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NVIDIA NCP-ADS Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Machine Learning	15%	- Model Development and Optimization 1. Memory optimization techniques (mixed precision, batching) 2. Multi-GPU training comparison 3. Feature engineering 4. Hyperparameter tuning
Topic 2: GPU and Cloud Computing	16%	- GPU Optimization and Infrastructure 1. CRISP-DM workflow execution 2. Benchmarking GPU workflows 3. Docker and Conda environment management
Topic 3: Data Preparation	17%	- Data Cleaning and Transformation 1. Data normalization and standardization 2. Synthetic data generation with RAPIDS 3. cuDF and pandas data preprocessing
Topic 4: Data Analysis	14%	- Exploratory Data Analysis (EDA) 1. Perform time series analysis and visualization 2. Use cuGraph for graph analytics 3. Detect anomalies in time series datasets
Topic 5: MLOps	19%	- Deployment and Monitoring 1. Memory and capacity evaluation 2. Model deployment in production environments 3. Performance benchmarking and optimization
Topic 6: Data Manipulation and Software Literacy	19%	- ETL and Data Processing Workflows 1. Distributed data processing frameworks (Dask) 2. Data caching and performance optimization 3. GPU-accelerated ETL design and implementation

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q31-Q36):

NEW QUESTION # 31
You are tasked with designing an ETL workflow for a large-scale data processing pipeline using NVIDIA technologies. You need to ensure that the extraction, transformation, and loading phases are optimized for performance using hardware acceleration.
Which of the following NVIDIA technologies would be most suitable for accelerating the ETL process?

A. RAPIDS
B. TensorRT
C. CUDA
D. TensorFlow

Answer: A

NEW QUESTION # 32
Which of the following Nvidia technologies is primarily used for performing benchmarking and optimizing GPU-accelerated deep learning workflows, especially focusing on model training performance?

A. Nvidia Triton Inference Server
B. Nvidia DeepStream SDK
C. Nvidia CUDA
D. Nvidia Nsight Systems

Answer: D

NEW QUESTION # 33
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. Using only CPU-based data augmentation to keep the GPU dedicated to training
C. Disabling cuDNN optimizations to allow more flexibility in kernel execution
D. Increasing the batch size beyond the GPU's memory limit

Answer: A

NEW QUESTION # 34
A data scientist is processing a dataset that is too large to fit into the memory of a single GPU. They decide to use Dask with cuDF to leverage multiple GPUs for accelerated computation.
Which of the following approaches ensures efficient parallelism when working with dask_cudf?

A. Assign explicit chunks of data to each worker by manually dividing the dataset before passing it to Dask.
B. Use a single-threaded scheduler to avoid contention when running on multiple GPUs.
C. Convert the dataset into a Dask cuDF dataframe and use persist() to keep it in GPU memory.
D. Use Dask futures to asynchronously submit GPU tasks and collect results when completed.

Answer: C

NEW QUESTION # 35
A data scientist is preprocessing a dataset containing several types of features:
A timestamp column storing millisecond-resolution timestamps.
A column with binary categorical values (Yes/No).
A column containing large continuous numerical values.
A column containing product category codes ranging from 0 to 5000.
Which of the following data type choices is the most optimal for maximizing GPU processing efficiency using NVIDIA cuDF?

A. Use float64 for timestamps, int8 for binary categorical values, float32 for continuous numerical values, and int32 for product category codes.
B. float32 is the best choice for large continuous numerical values, balancing precision and GPU efficiency.
C. Use string data type for timestamps, int32 for binary values, float16 for continuous numerical values, and int64 for product category codes.
D. Binary categorical values (Yes/No) should be stored as bool, which takes up minimal space.
E. Product category codes (range: 0-5000) fit within int16 (which can hold values from -32,768 to
32,767), making it more memory-efficient than int32.
F. Convert timestamps to datetime64[ms], encode binary values as bool, use float32 for continuous values, and int16 for product category codes.
G. Store timestamps as int64, encode binary values as float16, use float64 for continuous numerical values, and use int8 for product category codes.

Answer: F

NEW QUESTION # 36
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