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NVIDIA NCP-ADS Exam Syllabus Topics:

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

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q58-Q63):

NEW QUESTION # 58
A data scientist is working on a machine learning model for fraud detection. Due to the limited size of the dataset, they decide to generate synthetic data using NVIDIA RAPIDS AI and cuDF.
Which of the following approaches is the most efficient and effective for generating synthetic data while ensuring compatibility with RAPIDS AI workflows?

A. Use numpy and pandas to generate synthetic data, then convert the DataFrame to cuDF before using it in RAPIDS AI workflows.
B. Train a generative adversarial network (GAN) using PyTorch and then use the generated samples in RAPIDS AI without any additional processing.
C. Use cuDF DataFrame operations to create new synthetic samples by applying random transformations (e.g., noise injection, permutation) to the existing dataset.
D. Use the RAPIDS cuML library to directly generate synthetic tabular data with controlled statistical properties.

Answer: C

NEW QUESTION # 59
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 SQLite as a local database for large-scale data analysis
C. Using Excel to manipulate large datasets
D. Using Dask for parallel processing of large datasets
E. Using SQLAlchemy to interact with databases for large data processing

Answer: A,D

NEW QUESTION # 60
A data scientist is training a deep learning model on an NVIDIA GPU and wants to profile the model to identify performance bottlenecks. The scientist chooses to use NVIDIA DLProf.
Which of the following steps is the most effective way to profile the model using DLProf?

A. Modify the training script to manually insert timing functions for each layer and compare execution times.
B. Use nvprof instead of DLProf since it provides more detailed profiling for deep learning workloads.
C. Rely on general CPU profiling tools like perf and gprof to analyze GPU performance.
D. Run the model using dlprof --mode profile to collect performance metrics and generate a report.

Answer: D

NEW QUESTION # 61
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. Use dask.dataframe.repartition() to distribute data evenly across multiple GPUs.
B. Manually allocate GPU memory using cupy for each worker instead of using Dask's scheduler.
C. Set up a single Dask dataframe without partitioning and rely on automatic workload balancing.
D. Use dask_cuda.LocalCUDACluster() to create a multi-GPU cluster and dask.distributed.Client() to manage execution.

Answer: D

NEW QUESTION # 62
You are a data scientist working on a large-scale deep learning project. Your team needs to train a deep neural network (DNN) on terabytes of image data using NVIDIA GPUs in the cloud.
Which of the following approaches will maximize GPU utilization and optimize training time in an NVIDIA- accelerated cloud environment?

A. Use Jupyter Notebooks on a local machine with minimal GPU resources and transfer model checkpoints manually to the cloud for training.
B. Select low-cost, consumer-grade GPUs available in the cloud marketplace to reduce costs.
C. Use NVIDIA NGC containers with TensorFlow or PyTorch and enable NCCL for multi-GPU communication.
D. Avoid using mixed precision training and rely solely on full-precision (FP32) computations for numerical stability.

Answer: C

NEW QUESTION # 63
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