2026 NVIDIA Realistic Test NCP-ADS Registration

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NVIDIA NCP-ADS Exam Syllabus Topics:

Section	Weight	Objectives
GPU and Cloud Computing	16%	- GPU Optimization and Infrastructure 1. Docker and Conda environment management 2. CRISP-DM workflow execution 3. Benchmarking GPU workflows
Data Manipulation and Software Literacy	19%	- ETL and Data Processing Workflows 1. Data caching and performance optimization 2. GPU-accelerated ETL design and implementation 3. Distributed data processing frameworks (Dask)
Data Preparation	17%	- Data Cleaning and Transformation 1. Synthetic data generation with RAPIDS 2. cuDF and pandas data preprocessing 3. Data normalization and standardization
MLOps	19%	- Deployment and Monitoring 1. Performance benchmarking and optimization 2. Model deployment in production environments 3. Memory and capacity evaluation
Data Analysis	14%	- Exploratory Data Analysis (EDA) 1. Use cuGraph for graph analytics 2. Detect anomalies in time series datasets 3. Perform time series analysis and visualization
Machine Learning	15%	- Model Development and Optimization 1. Multi-GPU training comparison 2. Memory optimization techniques (mixed precision, batching) 3. Hyperparameter tuning 4. Feature engineering

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q93-Q98):

NEW QUESTION # 93
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'].astype('category')
B. df['category_column'] = df['category_column'].apply(lambda x: hash(x) % 1000)
C. df['category_column'] = df['category_column'].one_hot_encode()
D. df['category_column'] = LabelEncoder().fit_transform(df['category_column'])

Answer: A

NEW QUESTION # 94
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. Use the cugraph.pagerank() function with a damping factor of 0 and 10 iterations
B. Load the graph into NetworkX first, compute PageRank, and then convert the results back into cuGraph format
C. Convert the graph into an adjacency matrix and perform matrix multiplication iteratively for convergence
D. Run cugraph.pagerank() with a damping factor of 0.85 and set the max iterations to 100 with a convergence threshold

Answer: D

NEW QUESTION # 95
You are working with a GPU-based cloud environment and need to optimize the memory usage for a dataset that contains a column item_id representing unique product IDs. The item_id values are large integers, and there are over 10 million distinct product IDs.
Which of the following is the most memory-efficient data type choice for this column?

A. df['item_id'] = df['item_id'].astype('int32')
B. df['item_id'] = df['item_id'].astype('int64')
C. df['item_id'] = df['item_id'].astype('string')
D. df['item_id'] = df['item_id'].astype('float64')

Answer: B

NEW QUESTION # 96
After profiling a deep learning model using NVIDIA DLProf, you notice that a specific GEMM (General Matrix Multiplication) operation takes significantly longer than expected. The profiler output reveals that tensor cores are underutilized despite having an Ampere-based GPU with Tensor Cores enabled.
Which of the following actions is the MOST appropriate to improve performance?

A. Convert the model's data type to float16 or bfloat16 and re-run the training with automatic mixed precision (AMP).
B. Disable CUDA graphs and enforce PyTorch's eager execution mode to improve kernel execution order.
C. Switch from stochastic gradient descent (SGD) to Adam optimizer, as Adam improves convergence and computational efficiency.
D. Increase the batch size to maximize GPU memory usage and reduce kernel launch overhead.

Answer: A

NEW QUESTION # 97
You are working on a large-scale machine learning project that requires preprocessing terabytes of structured and semi-structured data. You need a distributed data processing framework that can leverage NVIDIA GPUs efficiently to accelerate computations.
Which of the following approaches would best achieve this goal?

A. Using plain NumPy with CUDA extensions to manually parallelize computations across multiple GPUs
B. Using Dask with RAPIDS cuDF and cuML for distributed GPU-accelerated processing
C. Using Apache Spark with PySpark for CPU-based distributed data processing
D. Using TensorFlow's Dataset API to load and preprocess the data on GPUs

Answer: B

NEW QUESTION # 98
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