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NVIDIA NCP-ADS Exam Syllabus Topics:

Section	Weight	Objectives
GPU and Cloud Computing	16%	- Cloud GPU environments and deployment - Resource management and scaling strategies - CRISP-DM and data science methodology - GPU architecture and acceleration principles
Data Manipulation and Software Literacy	19%	- Data processing libraries selection and usage - Dependency management and containerization - Performance profiling and optimization tools - GPU-accelerated ETL workflows
Data Preparation	17%	- Workflow monitoring and bottleneck identification - Feature engineering and data type optimization - Data validation and quality assurance - Data cleaning, preprocessing and transformation
Data Analysis	14%	- Distributed and parallel data processing - Exploratory Data Analysis (EDA) - Data visualization and graph analytics - Time-series analysis and anomaly detection
MLOps	19%	- Model deployment and serving - Monitoring, logging and maintenance - End-to-end workflow management - Pipeline automation and orchestration
Machine Learning	15%	- Distributed training strategies - Model evaluation and validation - Model training and hyperparameter tuning - GPU-accelerated ML frameworks and algorithms

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NVIDIA NCP-ADS Hot Questions, NCP-ADS Latest Materials

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q267-Q272):

NEW QUESTION # 267
A data engineer is designing an Extract, Transform, Load (ETL) pipeline for a retail analytics platform that processes millions of customer transactions per day. The primary objective is to accelerate data ingestion, transformation, and storage while ensuring efficient scalability.
Which of the following approaches would be the most effective for optimizing this ETL workflow using NVIDIA-accelerated ETL tools?

A. Implement ETL processes using only SQL-based transformations within a relational database system.
B. Use Apache Spark with CPU-based processing instead of leveraging GPU acceleration.
C. Perform all transformations using Pandas DataFrames and then use multiprocessing to parallelize the workload on CPUs.
D. Use NVIDIA RAPIDS cuDF for data transformations and Dask-cuDF for parallelized processing across multiple GPUs.

Answer: D

NEW QUESTION # 268
You are working on an accelerated data science project and need to acquire a large dataset stored in a Parquet file format and load it efficiently for GPU processing using NVIDIA RAPIDS.
Which of the following approaches is the most efficient way to load the dataset into a GPU-accelerated DataFrame?

A. df = cudf.read_csv("data.parquet")
B. df = cudf.read_parquet("data.parquet")
C. df = cudf.to_gpu(pd.read_parquet("data.parquet"))
D. df = pd.read_parquet("data.parquet")

Answer: B

NEW QUESTION # 269
You are working on a time-series forecasting project using NVIDIA RAPIDS and GPU-accelerated machine learning. The dataset consists of 10 years of daily stock price data. Your goal is to implement a model that efficiently handles large-scale time-series data while leveraging GPU acceleration for optimal performance.
Which approach best utilizes NVIDIA technologies for efficient forecasting?

A. Use Dask with pandas for data preprocessing, then train a TensorFlow LSTM model on the CPU.
B. Use cuDF to load and preprocess the data, then apply FB Prophet for forecasting.
C. Use PyTorch with CPU acceleration to train a convolutional neural network (CNN) for forecasting.
D. Use cuDF for data preprocessing and train an XGBoost model with GPU acceleration for forecasting.

Answer: D

NEW QUESTION # 270
You are using NVIDIA DLProf to analyze the performance of a deep learning model deployed on an A100 GPU. The report indicates that compute-bound operations are dominating execution time, and kernel execution efficiency is below 50%.
What is the best action to take based on this insight?

A. Use DLProf's Tensor Core Analysis to check if the model is leveraging Tensor Cores effectively.
B. Reduce the number of layers in the model to decrease computation time.
C. Enable mixed precision training to improve computational efficiency.
D. Increase the batch size to fully utilize available GPU memory and reduce per-sample processing overhead.

Answer: A

NEW QUESTION # 271
Which of the following statements best describes the role of GPUs in accelerating data science workloads?

A. GPUs are designed primarily for rendering graphics and have limited utility in machine learning and deep learning applications.
B. GPUs are only effective for acceleration when used in conjunction with Tensor Processing Units (TPUs), as they cannot train deep learning models independently.
C. GPUs are optimized for sequential data processing tasks, making them more efficient than CPUs for database operations.
D. GPUs use thousands of smaller cores that can execute many parallel computations simultaneously, making them ideal for large-scale matrix operations.

Answer: D

NEW QUESTION # 272
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