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NVIDIA NCP-ADS Exam Syllabus Topics:

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

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q148-Q153):

NEW QUESTION # 148
Your data science team is performing exploratory data analysis (EDA) on a large GPU-accelerated environment using cuDF and Dask-cuDF. During analysis, queries on categorical columns are performing poorly.
Which approach will most effectively improve query performance for categorical data in GPU-accelerated DataFrames?

A. Increase GPU memory allocation
B. Use CPU-based DataFrames (pandas) instead of GPU-based DataFrames
C. Store data in compressed file formats like Parquet
D. Convert categorical columns to integer codes

Answer: D

NEW QUESTION # 149
You are working on a data science project that requires augmenting a dataset using synthetic data.
You are utilizing cuDF and NVIDIA RAPIDS to speed up the data generation process.
Which of the following methods is the most effective way to generate synthetic data using cuDF in a RAPIDS workflow?

A. Use cudf.DataFrame.applymap() to create new synthetic features through complex mathematical functions.
B. Use cuDF to manipulate data distributions and generate new data points based on existing features.
C. Use cudf.DataFrame.sample() to duplicate random rows and create synthetic data.

Answer: B

NEW QUESTION # 150
You are working with a social network dataset containing millions of user interactions and need to identify influential users based on their connectivity and interactions.
Which approach using NVIDIA's cuGraph library is the most appropriate for this task?

A. Use cuGraph's DBSCAN clustering to detect communities in the social network.
B. Apply cuGraph's K-Means clustering to group users with similar connectivity patterns.
C. Use cuGraph's PageRank algorithm to rank users based on their importance in the network.
D. Run a breadth-first search (BFS) on the entire graph to find the most influential users.

Answer: C

NEW QUESTION # 151
You are optimizing a data pipeline for a large-scale machine learning project using NVIDIA RAPIDS and Apache Spark. The pipeline performs many expensive shuffle operations.
Which of the following is the most effective method to reduce shuffle and improve performance using NVIDIA technologies?

A. Implement a custom shuffle partitioning scheme using NVIDIA DALI for more control over data partitioning during shuffle operations.
B. Store data in HDFS before performing shuffle operations to reduce GPU memory overhead.
C. Use RAPIDS cuDF to repartition the data in the GPU memory after each shuffle.
D. Use RAPIDS cuDF to perform in-memory data processing on GPU before shuffling to avoid network communication.

Answer: D

NEW QUESTION # 152
You have a structured dataset containing 20 million records with missing values in several columns.
You need to fill missing values while ensuring that the approach is optimal for execution on NVIDIA GPUs.
Which method should you use?

A. Convert the dataset to a pandas DataFrame, fill missing values, and then convert it back to cuDF
B. Drop all missing values using .dropna() instead of filling them, as GPU memory is limited
C. Load the dataset into Modin with a Dask backend and use .fillna() for parallel execution
D. Use cuDF's .fillna() method to replace missing values in GPU memory

Answer: D

NEW QUESTION # 153
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