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Last Updated: Oct 11, 2026
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NVIDIA NCP-ADS Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Preparation | 17% | - Data loading and preprocessing
|
| Topic 2: Data Manipulation and Software Literacy | 19% | - Distributed computing with Dask
|
| Topic 3: Data Analysis | 14% | - Visualization
|
| Topic 4: MLOps | 19% | - Model deployment and serving
|
| Topic 5: GPU and Cloud Computing | 16% | - Cloud GPU environments
|
| Topic 6: Machine Learning | 15% | - Deep learning frameworks integration
|
NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:
A machine learning team is handling large-scale datasets that need to be efficiently stored and accessed within an NVIDIA RAPIDS workflow.
Which of the following storage formats and techniques provides the best performance for GPU-based data science pipelines?
- A. Save data in JSON format to ensure hierarchical relationships and flexibility before loading it into cuDF.
- B. Use CSV files for storage, as they are widely compatible and can be read quickly using cuDF's read_csv() method.
- C. Store data in SQLite databases and query it into pandas before converting it to cuDF.
- D. Store data in the Apache Parquet format and load it directly into cuDF using cuDF's read_parquet() method.
Correct Answer: D 🗳️
You are processing a large dataset with UNIX timestamps (seconds since Jan 1, 1970) ranging from Jan 1, 2000, to the present.
What is the most memory-efficient data type for the timestamp column in a GPU-accelerated cloud environment?
- A. df['timestamp'] = df['timestamp'].astype('int32')
- B. df['timestamp'] = df['timestamp'].astype('int64')
- C. df['timestamp'] = df['timestamp'].astype('datetime64[ms]')
- D. df['timestamp'] = df['timestamp'].astype('float32')
Correct Answer: A 🗳️
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 clip() to set a maximum and minimum threshold for numerical values
- B. Using quantile() to calculate the interquartile range (IQR) and filter out outliers
- C. Using dropna() to remove rows with outliers
- D. Using applymap() to apply a custom function for handling outliers
Correct Answer: A,B 🗳️
A machine learning engineer runs NVIDIA DLProf to analyze the performance of a deep learning model and receives a report indicating high GPU idle time.
What is the most likely cause of this issue?
- A. The CUDA cores are overheating, leading to automatic throttling of computations.
- B. The GPU is not powerful enough to process the deep learning model efficiently.
- C. The model is experiencing data loading bottlenecks, causing the GPU to wait for input batches.
- D. The batch size is too large, leading to excessive GPU memory utilization and slow processing.
Correct Answer: C 🗳️
You are working with a large dataset that contains missing values in multiple columns. Your goal is to prepare this dataset for training a machine learning model on an NVIDIA GPU using RAPIDS.
Which of the following approaches is the most efficient method to handle missing values in this scenario?
- A. Convert the dataset to a NumPy array and manually replace missing values with the mean
- B. Use fillna() with a fixed value on the GPU using cuDF
- C. Apply a deep learning-based imputation model before moving data to the GPU
- D. Drop all rows containing missing values using Pandas before transferring data to the GPU
Correct Answer: B 🗳️
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