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Following are some reference material for actual NVIDIA NCP-ADS exam test

NVIDIA NCP-ADS Exam Syllabus Topics:

NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. Which of the following can DLProf specifically help identify when profiling a deep learning model on Nvidia GPUs?

A) Training dataset bias.
B) Number of model parameters.
C) GPU utilization and memory usage.
D) Hyperparameter tuning results.

2. 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 a batch size of 1 to minimize memory usage
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 dynamic batch sizing based on available GPU memory

3. You are working with a dataset where numerical features have different scales. To ensure uniformity across features, you decide to standardize the data using NVIDIA RAPIDS cuML.
Which of the following methods correctly standardizes the data in a GPU-accelerated manner?

A) df = df.apply(lambda x: (x - x.mean()) / x.std(), axis=1)
B) df = (df - df.min()) / (df.max() - df.min())
C) df = (df - df.mean()) / df.std()
D) 1. scaler = cuml.preprocessing.StandardScaler() 2. df = scaler.fit_transform(df)

4. You are building an MLOps pipeline for a predictive model that uses tabular data with both categorical and numerical features.
To ensure efficient data processing and optimal model training on an NVIDIA GPU, which of the following data types would be most suitable for a categorical feature representing different product categories?

A) Int64
B) String
C) Float64
D) Int32

5. You are tasked with designing an ETL workflow for a large-scale data processing pipeline using NVIDIA technologies. You need to ensure that the extraction, transformation, and loading phases are optimized for performance using hardware acceleration.
Which of the following NVIDIA technologies would be most suitable for accelerating the ETL process?

A) RAPIDS
B) TensorFlow
C) TensorRT
D) CUDA

Solutions: