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NVIDIA NCP-ADS Exam Syllabus Topics:

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

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q146-Q151):

NEW QUESTION # 146
A machine learning engineer is working on an image classification problem where the dataset is small and lacks variability. To improve generalization, the engineer decides to augment the dataset using NVIDIA RAPIDS.
What is the best method to generate synthetic data efficiently while leveraging GPU acceleration?

A. Use traditional CPU-based augmentation techniques like OpenCV to transform images and generate new data.
B. Use cuDF with cudf.DataFrame.sample() to create new samples by randomly selecting existing rows.
C. Use cuML.PCA() to reduce dimensionality and create synthetic samples by reconstructing the data with added noise.
D. Apply cuML.GaussianMixture() to generate new synthetic data points based on an estimated probability distribution.

Answer: D

NEW QUESTION # 147
Which of the following data normalization techniques is most appropriate when the dataset contains outliers, and you want to minimize the influence of those outliers on the model performance?

A. Min-Max Scaling
B. Log Transformation
C. Z-score Standardization
D. Robust Scaling

Answer: D

NEW QUESTION # 148
You are working with a dataset in a cloud-based GPU environment that contains a column country representing the country of origin for customers. The column contains only 10 unique country values, but the dataset has millions of rows.
Which of the following is the most memory-efficient approach to handle the country column in a cuDF DataFrame?

A. df['country'] = df['country'].astype('object')
B. df['country'] = df['country'].astype('category')
C. df['country'] = df['country'].astype('int32')
D. df['country'] = df['country'].astype('string')

Answer: B

NEW QUESTION # 149
You are working with a large dataset in RAPIDS cuDF and plan to standardize the numerical features using cuml.preprocessing.StandardScaler(). However, some columns contain missing values.
What is the best approach to handle the missing values before applying standardization?

A. Replace missing values with zero before standardization.
B. Use cuml.impute.SimpleImputer(strategy='mean') to replace missing values with the column mean.
C. cuml.impute.KNNImputer() to replace missing values based on k-nearest neighbors.
D. Use cudf.DataFrame.fillna(method='ffill') to forward-fill missing values.

Answer: B

NEW QUESTION # 150
You are conducting rapid experimentation on an NVIDIA GPU to determine the best trade-off between model accuracy and inference latency.
Which approach is the most efficient for systematically evaluating multiple configurations?

A. Train each possible model variation from scratch to evaluate accuracy and performance differences
B. Reduce training epochs significantly to save time, even if the model is underfitting
C. Test different model configurations on a CPU first before moving to the GPU for final evaluation
D. Use automated hyperparameter tuning tools like Optuna or Ray Tune with mixed precision training

Answer: D

NEW QUESTION # 151
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