NCP-ADS Latest Test Vce | NCP-ADS Formal Test

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NVIDIA NCP-ADS Exam Syllabus Topics:

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

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q106-Q111):

NEW QUESTION # 106
You are tasked with optimizing the performance of an MLOps pipeline that uses GPU-accelerated workflows. After running initial benchmarks, you notice that the training time is higher than expected, despite the use of multiple GPUs.
What are the best strategies to optimize the GPU-accelerated workflow in this case? (Select two)

A. Ensure that the model is distributed evenly across GPUs to prevent some GPUs from being underutilized.
B. Reduce the number of GPUs used and focus on fine-tuning the hyperparameters for optimal performance on a single GPU.
C. Ensure efficient multi-GPU communication and synchronization strategies, such as using NCCL for distributed training.
D. Increase the batch size to better utilize the multiple GPUs and reduce the number of updates to the model during training.
E. Disable gradient accumulation when using multi-GPU setups to increase communication efficiency.

Answer: A,C

NEW QUESTION # 107
Which of the following statements best describes the role of GPUs in accelerating data science workloads?

A. GPUs are only effective for acceleration when used in conjunction with Tensor Processing Units (TPUs), as they cannot train deep learning models independently.
B. GPUs use thousands of smaller cores that can execute many parallel computations simultaneously, making them ideal for large-scale matrix operations.
C. GPUs are optimized for sequential data processing tasks, making them more efficient than CPUs for database operations.
D. GPUs are designed primarily for rendering graphics and have limited utility in machine learning and deep learning applications.

Answer: B

NEW QUESTION # 108
Your data science team is performing exploratory data analysis (EDA) on a large GPU-accelerated environment using cuDF and Dask-cuDF. During analysis, queries on categorical columns are performing poorly.
Which approach will most effectively improve query performance for categorical data in GPU-accelerated DataFrames?

A. Increase GPU memory allocation
B. Convert categorical columns to integer codes
C. Use CPU-based DataFrames (pandas) instead of GPU-based DataFrames
D. Store data in compressed file formats like Parquet

Answer: B

NEW QUESTION # 109
When performing benchmarking and optimization for GPU-accelerated workflows, which of the following tools is best suited for analyzing the memory utilization and computational efficiency of deep learning models running on Nvidia GPUs?

A. Nvidia Nsight Compute
B. Nvidia TensorRT
C. Nvidia CUDA Profiler
D. Nvidia Riva

Answer: A

NEW QUESTION # 110
A data engineer is tasked with processing a 5 TB dataset stored in Apache Parquet format. The dataset consists of user activity logs and needs to be filtered, aggregated, and processed for feature engineering before training an ML model. The engineer is deciding between Dask and Apache Spark.
Which statement best describes a key difference between the two frameworks?

A. Dask is optimized for in-memory processing, while Spark requires disk-based storage for computation.
B. Spark cannot utilize GPUs, whereas Dask has built-in GPU acceleration by default.
C. Dask is a more lightweight solution, often preferred for Python-centric workflows, whereas Spark provides broader ecosystem integrations and supports SQL-like operations natively.
D. Spark is better suited for structured and semi-structured data, while Dask excels at unstructured data processing.

Answer: C

NEW QUESTION # 111
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