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

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q139-Q144):

NEW QUESTION # 139
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 Apache Spark with CPU-based processing for ETL, then convert the results into cuDF for accelerated analytics.
B. Store all data as CSV files and perform ETL operations using traditional row-based processing.
C. Use Pandas DataFrame for transformation, and then convert the dataset to cuDF before writing to storage.
D. 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.

Answer: D

NEW QUESTION # 140
A machine learning engineer is training a convolutional neural network (CNN) on an NVIDIA GPU and needs to maximize throughput while avoiding OOM errors.
Which of the following techniques is the most effective way to balance memory efficiency and training speed?

A. Using dynamic batch sizing based on available GPU memory
B. Loading all dataset samples into GPU memory at the start of training
C. Allocating a fixed batch size without monitoring memory usage
D. Using a batch size of 1 to minimize memory usage

Answer: A

NEW QUESTION # 141
You are working with a large time-series dataset consisting of millions of records and want to efficiently visualize trends over time using NVIDIA technologies. The dataset is stored as a cuDF DataFrame, and you need to generate an interactive line plot with minimal performance overhead.
Which of the following is the best approach to achieve this goal?

A. Use the hvPlot library with RAPIDS cuDF to directly render the time-series data interactively
B. Use the Bokeh library to plot the time-series data from a cuDF DataFrame directly
C. Convert the cuDF DataFrame to a Pandas DataFrame and plot using Matplotlib
D. Load the data into a Spark DataFrame and visualize using Apache Zeppelin

Answer: A

NEW QUESTION # 142
A machine learning engineer is working with a 1 TB dataset stored in Apache Parquet format and wants to analyze the data for patterns before building a model. The engineer is considering various acceleration methods.
Which of the following approaches would be the best choice for efficient analysis?

A. Convert the Parquet file to a Pandas DataFrame and perform analysis using Pandas functions.
B. Use a GPU-accelerated library such as RAPIDS cuDF to load and process the Parquet file efficiently.
C. Read the Parquet file line by line using Python's built-in file handling functions to save memory.
D. Load the dataset into a relational database and query it using simple SQL statements.

Answer: B

NEW QUESTION # 143
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. Load the dataset into Modin with a Dask backend and use .fillna() for parallel execution
B. Use cuDF's .fillna() method to replace missing values in GPU memory
C. Convert the dataset to a pandas DataFrame, fill missing values, and then convert it back to cuDF
D. Drop all missing values using .dropna() instead of filling them, as GPU memory is limited

Answer: B

NEW QUESTION # 144
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