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NVIDIA NCP-ADS Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Preparation | 17% | - Feature engineering
|
| Topic 2: GPU and Cloud Computing | 16% | - GPU architecture and fundamentals
|
| Topic 3: MLOps | 19% | - Model monitoring and management
|
| Topic 4: Data Analysis | 14% | - Exploratory data analysis
|
| Topic 5: Data Manipulation and Software Literacy | 19% | - GPU-accelerated data manipulation using cuDF
|
| Topic 6: Machine Learning | 15% | - Feature engineering and hyperparameter tuning
|
NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:
Question #1
When deciding whether to use GPU acceleration or a traditional CPU approach for a machine learning task, which of the following factors should be considered to determine if the data qualifies as "big data" and whether GPU acceleration is beneficial? (Select two)
A. The complexity of the algorithm being used plays a crucial role in deciding whether to use GPU acceleration, with more complex algorithms benefiting from parallel computation.
B. GPU acceleration is beneficial when the dataset can be divided into independent chunks that can be processed in parallel.
C. The size of the dataset in terms of rows and columns is irrelevant when determining if it qualifies as big data.
D. The dataset must be over 100GB in size to qualify as big data and warrant GPU acceleration.
E. CPU-based machine learning methods are always more effective for small datasets, regardless of the algorithm used.
Question #2
You are working on a large-scale social network analysis project using cuGraph. Your goal is to identify the most influential individuals in the network based on their connectivity and reach.
Which of the following cuGraph algorithms would be the most appropriate choice for this task?
A. Louvain Clustering
B. PageRank
C. Connected Components
D. Single Source Shortest Path (SSSP)
Question #3
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. TensorFlow
B. RAPIDS
C. CUDA
D. TensorRT
Question #4
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 cudf.DataFrame.fillna(method='ffill') to forward-fill missing values.
B. Use cuml.impute.SimpleImputer(strategy='mean') to replace missing values with the column mean.
C. Replace missing values with zero before standardization.
D. cuml.impute.KNNImputer() to replace missing values based on k-nearest neighbors.
Question #5
You are consulting for a retail company that collects data from daily sales transactions, customer interactions, and inventory tracking across multiple locations. They are unsure whether their dataset qualifies as big data and which processing method would be most suitable.
Which of the following characteristics best indicate that the dataset requires big data processing and acceleration techniques?
A. The dataset includes a mix of numerical and categorical variables, requiring additional preprocessing
B. The dataset contains more than 1 million records, making it impossible to process using pandas
C. The dataset exceeds the memory capacity of a single machine and requires distributed or GPU- accelerated processing
D. The dataset is stored in multiple relational database tables, making querying inefficient
Solutions:
| Question #1 Correct Answer: A,B | Question #2 Correct Answer: B | Question #3 Correct Answer: B | Question #4 Correct Answer: B | Question #5 Correct Answer: C |
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