NVIDIA NCP-ADS Practice Test For Supreme Achievement 2026

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NVIDIA NCP-ADS Exam Syllabus Topics:

Section	Weight	Objectives
GPU and Cloud Computing	16%	- GPU architecture and fundamentals 1. CPU vs GPU workloads and memory transfer optimization 2. GPU architecture fundamentals for data science - Cloud GPU environments 1. Cloud-based GPU instance configuration 2. Containerized workflow deployment on cloud - GPU resource management 1. Efficient GPU resource allocation and scheduling - Performance optimization 1. Single and multi-GPU performance optimization 2. Memory profiling with DLProf 3. Mixed precision and bottleneck analysis
Data Analysis	14%	- Time-series analysis 1. Anomaly detection in time-series datasets 2. Time-series data handling and forecasting - Graph analytics 1. Creating and analyzing graph data using cuGraph 2. Node importance evaluation and network relationship visualization - Exploratory data analysis 1. Performing EDA on GPU-accelerated datasets 2. Descriptive statistics and summary analysis - Visualization 1. Selecting appropriate plots for different analysis goals 2. Visualizing data using Plotly and Matplotlib
Data Manipulation and Software Literacy	19%	- Distributed computing with Dask 1. Scaling data operations across multiple GPUs 2. Dask-cuDF for parallel data processing - GPU-accelerated data manipulation using cuDF 1. Data integration, joining, merging, and filtering 2. Groupby, apply, and aggregation operations 3. cuDF vs pandas API mapping and usage - Software literacy and development tools 1. RAPIDS ecosystem (cuDF, cuML, cuGraph, cuPy) 2. Python, NumPy, pandas, Jupyter proficiency
Machine Learning	15%	- Deep learning frameworks integration 1. Overfitting vs underfitting concepts 2. Using RAPIDS with TensorFlow and PyTorch - Model training with GPU acceleration 1. Training models using cuML and GPU-accelerated XGBoost 2. Multi-GPU training strategies 3. Selection of appropriate algorithms for GPU execution - Feature engineering and hyperparameter tuning 1. Feature engineering for ML models 2. Hyperparameter tuning techniques 3. Batching and memory-efficient training methods
MLOps	19%	- Experiment tracking 1. MLflow, Weights & Biases, and custom tracking tools 2. Benchmarking workflows and selecting optimal hardware - Containerization and environment management 1. Docker for reproducible GPU-accelerated workflows 2. Conda environment management - Model deployment and serving 1. Model saving, loading, and prediction generation 2. Production deployment strategies - Model monitoring and management 1. Managing model artifacts and configurations for reproducibility 2. Monitoring production models for drift and performance degradation
Data Preparation	17%	- Data loading and preprocessing 1. Handling class imbalance and generating synthetic data 2. NVIDIA DALI for high-performance data loading - Data cleaning and quality handling 1. Data governance and compliance 2. Handling missing values and data quality issues - GPU-accelerated ETL workflows 1. Efficient processing and storage with Parquet 2. RAPIDS-based ETL pipelines - Feature engineering 1. Feature engineering for numerical and categorical variables 2. Dimensionality reduction and data sampling

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q120-Q125):

NEW QUESTION # 120
In the context of cloud computing, what are the key benefits of using GPUs for data science tasks?
(Select two)

A. Efficient handling of matrix operations in machine learning models
B. Lower cost of cloud infrastructure
C. Faster parallel processing for large datasets
D. Lower energy consumption compared to CPUs
E. Better for memory-intensive workloads

Answer: A,C

NEW QUESTION # 121
You are processing a large dataset using RAPIDS cuDF and Dask-cuDF on an NVIDIA GPU. Your profiling indicates that data transfer times between CPU and GPU are significantly slowing down your pipeline.
What is the most effective way to reduce this bottleneck?

A. Transfer data in multiple smaller chunks to the GPU instead of larger batches
B. Use cudf.read_parquet() instead of Pandas to load data directly into GPU memory
C. Convert the dataset into a CSV format before transferring it to the GPU
D. Increase the CPU RAM allocation to store more data before transferring to the GPU

Answer: B

NEW QUESTION # 122
You are tasked with acquiring a dataset for training a machine learning model in healthcare, predicting patient readmission rates. Before using the dataset, you must assess its quality.
Which of the following is the most important factor to evaluate before acquisition?

A. Whether the dataset is stored on a cloud server or a local machine
B. The programming language used to preprocess the dataset
C. The number of missing values and inconsistencies in key columns
D. The file format (CSV, JSON, or XML) of the dataset

Answer: C

NEW QUESTION # 123
You are working on an MLOps workflow that loads a dataset into GPU memory for model training using RAPIDS cuDF. Before performing transformations, you want to verify that the dataset will fit into available GPU memory.
Which of the following methods provides the most accurate estimate of dataset memory consumption in a RAPIDS cudf.DataFrame?

A. cudf_df.__sizeof__()
B. cudf_df.memory_usage(deep=True).sum()
C. cudf_df.to_pandas().memory_usage(deep=True).sum()
D. cudf_df.memory_usage().sum()

Answer: D

NEW QUESTION # 124
A machine learning engineer is working on a multi-GPU workload using Dask and RAPIDS to process a massive dataset efficiently. However, they notice that GPU utilization is not optimal, and data transfer between GPUs is slowing down computation.
What is the best approach to minimize data transfer overhead and maximize parallel efficiency?

A. Use NVLink to enable high-bandwidth inter-GPU communication and dask_cuda.LocalCUDACluster
B. Load the dataset into a Pandas DataFrame and distribute computations across GPUs manually
C. Transfer data between GPUs using NumPy's .copy() function
D. Use Dask's single-threaded scheduler to avoid multi-threading overhead

Answer: A

NEW QUESTION # 125
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