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NVIDIA NCP-ADS Exam Syllabus Topics:

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

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q24-Q29):

NEW QUESTION # 24
Which of the following tools or techniques are essential for effectively working with large-scale data in a distributed environment? (Select two)

A. Using Apache Spark for distributed data processing
B. Using Excel to manipulate large datasets
C. Using SQLAlchemy to interact with databases for large data processing
D. Using Dask for parallel processing of large datasets
E. Using SQLite as a local database for large-scale data analysis

Answer: A,D

NEW QUESTION # 25
A financial services company is using NVIDIA RAPIDS cuML to train a credit risk assessment model.
The dataset contains hundreds of numerical and categorical features, including loan amount, credit score, income, employment history, and previous loan defaults.
To optimize feature selection using NVIDIA technologies, which approach should they take?

A. Convert all numerical features into categorical variables to simplify the data and reduce the need for feature selection.
B. Avoid feature selection, as modern deep learning models are capable of automatically handling redundant and irrelevant features.
C. Keep all features in the dataset to ensure the model captures as much information as possible, even if some features are redundant.
D. Use cuML's feature selection algorithms, such as Recursive Feature Elimination (RFE), to identify the most important predictors.

Answer: D

NEW QUESTION # 26
You are implementing a GPU-accelerated ETL pipeline that involves joining two large datasets:
Dataset A: A cuDF DataFrame with 10 million customer records.
Dataset B: A cuDF DataFrame with 100 million transaction records.
The goal is to efficiently perform a join operation to link customer details with transaction data, ensuring that the pipeline remains scalable and performant.
Which of the following is the best approach to optimize the join operation using NVIDIA RAPIDS?

A. Convert both DataFrames to Pandas before performing the join for compatibility
B. Store the transaction data as a CSV file and perform joins using SQL queries before loading it into cuDF
C. Perform the join operation entirely in a CPU-based Spark environment for better stability
D. Use cuDF's .merge() function and ensure both DataFrames have the correct index before joining

Answer: D

NEW QUESTION # 27
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 CUDA
B. Nvidia Nsight Systems
C. Nvidia Triton Inference Server
D. Nvidia DeepStream SDK

Answer: B

NEW QUESTION # 28
You are tasked with processing a large dataset using multiple GPUs to accelerate computation. You decide to use Dask to implement data parallelism with NVIDIA's RAPIDS framework to maximize GPU utilization.
Which of the following steps is essential for efficiently distributing the workload across multiple GPUs in Dask?

A. Manually allocate GPU memory using cupy for each worker instead of using Dask's scheduler.
B. Use dask.dataframe.repartition() to distribute data evenly across multiple GPUs.
C. Use dask_cuda.LocalCUDACluster() to create a multi-GPU cluster and dask.distributed.Client() to manage execution.
D. Set up a single Dask dataframe without partitioning and rely on automatic workload balancing.

Answer: C

NEW QUESTION # 29
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