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NVIDIA NCP-ADS Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Machine Learning	15%	- Distributed training strategies - GPU-accelerated ML frameworks and algorithms - Model evaluation and validation - Model training and hyperparameter tuning
Topic 2: Data Preparation	17%	- Workflow monitoring and bottleneck identification - Feature engineering and data type optimization - Data validation and quality assurance - Data cleaning, preprocessing and transformation
Topic 3: GPU and Cloud Computing	16%	- Cloud GPU environments and deployment - CRISP-DM and data science methodology - GPU architecture and acceleration principles - Resource management and scaling strategies
Topic 4: MLOps	19%	- Model deployment and serving - Pipeline automation and orchestration - End-to-end workflow management - Monitoring, logging and maintenance
Topic 5: Data Manipulation and Software Literacy	19%	- GPU-accelerated ETL workflows - Data processing libraries selection and usage - Performance profiling and optimization tools - Dependency management and containerization
Topic 6: Data Analysis	14%	- Distributed and parallel data processing - Exploratory Data Analysis (EDA) - Data visualization and graph analytics - Time-series analysis and anomaly detection

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q290-Q295):

NEW QUESTION # 290
You are tasked with optimizing the performance of a large-scale data science project that involves deep learning models on a cloud infrastructure. Your organization is using GPUs for model training.
Which of the following strategies would be the most effective in optimizing GPU performance for data science tasks? (Select two)

A. Overclock the GPU to achieve higher computational speeds and improve training times.
B. Utilize multi-GPU training to parallelize the workload, reducing training time.
C. Use a single cloud instance with the largest GPU available to ensure maximum performance.
D. Use larger batch sizes to make better use of GPU memory during model training.
E. Optimize GPU performance by limiting the number of threads running on each GPU.

Answer: B,D

NEW QUESTION # 291
You are a data scientist working on a large-scale deep learning project that requires significant computational resources. You have the option to run your workloads on a cloud-based GPU instance.
Which of the following statements best describes a key benefit of using cloud-based GPUs for your workload?

A. Cloud-based GPUs provide consistent and predictable performance, identical to on-premise dedicated GPUs.
B. Cloud-based GPUs are always more cost-effective than on-premise GPUs, regardless of workload size and duration.
C. Cloud-based GPUs enable scalable resource allocation, allowing you to dynamically increase or decrease GPU instances as needed.
D. Cloud-based GPUs eliminate all data transfer bottlenecks and latencies when training models on large datasets.

Answer: C

NEW QUESTION # 292
You are preprocessing a dataset using NVIDIA RAPIDS cuDF and need to handle missing values in the column temperature by replacing them with the column's median value.
Which of the following approaches correctly achieves this in an optimized manner?

A. df['temperature'].fillna(df['temperature'].mean(), inplace=True)
B. df['temperature'].fillna(df['temperature'].median(), inplace=True)
C. 1. df['temperature'] = df['temperature'].map(2. lambda x: df['temperature'].median() if x is None else x
3.)
D. df['temperature'].dropna(inplace=True)

Answer: B

NEW QUESTION # 293
You are setting up a GPU-accelerated data science environment that includes NVIDIA RAPIDS, PyTorch, TensorFlow, and other libraries for machine learning and data processing.
Given that these frameworks have different dependencies and version requirements, what is the best approach to avoid software conflicts while ensuring reproducibility across multiple environments?

A. Install all packages globally using pip on the system-wide Python installation to ensure consistency.
B. Use a single Docker container with the latest versions of all dependencies installed system-wide.
C. Use Conda to create isolated virtual environments for each project and install dependencies via conda-forge or NVIDIA channels.
D. Manually download and compile each library from source to guarantee compatibility across all versions.

Answer: C

NEW QUESTION # 294
Which of the following is the most efficient way to implement data parallelism using Dask for multi- GPU scaling on an Nvidia platform?

A. Use Dask with a single GPU, distributing data across multiple workers within the GPU without considering GPU memory limitations.
B. Use Dask with the dask_gpu package to assign data chunks across GPUs manually, without utilizing the distributed.Client.
C. Use Dask with the dask_cuda package to distribute computation across GPUs, ensuring that each GPU is responsible for a portion of the data, while using distributed.Client to connect the GPU workers.
D. Use Dask on a single GPU machine with no GPU-specific optimization, treating the system as if it were CPU-only.

Answer: C

NEW QUESTION # 295
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