NVIDIA NCP-ADS Questions & Answers - in .pdf
- Total Q&A: 303
- Update: Jul 25, 2026
- Price: $59.99
- Vendor: NVIDIA
- Exam Code: NCP-ADS
- Exam Name: NVIDIA-Certified-Professional Accelerated Data Science
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- Total Q&A: 303
- Update: Jul 25, 2026
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NVIDIA NCP-ADS Exam Overview:
| Certification Vendor: | NVIDIA |
| Exam Name: | NVIDIA-Certified Professional: Accelerated Data Science |
| Exam Number: | NCP-ADS |
| Exam Price: | 1580 CNY (~$200 USD) |
| Exam Format: | Multiple-choice, Scenario-based multiple-choice |
| Available Languages: | Chinese, English |
| Related Certifications: | NVIDIA-Certified Associate: Accelerated Data Science (NCA-ADS) |
| Real Exam Qty: | 60-70 |
| Certificate Validity Period: | 2 years |
| Exam Duration: | 120 minutes |
| Recommended Training: | Accelerating End-to-End Data Science Workflows (DLI) Fundamentals of Accelerated Data Science |
| Exam Registration: | NVIDIA Training & Certification Portal NVIDIA Certification Support |
| Sample Questions: | NVIDIA NCP-ADS Sample Questions |
| Exam Way: | Proctored exam (online or authorized test center depending on region) |
| Pre Condition: | 2–3 years of experience in accelerated data science, machine learning, and GPU computing |
| Official Syllabus URL: | https://www.nvidia.cn/training/certification/accelerated-data-science-professional/ |
NVIDIA NCP-ADS Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Machine Learning | 15% | - Model Development and Optimization
|
| GPU and Cloud Computing | 16% | - GPU Optimization and Infrastructure
|
| Data Manipulation and Software Literacy | 19% | - ETL and Data Processing Workflows
|
| Data Preparation | 17% | - Data Cleaning and Transformation
|
| Data Analysis | 14% | - Exploratory Data Analysis (EDA)
|
| MLOps | 19% | - Deployment and Monitoring
|
NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:
1. A machine learning engineer is working with a financial dataset that contains multiple numerical features, including income, loan amount, and transaction frequency. Some features are normally distributed, while others have a highly skewed distribution with extreme outliers.
Which of the following approaches best ensures uniformity across features before training a model?
A) Apply log transformation to skewed features before standardizing them with z-score normalization
B) Use one-hot encoding to transform numerical features into categorical representations
C) Scale all numerical features using min-max normalization
D) Remove outliers before applying standardization
2. 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) Use cuml.impute.SimpleImputer(strategy='mean') to replace missing values with the column mean.
B) cuml.impute.KNNImputer() to replace missing values based on k-nearest neighbors.
C) Replace missing values with zero before standardization.
D) Use cudf.DataFrame.fillna(method='ffill') to forward-fill missing values.
3. A data scientist is preparing a dataset containing numerical features with varying scales and distributions. Some features range from 0 to 1, while others have values ranging from -5000 to 5000. The dataset will be used in a machine learning model that relies on gradient-based optimization.
What is the best approach to standardizing the data to ensure uniformity across features?
A) Apply z-score normalization (standardization) to scale features to have a mean of 0 and a standard deviation of 1
B) Keep the features as they are since machine learning models can handle varying scales naturally
C) Normalize the dataset by dividing each feature by its maximum absolute value
D) Use min-max scaling to transform all features into the range [0,1]
4. You are working on a data science project that involves processing large-scale financial transaction data. You want to optimize data manipulation operations using NVIDIA's RAPIDS cuDF.
Which of the following approaches best leverages NVIDIA technologies for efficient data manipulation?
A) Load the dataset into a Pandas DataFrame and use multi-threading to speed up operations.
B) Use cuDF DataFrames for data manipulation and rely on GPU-accelerated functions like .groupby(),
.merge(), and .applymap().
C) Convert the dataset into a SQLite database and execute SQL queries to perform data transformations.
D) Preprocess the data using Apache Spark's CPU-based DataFrame API before transferring it to a GPU for machine learning.
5. You are setting up a deep learning model for training on a multi-GPU cluster. You want to maximize training efficiency while maintaining model convergence.
Which of the following strategies is most effective in ensuring efficient multi-GPU training?
A) Train each GPU independently on a different dataset to reduce the communication bottleneck.
B) Reduce batch size to ensure each GPU receives only a small portion of the training data.
C) Use Data Parallelism, where each GPU gets a different portion of the dataset, but gradients are averaged across all GPUs.
D) Use Model Parallelism, where different layers of the model are placed on different GPUs to reduce communication overhead.
Solutions:
| Question # 1 Answer: A | Question # 2 Answer: A | Question # 3 Answer: A | Question # 4 Answer: B | Question # 5 Answer: C |
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