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NVIDIA NCP-ADS Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: MLOps	19%	- Deployment and Monitoring 1. Performance benchmarking and optimization 2. Memory and capacity evaluation 3. Model deployment in production environments
Topic 2: Data Preparation	17%	- Data Cleaning and Transformation 1. Data normalization and standardization 2. cuDF and pandas data preprocessing 3. Synthetic data generation with RAPIDS
Topic 3: 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
Topic 4: Data Manipulation and Software Literacy	19%	- ETL and Data Processing Workflows 1. GPU-accelerated ETL design and implementation 2. Data caching and performance optimization 3. Distributed data processing frameworks (Dask)
Topic 5: GPU and Cloud Computing	16%	- GPU Optimization and Infrastructure 1. Docker and Conda environment management 2. Benchmarking GPU workflows 3. CRISP-DM workflow execution
Topic 6: 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

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q204-Q209):

NEW QUESTION # 204
You are selecting a dataset for GPU-accelerated data science using NVIDIA RAPIDS.
Which dataset characteristic is most suitable for taking advantage of GPU acceleration?

A. A dataset consisting of unstructured text stored as plain .txt files
B. A small dataset with fewer than 1,000 rows stored in an Excel file
C. A dataset stored as an SQLite database file
D. A large, structured dataset stored in a Parquet format

Answer: D

NEW QUESTION # 205
Which of the following hardware components is most critical for accelerating the training of deep learning models?

A. Random Access Memory (RAM)
B. Central Processing Unit (CPU)
C. Graphics Processing Unit (GPU)
D. Solid State Drive (SSD)

Answer: C

NEW QUESTION # 206
You are working with a large-scale financial dataset containing stock prices over the past 10 years.
Your goal is to forecast future prices using deep learning techniques optimized for GPU acceleration.
Which of the following approaches would be the most suitable for achieving accurate and efficient forecasting?

A. Use a k-Nearest Neighbors (k-NN) algorithm to identify similar historical price patterns and predict future values.
B. Use an LSTM (Long Short-Term Memory) network optimized with NVIDIA RAPIDS and CuDNN acceleration.
C. Apply Principal Component Analysis (PCA) to extract dominant trends and use them for forecasting.
D. Apply a simple moving average (SMA) over historical stock prices and extrapolate future values.

Answer: B

NEW QUESTION # 207
In the CRISP-DM process, which NVIDIA technology is commonly used during the "Data Preparation" phase to handle large datasets efficiently?

A. NVIDIA Jetson
B. NVIDIA Deep Learning AI
C. NVIDIA RAPIDS
D. NVIDIA CUDA Toolkit

Answer: C

NEW QUESTION # 208
A data scientist is using NVIDIA RAPIDS cuDF to process a large dataset of customer transactions.
The dataset contains numerical, categorical, and timestamp-based features.
To optimize memory usage and performance on NVIDIA GPUs, which approach should they take when selecting data types?

A. Avoid downcasting integer columns, as lower-bit integer types (e.g., int8) are not supported in GPU- accelerated computations.
B. Convert all timestamp features into object (string) format to maintain readability and ensure compatibility with GPU processing.
C. Convert categorical variables into cuDF categorical data types and downcast numerical columns to the smallest possible precision without losing information.
D. Store all numerical columns as float64 to preserve maximum precision, even if lower precision suffices.

Answer: C

NEW QUESTION # 209
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