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NVIDIA NCP-ADS Exam Syllabus Topics:

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

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q254-Q259):

NEW QUESTION # 254
When using cuDF from NVIDIA RAPIDS for GPU-accelerated data manipulation, which of the following operations will not efficiently utilize the GPU?

A. Filtering a large dataset based on a condition (e.g., column > 1000).
B. Applying a Python lambda function row-wise to a DataFrame.
C. Merging two large datasets on a common column.
D. Performing a group-by operation followed by an aggregation.

Answer: B

NEW QUESTION # 255
A machine learning team is handling large-scale datasets that need to be efficiently stored and accessed within an NVIDIA RAPIDS workflow.
Which of the following storage formats and techniques provides the best performance for GPU-based data science pipelines?

A. Store data in SQLite databases and query it into pandas before converting it to cuDF.
B. Store data in the Apache Parquet format and load it directly into cuDF using cuDF's read_parquet() method.
C. Save data in JSON format to ensure hierarchical relationships and flexibility before loading it into cuDF.
D. Use CSV files for storage, as they are widely compatible and can be read quickly using cuDF's read_csv() method.

Answer: B

NEW QUESTION # 256
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 # 257
You are working on a large dataset for a machine learning model that will be trained using RAPIDS cuML. The dataset includes categorical, integer, and floating-point features.
Which of the following approaches is the best practice for determining the optimal data type choice for each feature using NVIDIA's RAPIDS cuDF library?

A. Convert all numerical data to float64 for maximum precision in calculations.
B. Use float16 for all floating-point data to reduce memory usage and increase GPU processing speed.
C. Convert categorical variables into int8 to optimize GPU memory usage.
D. Use float32 instead of float64 for floating-point numbers when possible, and leverage int8, int16, or int32 for categorical and integer data based on their range.

Answer: D

NEW QUESTION # 258
You need to train a deep learning model using PyTorch on a dataset too large for a single GPU. You decide to use Dask with NVIDIA GPUs for multi-GPU scaling.
Which approach is the most effective for distributing the workload?

A. Use Dask's built-in deep learning API to automatically distribute PyTorch models across GPUs
B. Use Dask Bag to shard the dataset and train separate PyTorch models on each shard
C. Use Dask.delayed to wrap PyTorch training functions and schedule them across multiple GPUs
D. Use Dask-CUDA workers with PyTorch's DistributedDataParallel (DDP) for training across multiple GPUs

Answer: D

NEW QUESTION # 259
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