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NVIDIA NCP-ADS Exam Syllabus Topics:

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

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q95-Q100):

NEW QUESTION # 95
A data engineering team is tasked with processing terabytes of log data every hour using an ETL pipeline. Due to the large data volume, they need a scalable GPU-accelerated solution that can distribute data processing across multiple GPUs.
Which approach best meets their needs?

A. Process data using Pandas, then export the results to a CSV file for GPU-accelerated analytics.
B. Use Dask-cuDF to distribute cuDF DataFrame operations across multiple GPUs, enabling parallel ETL processing.
C. Use NumPy for data transformations before converting the dataset into cuDF for final storage.
D. Use cuDF alone for processing log data, as it provides optimal performance on a single GPU.

Answer: B

NEW QUESTION # 96
You are working on a data science project where you need to process a large dataset containing
500 million records. You want to determine whether GPU acceleration would significantly improve performance.
Which of the following factors best indicates that you should use an accelerated computing solution like RAPIDS?

A. The dataset consists of simple arithmetic operations on a few columns and can be processed using vectorized NumPy operations.
B. The dataset is a structured table with less than 100,000 records and can be handled efficiently with a Pandas DataFrame.
C. The dataset is heavily structured but mainly requires text-based analysis using regex-based search and manipulation.
D. The dataset has high-dimensional sparse features and requires complex operations such as nearest neighbor search and clustering.

Answer: D

NEW QUESTION # 97
A data scientist is working with a large dataset that contains string-based numeric values that need to be converted to floating-point numbers for further analysis. The dataset is stored as a cuDF DataFrame, and the scientist needs to ensure the conversion is performed optimally on a GPU.
Which of the following is the best method for converting string-based numeric values to floating-point numbers using NVIDIA-accelerated processing?

A. Use cudf.DataFrame.astype(float) to convert string values to floating-point numbers efficiently on a GPU.
B. Convert the cuDF DataFrame to a Pandas DataFrame first, then apply astype(float) and convert it back to cuDF.
C. Use pandas.to_numeric() since pandas automatically handles type conversion.
D. Use NumPy's astype(float) method after converting the cuDF DataFrame into a NumPy array.

Answer: A

NEW QUESTION # 98
You are performing data cleansing on a large dataset using CuDF. The dataset contains numerical values, some of which are outliers. You need to remove or adjust these outliers to make your model training more robust.
Which of the following approaches should you consider for handling outliers efficiently in CuDF? (Select two)

A. Using dropna() to remove rows with outliers
B. Using quantile() to calculate the interquartile range (IQR) and filter out outliers
C. Using applymap() to apply a custom function for handling outliers
D. Using clip() to set a maximum and minimum threshold for numerical values

Answer: B,D

NEW QUESTION # 99
Which of the following techniques are best suited for efficiently processing and organizing large datasets using NVIDIA technologies? (Select two)

A. Using cuDF for GPU-accelerated DataFrame operations
B. Storing data directly on an HDD and processing with GPUs
C. Leveraging Dask for distributed GPU-based parallel computing
D. Using TensorFlow with custom data pipelines for data loading

Answer: A,C

NEW QUESTION # 100
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