Exam Name: NVIDIA-Certified-Professional Accelerated Data Science
Certification Provider: NVIDIA
Corresponding Certification: NVIDIA-Certified Professional
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NVIDIA : NCP-ADS Exam Premium Dumps
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NVIDIA NCP-ADS Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Machine Learning | 15% | - Model evaluation and validation - Model training and hyperparameter tuning - GPU-accelerated ML frameworks and algorithms - Distributed training strategies |
| Data Preparation | 17% | - Workflow monitoring and bottleneck identification - Data cleaning, preprocessing and transformation - Feature engineering and data type optimization - Data validation and quality assurance |
| MLOps | 19% | - Model deployment and serving - End-to-end workflow management - Monitoring, logging and maintenance - Pipeline automation and orchestration |
| GPU and Cloud Computing | 16% | - GPU architecture and acceleration principles - Cloud GPU environments and deployment - Resource management and scaling strategies - CRISP-DM and data science methodology |
| Data Manipulation and Software Literacy | 19% | - Performance profiling and optimization tools - Dependency management and containerization - Data processing libraries selection and usage - GPU-accelerated ETL workflows |
| Data Analysis | 14% | - Distributed and parallel data processing - Time-series analysis and anomaly detection - Exploratory Data Analysis (EDA) - Data visualization and graph analytics |
NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:
You are performing data cleansing on a large dataset using CuDF. The dataset contains numerical values, some of which are outliers. You need to remove or adjust these outliers to make your model training more robust.
Which of the following approaches should you consider for handling outliers efficiently in CuDF? (Select two)
- A. Using clip() to set a maximum and minimum threshold for numerical values
- B. Using dropna() to remove rows with outliers
- C. Using quantile() to calculate the interquartile range (IQR) and filter out outliers
- D. Using applymap() to apply a custom function for handling outliers
Correct Answer: A,C 🗳️
When scaling a distributed data processing framework using NVIDIA GPU technology for big data processing, which of the following factors is most critical to optimize performance?
- A. Limiting the number of GPU nodes used in the cluster to avoid complexity.
- B. Using more CPU cores to handle computation-heavy tasks.
- C. Maximizing the amount of data transferred between GPUs for faster processing.
- D. Ensuring the proper configuration of GPU resources across all nodes in the distributed system.
Correct Answer: D 🗳️
You are tasked with optimizing an ETL pipeline that processes petabytes of data daily. Your organization is already using Apache Spark for distributed data processing but is experiencing performance bottlenecks. You need a solution that improves execution speed without requiring extensive code modifications.
Which of the following solutions best meets your needs?
- A. Using the NVIDIA Spark RAPIDS Accelerator to run Spark workloads on GPUs
- B. Using Apache Arrow to optimize in-memory columnar processing within Spark
- C. Switching to Dask and RAPIDS to fully utilize GPU acceleration
- D. Rewriting the entire ETL pipeline in CUDA to maximize GPU efficiency
Correct Answer: A 🗳️
You have a pandas DataFrame with a column containing floating-point numbers, but it takes up too much memory. You want to convert it into a lower-precision type using CuDF or pandas while ensuring computational efficiency.
Which function would you use?
- A. df.convert_dtypes()
- B. df.astype('float16')
- C. df['col'].apply(lambda x: np.float16(x))
- D. df.to_float16()
Correct Answer: B 🗳️
You have deployed a deep learning model for image classification in a production environment, but inference latency is high. You need to optimize the model to reduce response time while maintaining accuracy.
Which NVIDIA technology is best suited for this task?
- A. NVIDIA Clara Imaging to improve deep learning inference for image classification workloads.
- B. NVIDIA DeepStream to process image classification models for low-latency inference in batch mode.
- C. NVIDIA RAPIDS cuML to optimize deep learning inference using GPU-accelerated ML algorithms.
- D. NVIDIA TensorRT to optimize and accelerate deep learning inference by reducing model size and execution time.
Correct Answer: D 🗳️
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