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NVIDIA NCP-ADS Exam Syllabus Topics:

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

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q94-Q99):

NEW QUESTION # 94
You are working with a cuDF DataFrame and need to convert a column named sales from float64 to int32 to save memory.
Which of the following is the correct and most efficient way to perform this conversion in cuDF?

A. df['sales'].apply(lambda x: int(x))
B. df['sales'] = df['sales'].to_numeric('int32')
C. df['sales'] = df['sales'].astype('int32')
D. df['sales'].convert_dtypes('int32')

Answer: C

NEW QUESTION # 95
You are working on an MLOps workflow that loads a dataset into GPU memory for model training using RAPIDS cuDF. Before performing transformations, you want to verify that the dataset will fit into available GPU memory.
Which of the following methods provides the most accurate estimate of dataset memory consumption in a RAPIDS cudf.DataFrame?

A. cudf_df.memory_usage(deep=True).sum()
B. cudf_df.memory_usage().sum()
C. cudf_df.__sizeof__()
D. cudf_df.to_pandas().memory_usage(deep=True).sum()

Answer: B

NEW QUESTION # 96
You are working with cloud-based GPUs to process a large dataset (terabytes in size) stored in Parquet format. One column represents a unique identifier (e.g., product ID), and it contains only positive integers ranging from 1 to 100,000.
Which of the following data types provides the best balance of memory efficiency and performance?

A. float32
B. float64
C. int8
D. uint16

Answer: D

NEW QUESTION # 97
You are considering using a multi-GPU setup to accelerate training a large deep learning model.
Which of the following are important factors to consider when deciding whether to use single-GPU or multi-GPU training? (Select two)

A. The performance of multi-GPU training scales linearly with the number of GPUs, meaning adding more GPUs will always result in a proportional reduction in training time.
B. The success of multi-GPU training depends heavily on the ability to increase the batch size without exceeding memory limits.
C. The increase in training time from using a multi-GPU setup is negligible, as the overhead from communication and synchronization is minimal.
D. Multi-GPU setups require proper load balancing and efficient gradient synchronization, as uneven distribution of work can lead to suboptimal performance.

Answer: B,D

NEW QUESTION # 98
A data scientist is working with a large dataset for a machine learning model and wants to accelerate feature engineering using a GPU.
Which of the following approaches will provide the most significant performance boost when using GPU acceleration?

A. Reducing dataset size by randomly removing data points without considering class balance.
B. Using a single-threaded feature extraction approach to avoid overhead from parallelization.
C. Using RAPIDS cuDF and cuML libraries to perform feature transformations on a GPU.
D. Using traditional pandas DataFrames and NumPy operations optimized for CPU processing.

Answer: C

NEW QUESTION # 99
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