NVIDIA-Certified-Professional Accelerated Data Science exam dumps & NCP-ADS practice torrent & NVIDIA-Certified-Professional Accelerated Data Science training vces

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NVIDIA NCP-ADS Exam Syllabus Topics:

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

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q46-Q51):

NEW QUESTION # 46
A data scientist wants to process a large dataset using multiple GPUs on an NVIDIA-supported system. They decide to use Dask to enable parallelism.
Which of the following steps is most essential for leveraging Dask for multi-GPU scaling?

A. Train a deep learning model on a single GPU first, then switch to Dask for scaling
B. Use ThreadPoolExecutor to manage parallel computations on GPUs
C. Use dask.array or dask.dataframe with dask_cuda.CUDACluster to distribute computations across multiple GPUs
D. Convert all data into Pandas DataFrames before distributing computations

Answer: C

NEW QUESTION # 47
Which of the following is the main advantage of using TensorRT for inference in an accelerated data science pipeline?

A. TensorRT automatically builds training models from raw data without requiring pre-trained models.
B. TensorRT is only compatible with image classification models and does not support other model types.
C. TensorRT optimizes deep learning models to run efficiently on NVIDIA GPUs by reducing precision while maintaining accuracy.
D. TensorRT is mainly used for data visualization and not for model inference.

Answer: C

NEW QUESTION # 48
You need to benchmark GPU-accelerated data science frameworks across both cloud-based and on-premise GPU setups.
Which of the following is the most effective strategy for ensuring consistent and reliable benchmarking results?

A. Using mixed cloud and on-premise GPU instances to test various hardware types and configurations.
B. Running benchmarks in isolated virtual machines to simulate real-world multi-tenant cloud environments.
C. Prioritizing synthetic benchmarks over real-world workloads to get a more controlled performance measurement.
D. Running each benchmark multiple times and averaging the results to account for variance in execution time.

Answer: D

NEW QUESTION # 49
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 quantile() to calculate the interquartile range (IQR) and filter out outliers
B. Using applymap() to apply a custom function for handling outliers
C. Using clip() to set a maximum and minimum threshold for numerical values
D. Using dropna() to remove rows with outliers

Answer: A,C

NEW QUESTION # 50
You are working with a dataset containing billions of records stored in a Parquet file. You need to load this dataset efficiently into an NVIDIA-accelerated RAPIDS environment for feature engineering.
Which of the following is the best approach?

A. Convert the dataset into a CSV format and use cudf.read_csv() to load it into RAPIDS
B. Load the Parquet file directly into a cuDF DataFrame using cudf.read_parquet()
C. Use pandas.read_parquet() to load the dataset and then convert it to a cuDF DataFrame
D. Load the Parquet file into Dask and then convert it into a cuDF DataFrame for parallel processing

Answer: B

NEW QUESTION # 51
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