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NVIDIA NCP-ADS Exam Syllabus Topics:

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

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q151-Q156):

NEW QUESTION # 151
You are working with a dataset containing hundreds of millions of records, and you need to perform ETL operations such as filtering, joins, and aggregations. Given the dataset size, which NVIDIA- accelerated library should you use to achieve optimal performance?

A. cuPy, because it provides GPU-accelerated array operations, making it the best option for processing tabular data.
B. Pandas, as it is widely used and supports all common DataFrame operations, even for very large datasets.
C. cuDF, as it provides GPU-accelerated DataFrame operations similar to Pandas, allowing for efficient processing of large datasets.
D. NumPy, because it is optimized for numerical computing and offers better performance for handling tabular data.

Answer: C

NEW QUESTION # 152
You are working on a large dataset for a machine learning model and need to preprocess the data efficiently using NVIDIA RAPIDS cuDF on a GPU-accelerated system.
Which of the following statements is correct regarding data preparation using cuDF?

A. cuDF supports common Pandas-like operations, such as filtering, merging, and groupby, but performs them efficiently on the GPU.
B. cuDF requires all input data to be stored in Apache Parquet format, as it does not support CSV or JSON ingestion.
C. cuDF can be used as a direct replacement for Pandas, but it is optimized for multi-node distributed computing rather than GPU acceleration.
D. cuDF operations are executed lazily and require an explicit compute() call to execute transformations.

Answer: A

NEW QUESTION # 153
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 in cuGraph is a label propagation algorithm that clusters nodes into communities rather than ranking their importance.
B. PageRank in cuGraph operates only on undirected graphs and cannot be applied to networks where edges have a direction.
C. PageRank assigns equal importance to all nodes in the graph initially and updates values only based on outgoing edges, ignoring incoming edges.
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 # 154
A data scientist wants to process a large dataset using multiple GPUs on an NVIDIA-supported system. They decide to use Dask to enable parallelism.
Which of the following steps is most essential for leveraging Dask for multi-GPU scaling?

A. Use ThreadPoolExecutor to manage parallel computations on GPUs
B. Train a deep learning model on a single GPU first, then switch to Dask for scaling
C. Use dask.array or dask.dataframe with dask_cuda.CUDACluster to distribute computations across multiple GPUs
D. Convert all data into Pandas DataFrames before distributing computations

Answer: C

NEW QUESTION # 155
A team of data engineers is working on an Apache Spark-based distributed computing pipeline that leverages NVIDIA GPUs and RAPIDS. They notice that shuffle operations are causing significant slowdowns in performance.
Which optimization strategy should they implement to reduce shuffle impact?

A. Store shuffle data in Apache Parquet format on disk for faster access and reduced memory overhead.
B. Use Spark's default shuffle partitioning without any modification, as GPUs inherently optimize shuffle operations.
C. Disable GPU memory caching to allow automatic CPU-based shuffle optimization.
D. Use RAPIDS Spark-RAPIDS Plugin with GPU-accelerated caching to minimize redundant shuffle operations.

Answer: D

NEW QUESTION # 156
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