NCP-ADS Exam Guide: NVIDIA-Certified-Professional Accelerated Data Science - NCP-ADS Exam Collection

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NVIDIA NCP-ADS Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Data Manipulation and Software Literacy	19%	- Performance profiling and optimization tools - GPU-accelerated ETL workflows - Dependency management and containerization - Data processing libraries selection and usage
Topic 2: Data Analysis	14%	- Data visualization and graph analytics - Distributed and parallel data processing - Time-series analysis and anomaly detection - Exploratory Data Analysis (EDA)
Topic 3: MLOps	19%	- Monitoring, logging and maintenance - End-to-end workflow management - Model deployment and serving - Pipeline automation and orchestration
Topic 4: GPU and Cloud Computing	16%	- CRISP-DM and data science methodology - Cloud GPU environments and deployment - GPU architecture and acceleration principles - Resource management and scaling strategies
Topic 5: Machine Learning	15%	- GPU-accelerated ML frameworks and algorithms - Model evaluation and validation - Model training and hyperparameter tuning - Distributed training strategies
Topic 6: Data Preparation	17%	- Data validation and quality assurance - Feature engineering and data type optimization - Data cleaning, preprocessing and transformation - Workflow monitoring and bottleneck identification

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q172-Q177):

NEW QUESTION # 172
Which tools or technologies from NVIDIA are essential for implementing an efficient MLOps pipeline in production environments? (Select two)

A. NVIDIA DLA (Deep Learning Accelerator) for model deployment
B. NVIDIA NGC for storing and sharing machine learning datasets
C. NVIDIA TensorRT for efficient model inference
D. NVIDIA CUDA for model training in cloud environments
E. NVIDIA Triton Inference Server for managing deployment and serving models

Answer: C,E

NEW QUESTION # 173
You are a data scientist analyzing a social media network with NVIDIA cuGraph to identify the most influential users using the PageRank algorithm.
Which option best describes how cuGraph PageRank operates on a directed graph?

A. PageRank assigns equal importance to all nodes in the graph initially and updates values only based on outgoing edges, ignoring incoming edges.
B. PageRank in cuGraph operates only on undirected graphs and cannot be applied to networks where edges have a direction.
C. PageRank in cuGraph is a label propagation algorithm that clusters nodes into communities rather than ranking their importance.
D. PageRank in cuGraph uses an iterative power method to update node importance values based on incoming edges, incorporating a damping factor to handle random jumps.

Answer: D

NEW QUESTION # 174
You are designing an ETL workflow to process large-scale financial transaction data using GPU acceleration. The dataset is stored in a Parquet file and contains millions of records.
Which of the following approaches is the most efficient for performing extract, transform, and load (ETL) operations using NVIDIA RAPIDS technologies?

A. Use Pandas DataFrame for transformation, and then convert the dataset to cuDF before writing to storage.
B. Use Apache Spark with CPU-based processing for ETL, then convert the results into cuDF for accelerated analytics.
C. Load the Parquet file directly into a cuDF DataFrame and use cuDF's built-in functions for transformations before writing the results back to storage.
D. Store all data as CSV files and perform ETL operations using traditional row-based processing.

Answer: C

NEW QUESTION # 175
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 the Apache Parquet format and load it directly into cuDF using cuDF's read_parquet() method.
B. Use CSV files for storage, as they are widely compatible and can be read quickly using cuDF's read_csv() method.
C. Store data in SQLite databases and query it into pandas before converting it to cuDF.
D. Save data in JSON format to ensure hierarchical relationships and flexibility before loading it into cuDF.

Answer: A

NEW QUESTION # 176
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. Use NVIDIA NGC containers with TensorFlow or PyTorch and enable NCCL for multi-GPU communication.
C. Select low-cost, consumer-grade GPUs available in the cloud marketplace to reduce costs.
D. Avoid using mixed precision training and rely solely on full-precision (FP32) computations for numerical stability.

Answer: B

NEW QUESTION # 177
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