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NVIDIA NCP-ADS Exam Syllabus Topics:

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

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q270-Q275):

NEW QUESTION # 270
A data engineering team is tasked with processing terabytes of log data every hour using an ETL pipeline. Due to the large data volume, they need a scalable GPU-accelerated solution that can distribute data processing across multiple GPUs.
Which approach best meets their needs?

A. Use Dask-cuDF to distribute cuDF DataFrame operations across multiple GPUs, enabling parallel ETL processing.
B. Use NumPy for data transformations before converting the dataset into cuDF for final storage.
C. Use cuDF alone for processing log data, as it provides optimal performance on a single GPU.
D. Process data using Pandas, then export the results to a CSV file for GPU-accelerated analytics.

Answer: A

NEW QUESTION # 271
You are working with large datasets in cuDF and have noticed significant performance bottlenecks due to repeated computation and excessive shuffling in your workflow. You want to use data caching to optimize the execution plan and reduce redundant operations.
Which of the following is the best way to implement data caching in cuDF to avoid repeated recomputation and excessive shuffling?

A. Store intermediate DataFrames as temporary CSV files on disk and reload them when needed to simulate a cache.
B. Use the .to_pandas() method to convert the cuDF DataFrame into a Pandas DataFrame, leveraging CPU caching instead of GPU caching.
C. Use the .reset_index() method after performing transformations to force materialization of the dataset and cache results.
D. Use the .persist() method on a cuDF DataFrame to store intermediate results in GPU memory, reducing redundant calculations.

Answer: D

NEW QUESTION # 272
You are working on optimizing a deep learning model for inference on an NVIDIA GPU. You decide to use NVIDIA DLProf to profile the model and analyze its performance. After running DLProf, you review the generated reports and find that the GPU Utilization is significantly lower than expected.
Which of the following is the most likely reason for this issue, as indicated by the profiling data?

A. The GPU lacks sufficient VRAM, causing frequent memory swaps to system RAM.
B. The model contains a large number of small, inefficient kernel launches that introduce overhead.
C. The batch size is too large, leading to excessive memory allocation failures.
D. DLProf detected a high level of tensor core utilization, which generally indicates poor performance.

Answer: B

NEW QUESTION # 273
You are training a deep learning model on a large dataset of images stored in an Amazon S3 bucket.
You want to optimize data loading, augmentation, and preprocessing on NVIDIA GPUs to avoid CPU bottlenecks.
Which of the following approaches is the most efficient for GPU-accelerated data preprocessing?

A. Load the dataset using PyTorch's torchvision.transforms and DataLoader, leveraging the CPU for data preprocessing and transferring batches to the GPU before training.
B. Use TensorFlow's tf.data API with tf.image transformations and ensure that the preprocessed images are transferred to GPU memory at the end of the pipeline.
C. Use OpenCV to load and preprocess images on the CPU, then transfer the processed images to the GPU before training.
D. Use NVIDIA DALI to decode images, apply transformations such as resizing and normalization, and load batches directly to the GPU for training.

Answer: D

NEW QUESTION # 274
A data scientist is working with large-scale tabular datasets and wants to optimize data ingestion and storage for accelerated processing on NVIDIA GPUs. The scientist is considering different file formats and storage optimizations to maximize performance in a RAPIDS-based workflow.
Which of the following approaches is the most suitable for optimizing both storage and processing performance?

A. Load data directly into NumPy arrays before using RAPIDS cuDF for processing.
B. Use JSON format for easy readability and process it directly in cuDF.
C. Store data in Parquet format and load it using cuDF in RAPIDS.
D. Convert datasets into CSV format and store them in local disk storage.

Answer: C

NEW QUESTION # 275
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