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NVIDIA NCP-ADS Exam Syllabus Topics:

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

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NCP-ADS Latest Practice Questions - Latest NCP-ADS Exam Cost

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

NEW QUESTION # 21
You are building a predictive model for retail sales forecasting and need a dataset that includes historical sales transactions, customer demographics, and external economic indicators (e.g., inflation rate, unemployment rate).
Which of the following datasets would be the most appropriate for your model?

A. A dataset of product reviews and customer sentiments from an e-commerce website
B. A dataset with global temperature trends over the past decade
C. A public dataset of annual GDP per country
D. A dataset containing transaction history and customer profiles from a retail company

Answer: D

NEW QUESTION # 22
You are working on a data processing pipeline using NVIDIA GPUs for accelerating computations.
You need to monitor the pipeline's performance to identify bottlenecks.
Which of the following tools or techniques can be used to efficiently recognize bottlenecks in such a GPU-accelerated pipeline? (Select two)

A. NVIDIA Nsight Systems
B. NVIDIA TensorRT Profiling
C. NVIDIA CUDA Profiler (nvprof)
D. NVIDIA DLA (Deep Learning Accelerator)
E. NVIDIA nvidia-smi

Answer: A,C

NEW QUESTION # 23
A machine learning engineer is working on a multi-GPU workload using Dask and RAPIDS to process a massive dataset efficiently. However, they notice that GPU utilization is not optimal, and data transfer between GPUs is slowing down computation.
What is the best approach to minimize data transfer overhead and maximize parallel efficiency?

A. Use Dask's single-threaded scheduler to avoid multi-threading overhead
B. Load the dataset into a Pandas DataFrame and distribute computations across GPUs manually
C. Use NVLink to enable high-bandwidth inter-GPU communication and dask_cuda.LocalCUDACluster
D. Transfer data between GPUs using NumPy's .copy() function

Answer: C

NEW QUESTION # 24
You are designing an ETL workflow to process large-scale financial transaction data using GPU acceleration. The dataset is stored in a Parquet file and contains millions of records.
Which of the following approaches is the most efficient for performing extract, transform, and load (ETL) operations using NVIDIA RAPIDS technologies?

A. Load the Parquet file directly into a cuDF DataFrame and use cuDF's built-in functions for transformations before writing the results back to storage.
B. Use Apache Spark with CPU-based processing for ETL, then convert the results into cuDF for accelerated analytics.
C. Use Pandas DataFrame for transformation, and then convert the dataset to cuDF before writing to storage.
D. Store all data as CSV files and perform ETL operations using traditional row-based processing.

Answer: A

NEW QUESTION # 25
You are processing a large dataset in a distributed computing environment using RAPIDS and Dask.
Your workflow involves frequent shuffling of data between partitions, leading to significant slowdowns.
Which of the following strategies is the best way to implement data caching to reduce shuffle overhead using NVIDIA technologies?

A. Disable caching altogether to force a recomputation of results, ensuring up-to-date data processing.
B. Enable GPU-accelerated caching with RAPIDS cuDF and persist intermediate results in GPU memory.
C. Use a CPU-based caching solution like Memcached to store intermediate data before reloading into cuDF.
D. Use traditional disk-based caching by writing intermediate results to CSV files and reloading when needed.

Answer: B

NEW QUESTION # 26
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