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NVIDIA NCP-ADS Exam Syllabus Topics:

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

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NVIDIA NCP-ADS模擬試験サンプル & NCP-ADS日本語版対策ガイド

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NVIDIA-Certified-Professional Accelerated Data Science 認定 NCP-ADS 試験問題 (Q121-Q126):

質問 # 121
You are analyzing a large-scale transportation network using cuGraph and notice that query times are longer than expected when running graph algorithms.
What is the best way to optimize graph processing performance using GPU-accelerated tools?

A. Store the graph in COO (Coordinate List) format instead of CSR (Compressed Sparse Row) format for faster traversal.
B. Use cugraph.to_directed() to convert the graph into a directed format, which improves GPU parallelism.
C. Use cugraph.filter_unconnected_nodes() to remove unconnected nodes before processing.
D. Convert the graph to CSR (Compressed Sparse Row) format before running computations to improve memory efficiency.

正解：D

質問 # 122
You need to determine the optimal data processing library for a small dataset of 500,000 records that will be processed on a multi-core CPU machine with no GPU access.
Which of the following libraries would be the most efficient for this task?

A. cuDF
B. Dask
C. pandas
D. PyTorch

正解：C

質問 # 123
You are working with a dataset containing 2 billion rows of financial transactions, and you need to perform exploratory data analysis (EDA) before building a predictive model.
Which of the following approaches is the most appropriate for handling this data efficiently?

A. Convert the dataset to JSON format and use Python's built-in json module to parse and analyze it efficiently.
B. Load the entire dataset into a Pandas DataFrame and analyze it using Pandas built-in methods.
C. Use an accelerated data science framework such as RAPIDS cuDF or Dask to distribute computations across GPUs.
D. Use SQLite to store the data locally and run queries sequentially to minimize memory consumption.

正解：C

質問 # 124
You are designing an ETL pipeline to process terabytes of financial transaction data in real time.
The pipeline consists of:
Extracting data from multiple sources (CSV, Parquet, and SQL databases), Transforming the data using operations such as filtering, joins, and aggregations, Loading the processed data into a data lake for analytics.
Given that you are using NVIDIA RAPIDS cuDF for GPU-accelerated ETL, which of the following approaches optimizes performance while ensuring scalability?

A. Load all data into a single, large cuDF DataFrame before performing transformations
B. Use cuDF to read and process the data in batches, leveraging Dask-cuDF for distributed computation when necessary
C. Use CPU-based ETL frameworks such as Apache Spark without GPU acceleration
D. Convert cuDF DataFrames to Pandas DataFrames before performing transformations for compatibility

正解：B

質問 # 125
A data scientist is using an NVIDIA RAPIDS-based data processing pipeline on a GPU cluster. They notice that the pipeline is not performing as expected and suspect a bottleneck.
Which of the following approaches would best help identify the source of the bottleneck?

A. Disable GPU parallelism and run computations on the CPU to compare performance.
B. Increase the batch size of data being processed without analyzing memory utilization.
C. Rely on nvidia-smi alone to check GPU utilization without considering other profiling tools.
D. Use nsys (NVIDIA Nsight Systems) to profile GPU kernel execution and memory transfer times.

正解：D

質問 # 126
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