Professional NCP-ADS–100% Free Latest Exam Questions Vce | NCP-ADS Pdf Dumps

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NVIDIA NCP-ADS Exam Syllabus Topics:

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

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q21-Q26):

NEW QUESTION # 21
In the context of cloud computing, what are the key benefits of using GPUs for data science tasks?
(Select two)

A. Faster parallel processing for large datasets
B. Lower cost of cloud infrastructure
C. Lower energy consumption compared to CPUs
D. Better for memory-intensive workloads
E. Efficient handling of matrix operations in machine learning models

Answer: A,E

NEW QUESTION # 22
You are working with a large dataset that contains missing values in multiple columns. Your goal is to prepare this dataset for training a machine learning model on an NVIDIA GPU using RAPIDS.
Which of the following approaches is the most efficient method to handle missing values in this scenario?

A. Apply a deep learning-based imputation model before moving data to the GPU
B. Use fillna() with a fixed value on the GPU using cuDF
C. Convert the dataset to a NumPy array and manually replace missing values with the mean
D. Drop all rows containing missing values using Pandas before transferring data to the GPU

Answer: B

NEW QUESTION # 23
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 # 24
You are designing a data pipeline for a healthcare analytics company that processes petabytes of structured and semi-structured patient data. Your goal is to optimize performance while maintaining accuracy.
Which of the following strategies is the most appropriate choice for accelerating this workload?

A. Use RAPIDS cuDF and Dask-cuDF for distributed preprocessing and RAPIDS cuML for GPU- accelerated machine learning model training.
B. Load the data into a PostgreSQL database and run SQL queries for all transformations before training a model.
C. Use RAPIDS cuDF for data ingestion, preprocessing, and transformation, then offload model training to scikit-learn on CPUs.
D. Use Apache Hadoop and MapReduce for preprocessing before training models on a single GPU.

Answer: A

NEW QUESTION # 25
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 the dask_gpu package to assign data chunks across GPUs manually, without utilizing the distributed.Client.
B. Use Dask on a single GPU machine with no GPU-specific optimization, treating the system as if it were CPU-only.
C. Use Dask with a single GPU, distributing data across multiple workers within the GPU without considering GPU memory limitations.
D. 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.

Answer: D

NEW QUESTION # 26
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