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NVIDIA NCP-ADS Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Data Preparation	17%	- Data Cleaning and Transformation 1. cuDF and pandas data preprocessing 2. Data normalization and standardization 3. Synthetic data generation with RAPIDS
Topic 2: 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 3: Data Manipulation and Software Literacy	19%	- ETL and Data Processing Workflows 1. GPU-accelerated ETL design and implementation 2. Distributed data processing frameworks (Dask) 3. Data caching and performance optimization
Topic 4: Data Analysis	14%	- Exploratory Data Analysis (EDA) 1. Detect anomalies in time series datasets 2. Perform time series analysis and visualization 3. Use cuGraph for graph analytics
Topic 5: GPU and Cloud Computing	16%	- GPU Optimization and Infrastructure 1. Benchmarking GPU workflows 2. Docker and Conda environment management 3. CRISP-DM workflow execution
Topic 6: MLOps	19%	- Deployment and Monitoring 1. Model deployment in production environments 2. Memory and capacity evaluation 3. Performance benchmarking and optimization

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q214-Q219):

NEW QUESTION # 214
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. Manually install required libraries in a system-wide Python environment using pip install without version specifications
B. Use a virtual machine with all dependencies pre-installed to avoid any software conflicts
C. Use Conda environments with version-pinned dependencies to create an isolated environment for RAPIDS and CUDA
D. Install RAPIDS, PyTorch, TensorFlow, and other libraries in the same Conda base environment for simplified access

Answer: C

NEW QUESTION # 215
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 large, structured dataset stored in a Parquet format
B. A dataset consisting of unstructured text stored as plain .txt files
C. A dataset stored as an SQLite database file
D. A small dataset with fewer than 1,000 rows stored in an Excel file

Answer: A

NEW QUESTION # 216
You are tasked with optimizing the performance of a large-scale data science project that involves deep learning models on a cloud infrastructure. Your organization is using GPUs for model training.
Which of the following strategies would be the most effective in optimizing GPU performance for data science tasks? (Select two)

A. Use a single cloud instance with the largest GPU available to ensure maximum performance.
B. Overclock the GPU to achieve higher computational speeds and improve training times.
C. Use larger batch sizes to make better use of GPU memory during model training.
D. Utilize multi-GPU training to parallelize the workload, reducing training time.
E. Optimize GPU performance by limiting the number of threads running on each GPU.

Answer: C,D

NEW QUESTION # 217
A data scientist is training a deep learning model on an NVIDIA GPU but notices that the training speed is not significantly faster than when using a CPU.
Which of the following strategies is the best approach to fully utilize GPU acceleration and optimize training performance?

A. Using mixed precision training with NVIDIA Tensor Cores
B. Disabling cuDNN optimizations to allow more flexibility in kernel execution
C. Using only CPU-based data augmentation to keep the GPU dedicated to training
D. Increasing the batch size beyond the GPU's memory limit

Answer: A

NEW QUESTION # 218
You are working on a data science project that involves processing large-scale financial transaction data. You want to optimize data manipulation operations using NVIDIA's RAPIDS cuDF.
Which of the following approaches best leverages NVIDIA technologies for efficient data manipulation?

A. Convert the dataset into a SQLite database and execute SQL queries to perform data transformations.
B. Use cuDF DataFrames for data manipulation and rely on GPU-accelerated functions like .groupby(),
.merge(), and .applymap().
C. Load the dataset into a Pandas DataFrame and use multi-threading to speed up operations.
D. Preprocess the data using Apache Spark's CPU-based DataFrame API before transferring it to a GPU for machine learning.

Answer: B

NEW QUESTION # 219
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