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NVIDIA NCP-ADS Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| MLOps | 19% | - End-to-end workflow management - Monitoring, logging and maintenance - Pipeline automation and orchestration - Model deployment and serving |
| GPU and Cloud Computing | 16% | - Resource management and scaling strategies - CRISP-DM and data science methodology - GPU architecture and acceleration principles - Cloud GPU environments and deployment |
| Machine Learning | 15% | - Model training and hyperparameter tuning - Distributed training strategies - Model evaluation and validation - GPU-accelerated ML frameworks and algorithms |
| Data Analysis | 14% | - Exploratory Data Analysis (EDA) - Time-series analysis and anomaly detection - Data visualization and graph analytics - Distributed and parallel data processing |
| Data Manipulation and Software Literacy | 19% | - GPU-accelerated ETL workflows - Data processing libraries selection and usage - Dependency management and containerization - Performance profiling and optimization tools |
| Data Preparation | 17% | - Feature engineering and data type optimization - Data validation and quality assurance - Workflow monitoring and bottleneck identification - Data cleaning, preprocessing and transformation |
NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:
You are tasked with processing a large dataset of 100 million records for a deep learning project using NVIDIA technologies. You need to determine the most efficient data processing library for this task to maximize performance and reduce processing time.
Which of the following libraries is best suited for this task?
- A. pandas
- B. cuDF
- C. PySpark
- D. Dask
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. Load the dataset into an Apache Spark DataFrame and run .show() to inspect the data.
- B. Convert the dataset into a cuDF DataFrame and perform operations like .describe() and
.value_counts() on the GPU. - C. Use Pandas directly for data manipulation and visualization.
- D. Perform all EDA using NumPy and SciPy for optimized array computations.
A data scientist is training a deep learning model on an NVIDIA GPU and wants to profile the model to identify performance bottlenecks. The scientist chooses to use NVIDIA DLProf.
Which of the following steps is the most effective way to profile the model using DLProf?
- A. Rely on general CPU profiling tools like perf and gprof to analyze GPU performance.
- B. Run the model using dlprof --mode profile to collect performance metrics and generate a report.
- C. Use nvprof instead of DLProf since it provides more detailed profiling for deep learning workloads.
- D. Modify the training script to manually insert timing functions for each layer and compare execution times.
You are training a machine learning model using RAPIDS cuML and need to ensure that all numeric features are standardized for better model performance.
Which of the following is the best approach for scaling data using RAPIDS?
- A. scaler = cuml.preprocessing.StandardScaler()
- B. df_scaled = df.apply(lambda x: x / np.linalg.norm(x))
- C. df_scaled = df / df.max()
- D. df_scaled = (df - df.min()) / (df.max() - df.min())
- E. df_scaled = scaler.fit_transform(df)
You are processing a multi-terabyte dataset in CuDF and want to optimize query performance and storage efficiency.
Which approach should you follow to ensure that the dataset remains efficiently partitioned and easily accessible?
- A. Store the entire dataset in a single large file to minimize the number of files in the directory.
- B. Use pandas for large-scale processing instead of CuDF since pandas is more stable for big data processing.
- C. Convert all numeric columns to float64 for higher precision, even if float32 is sufficient.
- D. Split the dataset into multiple files based on a logical partitioning key, such as a date column.
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