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NVIDIA NCP-ADS Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: MLOps | 19% | - Deployment and Monitoring
|
| Topic 2: GPU and Cloud Computing | 16% | - GPU Optimization and Infrastructure
|
| Topic 3: Machine Learning | 15% | - Model Development and Optimization
|
| Topic 4: Data Preparation | 17% | - Data Cleaning and Transformation
|
| Topic 5: Data Manipulation and Software Literacy | 19% | - ETL and Data Processing Workflows
|
| Topic 6: Data Analysis | 14% | - Exploratory Data Analysis (EDA)
|
NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:
1. You are a data scientist analyzing a social media network with NVIDIA cuGraph to identify the most influential users using the PageRank algorithm.
Which option best describes how cuGraph PageRank operates on a directed graph?
A) PageRank in cuGraph operates only on undirected graphs and cannot be applied to networks where edges have a direction.
B) PageRank in cuGraph uses an iterative power method to update node importance values based on incoming edges, incorporating a damping factor to handle random jumps.
C) PageRank assigns equal importance to all nodes in the graph initially and updates values only based on outgoing edges, ignoring incoming edges.
D) PageRank in cuGraph is a label propagation algorithm that clusters nodes into communities rather than ranking their importance.
2. You are training a deep learning model on a large dataset. Initially, you train the model on a single GPU and achieve a training time of 10 hours. To speed up training, you switch to a multi-GPU setup with four GPUs. However, after testing, you notice that the training time is only reduced to 3.5 hours instead of the expected 2.5 hours (a linear speedup).
What is the most likely reason for this sublinear speedup?
A) The optimizer is not designed for multi-GPU training, causing inefficiency.
B) Data transfer and communication overhead between GPUs limit scalability.
C) Increased memory bandwidth usage causes a bottleneck in the system.
D) The learning rate is too low, causing slower convergence despite multiple GPUs.
3. A retail company is deploying an AI-driven demand forecasting system using NVIDIA GPUs. The team follows the CRISP-DM framework and is currently in the Evaluation phase.
Which approach best leverages NVIDIA technologies to assess model performance effectively?
A) Perform evaluation on a small CPU-based subset of the dataset instead of using full GPU-accelerated inference.
B) Rely only on training loss as the primary evaluation metric without considering validation performance.
C) Assume that a high training accuracy guarantees excellent real-world performance, skipping the evaluation phase.
D) Use RAPIDS cuML to rapidly compute evaluation metrics like RMSE and R-squared on large datasets using GPUs.
4. You are designing an ETL workflow to process large-scale financial transaction data using GPU acceleration. The dataset is stored in a Parquet file and contains millions of records.
Which of the following approaches is the most efficient for performing extract, transform, and load (ETL) operations using NVIDIA RAPIDS technologies?
A) Use Apache Spark with CPU-based processing for ETL, then convert the results into cuDF for accelerated analytics.
B) Load the Parquet file directly into a cuDF DataFrame and use cuDF's built-in functions for transformations before writing the results back to storage.
C) Use Pandas DataFrame for transformation, and then convert the dataset to cuDF before writing to storage.
D) Store all data as CSV files and perform ETL operations using traditional row-based processing.
5. You are developing an accelerated ETL workflow that requires data transformations such as filtering, aggregating, and joining large datasets. You decide to leverage NVIDIA GPUs to accelerate the transformation phase of your ETL pipeline.
Which of the following approaches will provide the greatest performance improvements when working with large-scale tabular datasets?
A) Relying on traditional pandas for in-memory transformations
B) Using RAPIDS cuDF to perform transformations on a GPU
C) Performing transformations using SQL-based queries on CPU
D) Using TensorFlow for data transformation tasks
Solutions:
| Question # 1 Answer: B | Question # 2 Answer: B | Question # 3 Answer: D | Question # 4 Answer: B | Question # 5 Answer: B |
