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NVIDIA NCP-ADS Exam Syllabus Topics:

Section	Weight	Objectives
GPU and Cloud Computing	16%	- Resource management and scaling strategies - CRISP-DM and data science methodology - Cloud GPU environments and deployment - GPU architecture and acceleration principles
Machine Learning	15%	- Model evaluation and validation - GPU-accelerated ML frameworks and algorithms - Distributed training strategies - Model training and hyperparameter tuning
Data Manipulation and Software Literacy	19%	- Performance profiling and optimization tools - Dependency management and containerization - Data processing libraries selection and usage - GPU-accelerated ETL workflows
MLOps	19%	- Pipeline automation and orchestration - End-to-end workflow management - Model deployment and serving - Monitoring, logging and maintenance
Data Preparation	17%	- Data validation and quality assurance - Data cleaning, preprocessing and transformation - Workflow monitoring and bottleneck identification - Feature engineering and data type optimization
Data Analysis	14%	- Time-series analysis and anomaly detection - Exploratory Data Analysis (EDA) - Distributed and parallel data processing - Data visualization and graph analytics

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q142-Q147):

NEW QUESTION # 142
A data scientist is working on a machine learning model for fraud detection. Due to the limited size of the dataset, they decide to generate synthetic data using NVIDIA RAPIDS AI and cuDF.
Which of the following approaches is the most efficient and effective for generating synthetic data while ensuring compatibility with RAPIDS AI workflows?

A. Use cuDF DataFrame operations to create new synthetic samples by applying random transformations (e.g., noise injection, permutation) to the existing dataset.
B. Train a generative adversarial network (GAN) using PyTorch and then use the generated samples in RAPIDS AI without any additional processing.
C. Use numpy and pandas to generate synthetic data, then convert the DataFrame to cuDF before using it in RAPIDS AI workflows.
D. Use the RAPIDS cuML library to directly generate synthetic tabular data with controlled statistical properties.

Answer: A

NEW QUESTION # 143
You are working on a data science project using NVIDIA RAPIDS on a multi-GPU system.
To ensure reproducibility and avoid software versioning conflicts, which of the following is the best approach for managing dependencies?

A. Install all required packages globally on the system using pip install without a virtual environment.
B. Use a manually compiled CUDA installation alongside system-installed Python libraries to manage GPU dependencies.
C. Use a Conda environment with RAPIDS-compatible versions of libraries installed using conda install
-c rapidsai -c nvidia.
D. Avoid dependency management frameworks and rely on manual tracking of package versions using a text file.

Answer: C

NEW QUESTION # 144
You are working on an accelerated data science project and need to acquire a large dataset stored in a Parquet file format and load it efficiently for GPU processing using NVIDIA RAPIDS.
Which of the following approaches is the most efficient way to load the dataset into a GPU-accelerated DataFrame?

A. df = cudf.read_parquet("data.parquet")
B. df = cudf.read_csv("data.parquet")
C. df = pd.read_parquet("data.parquet")
D. df = cudf.to_gpu(pd.read_parquet("data.parquet"))

Answer: A

NEW QUESTION # 145
A data science team is using NVIDIA GPUs to accelerate their AI workflow for a fraud detection system. They follow the CRISP-DM methodology to ensure a structured approach to the project.
During the Data Preparation phase, what is the most effective way to leverage NVIDIA technologies?

A. Use NVIDIA RAPIDS to preprocess large-scale transaction datasets efficiently on GPUs before training the model.
B. Ignore feature engineering and feed raw data directly into the model, relying on deep learning to extract relevant features.
C. Perform all data transformation on a single CPU core to ensure stability before deploying to GPU- accelerated training.
D. Manually clean and transform data using traditional CPU-based processing tools like pandas, then transfer the data to the GPU only for training.

Answer: A

NEW QUESTION # 146
You are building a real-time recommendation system that processes high-frequency transactional data from millions of users.
The system must:
- Ingest and preprocess data efficiently
- Perform similarity computations for user-item recommendations
- Scale to handle rapid incoming transactions
Which of the following NVIDIA technologies is the best choice for this use case?

A. CUDA Kernels with Custom C++ Code
B. RAPIDS cuGraph
C. NVIDIA Triton Inference Server
D. NVIDIA NVTabular

Answer: D

NEW QUESTION # 147
......

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