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NVIDIA NCP-ADS Exam Syllabus Topics:

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

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q28-Q33):

NEW QUESTION # 28
You are working on a data science project that requires augmenting a dataset using synthetic data.
You are utilizing cuDF and NVIDIA RAPIDS to speed up the data generation process.
Which of the following methods is the most effective way to generate synthetic data using cuDF in a RAPIDS workflow?

A. Use cudf.DataFrame.applymap() to create new synthetic features through complex mathematical functions.
B. Use cuDF to manipulate data distributions and generate new data points based on existing features.
C. Use cudf.DataFrame.sample() to duplicate random rows and create synthetic data.

Answer: B

NEW QUESTION # 29
You are preprocessing a dataset using NVIDIA RAPIDS cuDF and need to handle missing values in the column temperature by replacing them with the column's median value.
Which of the following approaches correctly achieves this in an optimized manner?

A. df['temperature'].fillna(df['temperature'].mean(), inplace=True)
B. 1. df['temperature'] = df['temperature'].map(2. lambda x: df['temperature'].median() if x is None else x
3.)
C. df['temperature'].dropna(inplace=True)
D. df['temperature'].fillna(df['temperature'].median(), inplace=True)

Answer: D

NEW QUESTION # 30
You are working with a GPU-based cloud environment and need to optimize the memory usage for a dataset that contains a column item_id representing unique product IDs. The item_id values are large integers, and there are over 10 million distinct product IDs.
Which of the following is the most memory-efficient data type choice for this column?

A. df['item_id'] = df['item_id'].astype('int32')
B. df['item_id'] = df['item_id'].astype('string')
C. df['item_id'] = df['item_id'].astype('int64')
D. df['item_id'] = df['item_id'].astype('float64')

Answer: C

NEW QUESTION # 31
You are working with a large dataset using NVIDIA RAPIDS cuDF and need to normalize a numerical column (price) to scale its values between 0 and 1.
Which of the following approaches correctly normalizes the column using cuDF?

A. df["price"] = ( 2. df["price"] - df["price"].min() 3. ) / (df["price"].max() - df["price"].min())
B. df["price"] = df["price"] / df["price"].max()
C. df["price"] = (df["price"] - df["price"].mean()) / df["price"].std()
D. df["price"] = df["price"].applymap( 2. lambda x: (x - df["price"].min()) 3. / (df["price"].max() - df["price"].min()) 4. )

Answer: A

NEW QUESTION # 32
A data scientist is using NVIDIA RAPIDS cuDF to process a large dataset of customer transactions.
The dataset contains numerical, categorical, and timestamp-based features.
To optimize memory usage and performance on NVIDIA GPUs, which approach should they take when selecting data types?

A. Avoid downcasting integer columns, as lower-bit integer types (e.g., int8) are not supported in GPU- accelerated computations.
B. Convert all timestamp features into object (string) format to maintain readability and ensure compatibility with GPU processing.
C. Store all numerical columns as float64 to preserve maximum precision, even if lower precision suffices.
D. Convert categorical variables into cuDF categorical data types and downcast numerical columns to the smallest possible precision without losing information.

Answer: D

NEW QUESTION # 33
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