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NVIDIA NCP-ADS Exam Syllabus Topics:

SectionWeightObjectives
Data Analysis14%- Distributed and parallel data processing
- Data visualization and graph analytics
- Time-series analysis and anomaly detection
- Exploratory Data Analysis (EDA)
GPU and Cloud Computing16%- CRISP-DM and data science methodology
- GPU architecture and acceleration principles
- Cloud GPU environments and deployment
- Resource management and scaling strategies
MLOps19%- Pipeline automation and orchestration
- Monitoring, logging and maintenance
- End-to-end workflow management
- Model deployment and serving
Data Preparation17%- Feature engineering and data type optimization
- Data cleaning, preprocessing and transformation
- Data validation and quality assurance
- Workflow monitoring and bottleneck identification
Data Manipulation and Software Literacy19%- GPU-accelerated ETL workflows
- Performance profiling and optimization tools
- Data processing libraries selection and usage
- Dependency management and containerization
Machine Learning15%- GPU-accelerated ML frameworks and algorithms
- Model training and hyperparameter tuning
- Distributed training strategies
- Model evaluation and validation

NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

A data scientist is deploying a deep learning model to production using an NVIDIA GPU-powered inference server. They want to ensure low latency and high throughput while maintaining model accuracy.
Which of the following deployment strategies would be most effective?

  • A. Using NVIDIA Triton Inference Server with model ensemble optimization
  • B. Deploying a model without batching to ensure real-time responses
  • C. Disabling mixed-precision inference to avoid accuracy loss
  • D. Running inference on a CPU instead of a GPU to reduce power consumption
Reveal Solution  Discussion  0

Correct Answer: A  🗳️

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 assigns equal importance to all nodes in the graph initially and updates values only based on outgoing edges, ignoring incoming edges.
  • C. 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.
  • D. PageRank in cuGraph is a label propagation algorithm that clusters nodes into communities rather than ranking their importance.
Reveal Solution  Discussion  0

Correct Answer: C  🗳️

You are a data scientist working with a large dataset containing millions of records. You want to perform exploratory data analysis (EDA) efficiently using NVIDIA RAPIDS on a GPU-accelerated system.
Which of the following approaches is the most efficient way to handle large-scale EDA using RAPIDS?

  • A. Use Pandas directly for data manipulation and visualization.
  • B. Convert the dataset into a cuDF DataFrame and perform operations like .describe() and
    .value_counts() on the GPU.
  • C. Perform all EDA using NumPy and SciPy for optimized array computations.
  • D. Load the dataset into an Apache Spark DataFrame and run .show() to inspect the data.
Reveal Solution  Discussion  0

Correct Answer: B  🗳️

In the context of cloud computing, what are the key benefits of using GPUs for data science tasks?
(Select two)

  • A. Efficient handling of matrix operations in machine learning models
  • B. Better for memory-intensive workloads
  • C. Lower cost of cloud infrastructure
  • D. Lower energy consumption compared to CPUs
  • E. Faster parallel processing for large datasets
Reveal Solution  Discussion  0

Correct Answer: A,E  🗳️

You are processing a dataset with billions of records and want to encode a categorical column efficiently using NVIDIA RAPIDS.
Which of the following methods correctly encodes categorical data using cuDF?

  • A. df['category_column'] = LabelEncoder().fit_transform(df['category_column'])
  • B. df['category_column'] = df['category_column'].one_hot_encode()
  • C. df['category_column'] = df['category_column'].astype('category')
  • D. df['category_column'] = df['category_column'].apply(lambda x: hash(x) % 1000)
Reveal Solution  Discussion  0

Correct Answer: C  🗳️

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