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NVIDIA NCP-ADS Exam Syllabus Topics:

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

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q81-Q86):

NEW QUESTION # 81
A data scientist is working with datasets ranging from hundreds of megabytes to several terabytes and needs to select the most efficient NVIDIA-accelerated data processing library for optimal memory management and performance.
Which approach is best for selecting the appropriate library for different dataset sizes?

A. Use RAPIDS cuML for handling all data processing tasks, regardless of dataset size.
B. Always use Dask regardless of dataset size since Dask automatically scales from small to large datasets.
C. Use Pandas for all dataset sizes since Pandas has built-in multi-threading optimizations.
D. Use cuDF for small datasets and Dask-cuDF for larger datasets that do not fit in a single GPU's memory.

Answer: D

NEW QUESTION # 82
You are working on a large dataset for a machine learning model that will be trained using RAPIDS cuML. The dataset includes categorical, integer, and floating-point features.
Which of the following approaches is the best practice for determining the optimal data type choice for each feature using NVIDIA's RAPIDS cuDF library?

A. Use float16 for all floating-point data to reduce memory usage and increase GPU processing speed.
B. Convert all numerical data to float64 for maximum precision in calculations.
C. Convert categorical variables into int8 to optimize GPU memory usage.
D. Use float32 instead of float64 for floating-point numbers when possible, and leverage int8, int16, or int32 for categorical and integer data based on their range.

Answer: D

NEW QUESTION # 83
Which of the following is the most appropriate way to perform large-scale data processing in a GPU- accelerated environment using NVIDIA RAPIDS?

A. Use Dask on top of RAPIDS for distributed computing across multiple GPUs.
B. Use pandas for all data manipulations and rely on multi-threading for parallel execution.
C. Use NumPy exclusively for processing large datasets on GPUs.
D. Use TensorFlow for all data manipulations in a GPU environment.

Answer: A

NEW QUESTION # 84
You are working on a data science project using NVIDIA RAPIDS on a multi-GPU system.
To ensure reproducibility and avoid software versioning conflicts, which of the following is the best approach for managing dependencies?

A. Install all required packages globally on the system using pip install without a virtual environment.
B. Use a Conda environment with RAPIDS-compatible versions of libraries installed using conda install
-c rapidsai -c nvidia.
C. Avoid dependency management frameworks and rely on manual tracking of package versions using a text file.
D. Use a manually compiled CUDA installation alongside system-installed Python libraries to manage GPU dependencies.

Answer: B

NEW QUESTION # 85
You are tasked with optimizing a data science workflow to scale across multiple GPUs using Dask.
Which of the following approaches would be most effective for implementing data parallelism in this scenario? (Select two)

A. Use Dask's distributed scheduler along with dask_cuda to handle GPU resources for parallel processing
B. Use dask_cuda's CUDACluster to manage the multi-GPU setup and parallelize computations
C. Manually partition the dataset and assign each partition to a specific GPU using dask.delayed
D. Run the computation on a single GPU and let Dask handle CPU parallelism automatically
E. Use Dask's default scheduler to distribute work across GPUs without any specific configuration

Answer: A,B

NEW QUESTION # 86
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