGraph?

• A.Convert the graph into an adjacency matrix and perform matrix multiplication iteratively for convergence
• B.Run cugraph.pagerank() with a damping factor of 0.85 and set the max iterations to 100 with a convergence threshold
• C.Use the cugraph.pagerank() function with a damping factor of 0 and 10 iterations
• D.Load the graph into NetworkX first, compute PageRank, and then convert the results back into cuGraph format

A data scientist is preprocessing a dataset containing several types of features:
A timestamp column storing millisecond-resolution timestamps.
A column with binary categorical values (Yes/No).
A column containing large continuous numerical values.
A column containing product category codes ranging from 0 to 5000.
Which of the following data type choices is the most optimal for maximizing GPU processing efficiency using NVIDIA cuDF?

• A.Convert timestamps to datetime64[ms], encode binary values as bool, use float32 for continuous values, and int16 for product category codes.
• B.Store timestamps as int64, encode binary values as float16, use float64 for continuous numerical values, and use int8 for product category codes.
• C.Use float64 for timestamps, int8 for binary categorical values, float32 for continuous numerical values, and int32 for product category codes.
• D.Use string data type for timestamps, int32 for binary values, float16 for continuous numerical values, and int64 for product category codes.
• E.Product category codes (range: 0-5000) fit within int16 (which can hold values from -32,768 to
  32,767), making it more memory-efficient than int32.
• F.float32 is the best choice for large continuous numerical values, balancing precision and GPU efficiency.
• G.Binary categorical values (Yes/No) should be stored as bool, which takes up minimal space.

Which of the following is the most efficient way to implement data parallelism using Dask for multi- GPU scaling on an Nvidia platform?

• A.Use Dask with a single GPU, distributing data across multiple workers within the GPU without considering GPU memory limitations.
• B.Use Dask with the dask_gpu package to assign data chunks across GPUs manually, without utilizing the distributed.Client.
• C.Use Dask with the dask_cuda package to distribute computation across GPUs, ensuring that each GPU is responsible for a portion of the data, while using distributed.Client to connect the GPU workers.
• D.Use Dask on a single GPU machine with no GPU-specific optimization, treating the system as if it were CPU-only.

A data scientist is working with a large dataset for a machine learning model and wants to accelerate feature engineering using a GPU.
Which of the following approaches will provide the most significant performance boost when using GPU acceleration?

• A.Using a single-threaded feature extraction approach to avoid overhead from parallelization.
• B.Reducing dataset size by randomly removing data points without considering class balance.
• C.Using traditional pandas DataFrames and NumPy operations optimized for CPU processing.
• D.Using RAPIDS cuDF and cuML libraries to perform feature transformations on a GPU.

Which of the following methods are commonly used to handle missing data in data analysis? (Select two)

• A.Ignoring missing data entirely
• B.Removing rows with missing data
• C.Imputing with the mean or median of the feature
• D.Using a fixed constant value for imputation
• E.Using the mode to impute missing values