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NVIDIA NCP-ADS Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: MLOps	19%	- Pipeline automation and orchestration - Monitoring, logging and maintenance - End-to-end workflow management - Model deployment and serving
Topic 2: Data Analysis	14%	- Exploratory Data Analysis (EDA) - Time-series analysis and anomaly detection - Distributed and parallel data processing - Data visualization and graph analytics
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: Machine Learning	15%	- Distributed training strategies - Model evaluation and validation - GPU-accelerated ML frameworks and algorithms - Model training and hyperparameter tuning
Topic 5: Data Manipulation and Software Literacy	19%	- Data processing libraries selection and usage - Dependency management and containerization - GPU-accelerated ETL workflows - Performance profiling and optimization tools
Topic 6: 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

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q218-Q223):

NEW QUESTION # 218
A data scientist is analyzing a large dataset of financial transactions containing millions of records.
To efficiently perform exploratory data analysis (EDA) using RAPIDS cuDF, which approach provides the most optimized performance while ensuring comprehensive insights?

A. Use RAPIDS cuDF functions like .describe() and .value_counts() to perform statistical summaries directly on the GPU.
B. Downsample the dataset and analyze a subset using Pandas for efficiency.
C. Convert the dataset to a Pandas DataFrame for easier visualization and use .describe() to summarize statistics.
D. Perform all analysis on the CPU to avoid potential GPU memory limitations.

Answer: A

NEW QUESTION # 219
You are working with cloud-based GPUs to process a large dataset (terabytes in size) stored in Parquet format. One column represents a unique identifier (e.g., product ID), and it contains only positive integers ranging from 1 to 100,000.
Which of the following data types provides the best balance of memory efficiency and performance?

A. int8
B. uint16
C. float32
D. float64

Answer: B

NEW QUESTION # 220
You are building a large-scale AI training pipeline that requires efficient storage and retrieval of structured and unstructured datasets across multiple GPUs.
Which of the following is the best NVIDIA technology to organize and manage datasets at scale?

A. NVIDIA Nsight Systems for managing dataset storage and retrieval performance.
B. NVIDIA Clara Imaging for storing structured and unstructured datasets efficiently.
C. NVIDIA Magnum IO for high-performance I/O and dataset storage optimization.
D. NVIDIA Morpheus for accelerating dataset indexing and retrieval in AI pipelines.

Answer: C

NEW QUESTION # 221
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. Prioritizing synthetic benchmarks over real-world workloads to get a more controlled performance measurement.
C. Running benchmarks in isolated virtual machines to simulate real-world multi-tenant cloud environments.
D. Running each benchmark multiple times and averaging the results to account for variance in execution time.

Answer: D

NEW QUESTION # 222
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. Optimize GPU performance by limiting the number of threads running on each GPU.
B. Use larger batch sizes to make better use of GPU memory during model training.
C. Overclock the GPU to achieve higher computational speeds and improve training times.
D. Use a single cloud instance with the largest GPU available to ensure maximum performance.
E. Utilize multi-GPU training to parallelize the workload, reducing training time.

Answer: B,E

NEW QUESTION # 223
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