NCP-ADS Latest Test Preparation, New NCP-ADS Practice Questions

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NVIDIA NCP-ADS Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Data Analysis	14%	- Graph analytics 1. Creating and analyzing graph data using cuGraph 2. Node importance evaluation and network relationship visualization - Exploratory data analysis 1. Descriptive statistics and summary analysis 2. Performing EDA on GPU-accelerated datasets - Visualization 1. Visualizing data using Plotly and Matplotlib 2. Selecting appropriate plots for different analysis goals - Time-series analysis 1. Anomaly detection in time-series datasets 2. Time-series data handling and forecasting
Topic 2: Machine Learning	15%	- Feature engineering and hyperparameter tuning 1. Hyperparameter tuning techniques 2. Feature engineering for ML models 3. Batching and memory-efficient training methods - Model training with GPU acceleration 1. Selection of appropriate algorithms for GPU execution 2. Training models using cuML and GPU-accelerated XGBoost 3. Multi-GPU training strategies - Deep learning frameworks integration 1. Using RAPIDS with TensorFlow and PyTorch 2. Overfitting vs underfitting concepts
Topic 3: Data Preparation	17%	- Feature engineering 1. Feature engineering for numerical and categorical variables 2. Dimensionality reduction and data sampling - GPU-accelerated ETL workflows 1. RAPIDS-based ETL pipelines 2. Efficient processing and storage with Parquet - Data cleaning and quality handling 1. Handling missing values and data quality issues 2. Data governance and compliance - Data loading and preprocessing 1. Handling class imbalance and generating synthetic data 2. NVIDIA DALI for high-performance data loading
Topic 4: GPU and Cloud Computing	16%	- Performance optimization 1. Single and multi-GPU performance optimization 2. Mixed precision and bottleneck analysis 3. Memory profiling with DLProf - GPU architecture and fundamentals 1. GPU architecture fundamentals for data science 2. CPU vs GPU workloads and memory transfer optimization - GPU resource management 1. Efficient GPU resource allocation and scheduling - Cloud GPU environments 1. Cloud-based GPU instance configuration 2. Containerized workflow deployment on cloud
Topic 5: Data Manipulation and Software Literacy	19%	- GPU-accelerated data manipulation using cuDF 1. cuDF vs pandas API mapping and usage 2. Data integration, joining, merging, and filtering 3. Groupby, apply, and aggregation operations - Software literacy and development tools 1. Python, NumPy, pandas, Jupyter proficiency 2. RAPIDS ecosystem (cuDF, cuML, cuGraph, cuPy) - Distributed computing with Dask 1. Scaling data operations across multiple GPUs 2. Dask-cuDF for parallel data processing
Topic 6: MLOps	19%	- Containerization and environment management 1. Conda environment management 2. Docker for reproducible GPU-accelerated workflows - Model deployment and serving 1. Production deployment strategies 2. Model saving, loading, and prediction generation - Model monitoring and management 1. Managing model artifacts and configurations for reproducibility 2. Monitoring production models for drift and performance degradation - Experiment tracking 1. Benchmarking workflows and selecting optimal hardware 2. MLflow, Weights & Biases, and custom tracking tools

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q215-Q220):

NEW QUESTION # 215
You are working on a data processing pipeline using NVIDIA GPUs for accelerating computations.
You need to monitor the pipeline's performance to identify bottlenecks.
Which of the following tools or techniques can be used to efficiently recognize bottlenecks in such a GPU-accelerated pipeline? (Select two)

A. NVIDIA DLA (Deep Learning Accelerator)
B. NVIDIA Nsight Systems
C. NVIDIA TensorRT Profiling
D. NVIDIA CUDA Profiler (nvprof)
E. NVIDIA nvidia-smi

Answer: B,D

NEW QUESTION # 216
A data scientist is preprocessing a dataset containing multiple categorical features using NVIDIA RAPIDS to accelerate feature engineering.
The dataset contains:
A low-cardinality categorical feature (Product Type) with 10 unique values.
A high-cardinality categorical feature (User ID) with 100,000 unique values.
A numerical feature (Price) that requires transformation.
Which of the following feature engineering approaches will be the most efficient for GPU acceleration?

A. Convert both Product Type and User ID to int64 and use standardization (mean normalization) on Price.
B. Using float32 for Price is optimal for GPU-based ML models, balancing precision and computational efficiency.
C. Apply one-hot encoding to both Product Type and User ID, and scale Price using float64 precision.
D. Store both Product Type and User ID as string data types in cuDF to maintain raw categorical information.
E. Convert Product Type to integers using label encoding, use frequency encoding for User ID, and normalize Price using float32.
F. Frequency encoding for User ID is an efficient alternative to one-hot encoding, as it replaces each category with its frequency in the dataset, reducing dimensionality while preserving useful information.

Answer: E

NEW QUESTION # 217
You are working with cuGraph to analyze a large social network dataset where users are represented as nodes, and their connections (friendships) are represented as edges. You need to determine the most efficient way to store and process the graph in cuGraph for high-performance analytics.
Which of the following graph representations is best suited for efficient processing in cuGraph?

A. Incidence Matrix representation
B. Edge List stored in a Pandas DataFrame
C. Adjacency List representation using Python dictionaries
D. Compressed Sparse Row (CSR) format

Answer: D

NEW QUESTION # 218
You are tasked with cleansing a dataset containing numerical data that has significant outliers.
You're using pandas to identify and appropriately handle these outliers before applying CuDF for accelerated downstream analysis.
Which method effectively manages the numerical outliers while preserving the dataset's integrity for subsequent accelerated analytics?

A. Clip outliers using pandas.Series.clip() based on percentile thresholds (e.g., 5th and 95th percentile).
B. Remove outliers completely using pandas.DataFrame.drop().
C. Fill outlier values with mean using pandas.Series.fillna().
D. Replace outliers with zero using pandas.Series.replace().

Answer: A

NEW QUESTION # 219
A data scientist is training a deep learning model on an NVIDIA GPU-accelerated platform. The model is suffering from overfitting, leading to poor generalization on unseen data.
Which of the following techniques is the most effective for reducing overfitting in this scenario?

A. Reducing the learning rate
B. Applying dropout regularization
C. Removing data augmentation techniques
D. Increasing the number of layers in the model

Answer: B

NEW QUESTION # 220
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