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NVIDIA NCP-ADS Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Data Preparation	17%	- Data Cleaning and Transformation 1. Synthetic data generation with RAPIDS 2. Data normalization and standardization 3. cuDF and pandas data preprocessing
Topic 2: 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 3: MLOps	19%	- Deployment and Monitoring 1. Performance benchmarking and optimization 2. Memory and capacity evaluation 3. Model deployment in production environments
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: Data Analysis	14%	- Exploratory Data Analysis (EDA) 1. Detect anomalies in time series datasets 2. Use cuGraph for graph analytics 3. Perform time series analysis and visualization
Topic 6: Machine Learning	15%	- Model Development and Optimization 1. Memory optimization techniques (mixed precision, batching) 2. Multi-GPU training comparison 3. Hyperparameter tuning 4. Feature engineering

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q74-Q79):

NEW QUESTION # 74
Which of the following hardware components is most critical for accelerating the training of deep learning models?

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

Answer: B

NEW QUESTION # 75
A machine learning team needs to process terabytes of image metadata stored in a distributed storage system. They want to leverage GPU acceleration to speed up preprocessing and transformation while ensuring efficient parallel access.
Which of the following approaches best aligns with NVIDIA's accelerated data science ecosystem?

A. Use SQLite for metadata storage and process it with RAPIDS cuDF.
B. Use Dask-cuDF with Parquet files stored in an object storage system like S3.
C. Process metadata using PySpark on a CPU cluster before sending it to a GPU.
D. Store metadata in a traditional SQL database and query it using Pandas.

Answer: B

NEW QUESTION # 76
You are working with a large dataset containing 500 million records stored as a parquet file. Your task is to perform data filtering, aggregation, and transformation as efficiently as possible.
Which of the following approaches would be the best choice for accelerated data manipulation using NVIDIA technologies?

A. Use multiprocessing in Python to parallelize pandas operations across CPU cores
B. Convert the dataset to a NumPy array and use standard Python loops
C. Use RAPIDS cuDF to load, filter, and transform the dataset on the GPU
D. Load the dataset into pandas and use apply() for transformations

Answer: C

NEW QUESTION # 77
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. NVIDIA NVTabular
B. RAPIDS cuGraph
C. CUDA Kernels with Custom C++ Code
D. NVIDIA Triton Inference Server

Answer: A

NEW QUESTION # 78
A data scientist is using NVIDIA RAPIDS to perform statistical analysis as part of exploratory data analysis (EDA) on a dataset containing millions of product reviews. They need to compute basic descriptive statistics such as mean, median, and variance efficiently.
Which of the following methods is the most appropriate for performing these calculations on GPUs?

A. Convert the dataset into a PyTorch tensor and use PyTorch's statistical methods
B. Use NumPy's statistical functions, such as numpy.mean() and numpy.var()
C. Use a traditional SQL database to compute statistics and then transfer results to the GPU
D. Use cuDF's built-in statistical functions like .mean(), .median(), and .var()

Answer: D

NEW QUESTION # 79
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