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NVIDIA NCP-ADS Exam Syllabus Topics:

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

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q94-Q99):

NEW QUESTION # 94
You are working with a large dataset in RAPIDS cuDF and plan to standardize the numerical features using cuml.preprocessing.StandardScaler(). However, some columns contain missing values.
What is the best approach to handle the missing values before applying standardization?

A. Use cuml.impute.SimpleImputer(strategy='mean') to replace missing values with the column mean.
B. Use cudf.DataFrame.fillna(method='ffill') to forward-fill missing values.
C. Replace missing values with zero before standardization.
D. cuml.impute.KNNImputer() to replace missing values based on k-nearest neighbors.

Answer: A

NEW QUESTION # 95
A data engineer is using cuDF in NVIDIA RAPIDS to generate a large synthetic dataset for machine learning training. The dataset consists of numerical and categorical features. The engineer needs to generate millions of rows efficiently while preserving the relationships between features.
Which of the following approaches is the most optimal?

A. Use cudf.to_pandas(), generate synthetic data using pandas and Scikit-learn, and then convert it back to cuDF.
B. Use cudf.Series.random() to create independent random values for each column separately.
C. Train a cuML KMeans model on the original data and use the cluster centroids as new synthetic data points.
D. Leverage cuML's PCA.inverse_transform() after fitting PCA to the original dataset to generate new synthetic samples.

Answer: D

NEW QUESTION # 96
You are training a large-scale random forest model on a dataset with millions of rows and hundreds of features. The training time is significantly high when using traditional CPU-based machine learning frameworks.
Which NVIDIA technology should you use to accelerate training while maintaining compatibility with common ML frameworks like scikit-learn?

A. NVIDIA DeepStream to preprocess tabular data and optimize random forest model execution.
B. NVIDIA RAPIDS cuML to accelerate random forest training using GPU-optimized implementations.
C. NVIDIA Triton Inference Server to distribute random forest model training across multiple GPUs.
D. NVIDIA TensorRT to accelerate random forest model training by optimizing tree-based algorithms.

Answer: B

NEW QUESTION # 97
You are setting up a GPU-accelerated data science environment on a cloud-based instance that utilizes NVIDIA GPUs.
To ensure compatibility between CUDA, RAPIDS, and Python libraries, which of the following is the most effective approach for managing dependencies and avoiding version conflicts?

A. Use Conda environments with version-pinned dependencies to create an isolated environment for RAPIDS and CUDA
B. Manually install required libraries in a system-wide Python environment using pip install without version specifications
C. Use a virtual machine with all dependencies pre-installed to avoid any software conflicts
D. Install RAPIDS, PyTorch, TensorFlow, and other libraries in the same Conda base environment for simplified access

Answer: A

NEW QUESTION # 98
You are analyzing a large financial dataset containing stock market tick-by-tick data stored in a cuDF DataFrame. Since the dataset contains billions of data points, you need to aggregate it at the minute level before visualizing price trends efficiently.
Which of the following is the best approach for aggregating and visualizing this time-series data using NVIDIA technologies?

A. Use cuML's TSNE function to reduce dimensionality before visualizing with Bokeh
B. Use cuDF's .groupby() function to aggregate at the minute level, then visualize using hvPlot
C. Load the data into a relational database (e.g., PostgreSQL), run an SQL query for aggregation, and visualize using Seaborn
D. Convert cuDF to Pandas, aggregate using .resample() in Pandas, and visualize using Matplotlib

Answer: B

NEW QUESTION # 99
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