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NVIDIA NCP-ADS Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: GPU and Cloud Computing | 16% | - Resource management and scaling strategies - Cloud GPU environments and deployment - GPU architecture and acceleration principles - CRISP-DM and data science methodology |
| Topic 2: Data Manipulation and Software Literacy | 19% | - Dependency management and containerization - Performance profiling and optimization tools - GPU-accelerated ETL workflows - Data processing libraries selection and usage |
| Topic 3: Data Preparation | 17% | - Data validation and quality assurance - Data cleaning, preprocessing and transformation - Workflow monitoring and bottleneck identification - Feature engineering and data type optimization |
| Topic 4: Data Analysis | 14% | - Distributed and parallel data processing - Time-series analysis and anomaly detection - Data visualization and graph analytics - Exploratory Data Analysis (EDA) |
| Topic 5: MLOps | 19% | - Model deployment and serving - Monitoring, logging and maintenance - Pipeline automation and orchestration - End-to-end workflow management |
| Topic 6: Machine Learning | 15% | - Distributed training strategies - Model evaluation and validation - Model training and hyperparameter tuning - GPU-accelerated ML frameworks and algorithms |
NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:
1. A data scientist is working with a large dataset that contains string-based numeric values that need to be converted to floating-point numbers for further analysis. The dataset is stored as a cuDF DataFrame, and the scientist needs to ensure the conversion is performed optimally on a GPU.
Which of the following is the best method for converting string-based numeric values to floating-point numbers using NVIDIA-accelerated processing?
A) Convert the cuDF DataFrame to a Pandas DataFrame first, then apply astype(float) and convert it back to cuDF.
B) Use NumPy's astype(float) method after converting the cuDF DataFrame into a NumPy array.
C) Use cudf.DataFrame.astype(float) to convert string values to floating-point numbers efficiently on a GPU.
D) Use pandas.to_numeric() since pandas automatically handles type conversion.
2. A machine learning engineer is working with a 1 TB dataset stored in Apache Parquet format and wants to analyze the data for patterns before building a model. The engineer is considering various acceleration methods.
Which of the following approaches would be the best choice for efficient analysis?
A) Use a GPU-accelerated library such as RAPIDS cuDF to load and process the Parquet file efficiently.
B) Convert the Parquet file to a Pandas DataFrame and perform analysis using Pandas functions.
C) Load the dataset into a relational database and query it using simple SQL statements.
D) Read the Parquet file line by line using Python's built-in file handling functions to save memory.
3. You are working on a financial dataset that tracks stock prices over time, and you need to detect anomalies such as sudden spikes or drops using NVIDIA technologies.
Which of the following approaches would be the most effective for anomaly detection in a time-series dataset using NVIDIA's RAPIDS AI and TensorRT?
A) Perform anomaly detection by applying DBSCAN clustering with RAPIDS cuML without any feature engineering.
B) Use traditional ARIMA modeling with RAPIDS cuML to classify anomalies based on residual analysis.
C) Apply a traditional rule-based thresholding method using pandas and NumPy for detecting sudden spikes in stock prices.
D) Use RAPIDS cuML's Isolation Forest for anomaly detection and deploy it with NVIDIA Triton Inference Server.
4. A data engineer is preparing a dataset for training a deep learning model. The dataset contains numerical features with missing values, outliers, and inconsistent units.
Which of the following strategies is the most appropriate for ensuring a standardized and clean dataset?
A) Remove all rows with missing values and outliers to ensure only clean data is used.
B) Use the median to fill missing values, convert all numerical values into categorical bins, and apply Min-Max scaling.
C) Standardize the dataset using the mean and standard deviation, but keep missing values and outliers unchanged to avoid data manipulation.
D) Replace missing values with the mean, apply z-score normalization, and clip extreme outliers based on a threshold (e.g., 3 standard deviations).
5. When using cuDF from NVIDIA RAPIDS for GPU-accelerated data manipulation, which of the following operations will not efficiently utilize the GPU?
A) Applying a Python lambda function row-wise to a DataFrame.
B) Merging two large datasets on a common column.
C) Filtering a large dataset based on a condition (e.g., column > 1000).
D) Performing a group-by operation followed by an aggregation.
Solutions:
| Question # 1 Answer: C | Question # 2 Answer: A | Question # 3 Answer: D | Question # 4 Answer: D | Question # 5 Answer: A |
