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NVIDIA NCP-ADS Exam Syllabus Topics:

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

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q261-Q266):

NEW QUESTION # 261
You are building a predictive model for retail sales forecasting and need a dataset that includes historical sales transactions, customer demographics, and external economic indicators (e.g., inflation rate, unemployment rate).
Which of the following datasets would be the most appropriate for your model?

A. A dataset with global temperature trends over the past decade
B. A dataset of product reviews and customer sentiments from an e-commerce website
C. A dataset containing transaction history and customer profiles from a retail company
D. A public dataset of annual GDP per country

Answer: C

NEW QUESTION # 262
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. Save data in JSON format to ensure hierarchical relationships and flexibility before loading it into cuDF.
B. Store data in SQLite databases and query it into pandas before converting it to cuDF.
C. Use CSV files for storage, as they are widely compatible and can be read quickly using cuDF's read_csv() method.
D. Store data in the Apache Parquet format and load it directly into cuDF using cuDF's read_parquet() method.

Answer: D

NEW QUESTION # 263
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. PyTorch
B. pandas
C. cuDF
D. Dask

Answer: B

NEW QUESTION # 264
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. Rely on general CPU profiling tools like perf and gprof to analyze GPU performance.
B. Run the model using dlprof --mode profile to collect performance metrics and generate a report.
C. Use nvprof instead of DLProf since it provides more detailed profiling for deep learning workloads.
D. Modify the training script to manually insert timing functions for each layer and compare execution times.

Answer: B

NEW QUESTION # 265
You are preprocessing a dataset using NVIDIA RAPIDS cuDF and need to handle missing values in the column temperature by replacing them with the column's median value.
Which of the following approaches correctly achieves this in an optimized manner?

A. df['temperature'].fillna(df['temperature'].mean(), inplace=True)
B. 1. df['temperature'] = df['temperature'].map(2. lambda x: df['temperature'].median() if x is None else x
3.)
C. df['temperature'].fillna(df['temperature'].median(), inplace=True)
D. df['temperature'].dropna(inplace=True)

Answer: C

NEW QUESTION # 266
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