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NVIDIA NCP-ADS Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Data Analysis	14%	- Exploratory Data Analysis (EDA) 1. Perform time series analysis and visualization 2. Detect anomalies in time series datasets 3. Use cuGraph for graph analytics
Topic 2: 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 3: 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 4: Data Manipulation and Software Literacy	19%	- ETL and Data Processing Workflows 1. Distributed data processing frameworks (Dask) 2. GPU-accelerated ETL design and implementation 3. Data caching and performance optimization
Topic 5: MLOps	19%	- Deployment and Monitoring 1. Performance benchmarking and optimization 2. Memory and capacity evaluation 3. Model deployment in production environments
Topic 6: Machine Learning	15%	- Model Development and Optimization 1. Memory optimization techniques (mixed precision, batching) 2. Feature engineering 3. Multi-GPU training comparison 4. Hyperparameter tuning

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q19-Q24):

NEW QUESTION # 19
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 RAPIDS cuDF to load, filter, and transform the dataset on the GPU
B. Convert the dataset to a NumPy array and use standard Python loops
C. Use multiprocessing in Python to parallelize pandas operations across CPU cores
D. Load the dataset into pandas and use apply() for transformations

Answer: A

NEW QUESTION # 20
A data scientist is working with large-scale ETL (Extract, Transform, Load) pipelines on GPU- accelerated infrastructure using RAPIDS. The workload involves frequent shuffle operations, which significantly impact performance.
What is the best approach using NVIDIA technologies to reduce shuffle overhead and improve performance?

A. Use RAPIDS cuDF's GPU memory caching to store intermediate DataFrames and avoid redundant shuffle operations.
B. Use RAPIDS cuML to replace shuffle-intensive operations with an ML model that predicts data distribution.
C. Enable CUDA Unified Memory to automatically optimize shuffle performance without manual intervention.
D. Store all intermediate shuffle data in CPU memory using pandas to ensure persistence and reduce GPU load.

Answer: A

NEW QUESTION # 21
A financial institution is training a fraud detection model on a GPU-powered NVIDIA RAPIDS cuML pipeline. Their dataset includes customer ages, transaction amounts, merchant names, and transaction timestamps.
To optimize GPU memory usage while preserving accuracy, how should they store these features?

A. Keep customer ages in float16 format to reduce memory consumption, as integer types are less efficient in GPU operations.
B. Store transaction amounts as float32, customer ages as int8, merchant names as categorical, and timestamps as datetime64.
C. Convert all numeric data to float64 for maximum precision, as rounding errors in lower precision could impact the model's accuracy.
D. Convert timestamps into UNIX epoch integers instead of using datetime64 for more efficient GPU computations.

Answer: B

NEW QUESTION # 22
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. Disabling cuDNN optimizations to allow more flexibility in kernel execution
B. Increasing the batch size beyond the GPU's memory limit
C. Using only CPU-based data augmentation to keep the GPU dedicated to training
D. Using mixed precision training with NVIDIA Tensor Cores

Answer: D

NEW QUESTION # 23
A data scientist is working with a large dataset for a machine learning model and wants to accelerate feature engineering using a GPU.
Which of the following approaches will provide the most significant performance boost when using GPU acceleration?

A. Reducing dataset size by randomly removing data points without considering class balance.
B. Using a single-threaded feature extraction approach to avoid overhead from parallelization.
C. Using traditional pandas DataFrames and NumPy operations optimized for CPU processing.
D. Using RAPIDS cuDF and cuML libraries to perform feature transformations on a GPU.

Answer: D

NEW QUESTION # 24
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