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| Section | Weight | Objectives |
|---|
| Topic 1: Data Manipulation and Software Literacy | 19% | - Performance profiling and optimization tools - GPU-accelerated ETL workflows - Data processing libraries selection and usage - Dependency management and containerization
|
| Topic 2: GPU and Cloud Computing | 16% | - Resource management and scaling strategies - GPU architecture and acceleration principles - Cloud GPU environments and deployment - CRISP-DM and data science methodology
|
| Topic 3: Data Analysis | 14% | - Exploratory Data Analysis (EDA) - Distributed and parallel data processing - Data visualization and graph analytics - Time-series analysis and anomaly detection
|
| Topic 4: MLOps | 19% | - Monitoring, logging and maintenance - Model deployment and serving - End-to-end workflow management - Pipeline automation and orchestration
|
| Topic 5: Machine Learning | 15% | - Model evaluation and validation - GPU-accelerated ML frameworks and algorithms - Distributed training strategies - Model training and hyperparameter tuning
|
| Topic 6: Data Preparation | 17% | - Data cleaning, preprocessing and transformation - Feature engineering and data type optimization - Data validation and quality assurance - Workflow monitoring and bottleneck identification
|
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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q99-Q104):
NEW QUESTION # 99
Which of the following can DLProf specifically help identify when profiling a deep learning model on Nvidia GPUs?
- A. Training dataset bias.
- B. Number of model parameters.
- C. Hyperparameter tuning results.
- D. GPU utilization and memory usage.
Answer: D
NEW QUESTION # 100
You are implementing a GPU-accelerated ETL pipeline that involves joining two large datasets:
Dataset A: A cuDF DataFrame with 10 million customer records.
Dataset B: A cuDF DataFrame with 100 million transaction records.
The goal is to efficiently perform a join operation to link customer details with transaction data, ensuring that the pipeline remains scalable and performant.
Which of the following is the best approach to optimize the join operation using NVIDIA RAPIDS?
- A. Use cuDF's .merge() function and ensure both DataFrames have the correct index before joining
- B. Store the transaction data as a CSV file and perform joins using SQL queries before loading it into cuDF
- C. Perform the join operation entirely in a CPU-based Spark environment for better stability
- D. Convert both DataFrames to Pandas before performing the join for compatibility
Answer: A
NEW QUESTION # 101
You are selecting a dataset for GPU-accelerated data science using NVIDIA RAPIDS.
Which dataset characteristic is most suitable for taking advantage of GPU acceleration?
- A. A small dataset with fewer than 1,000 rows stored in an Excel file
- B. A dataset stored as an SQLite database file
- C. A dataset consisting of unstructured text stored as plain .txt files
- D. A large, structured dataset stored in a Parquet format
Answer: D
NEW QUESTION # 102
You are working on a large-scale graph analysis project using NVIDIA cuGraph for accelerated computations. Your dataset consists of millions of nodes and edges representing social network interactions. You need to efficiently compute PageRank while minimizing memory usage.
Which of the following techniques would be the most effective?
- A. Convert the graph into an adjacency list format and store it in Python dictionaries for faster lookups.
- B. Use Pandas DataFrames to manage the graph structure and leverage Pandas indexing for efficient queries.
- C. Use cuGraph's sparse matrix representation to store graph data and perform computations efficiently.
- D. Manually unroll loops and precompute node ranks in a NumPy array before feeding them into cuGraph.
Answer: C
NEW QUESTION # 103
You are designing a data pipeline for a healthcare analytics company that processes petabytes of structured and semi-structured patient data. Your goal is to optimize performance while maintaining accuracy.
Which of the following strategies is the most appropriate choice for accelerating this workload?
- A. Use Apache Hadoop and MapReduce for preprocessing before training models on a single GPU.
- B. Load the data into a PostgreSQL database and run SQL queries for all transformations before training a model.
- C. Use RAPIDS cuDF for data ingestion, preprocessing, and transformation, then offload model training to scikit-learn on CPUs.
- D. Use RAPIDS cuDF and Dask-cuDF for distributed preprocessing and RAPIDS cuML for GPU- accelerated machine learning model training.
Answer: D
NEW QUESTION # 104
......
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