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NVIDIA NCP-ADS Exam Syllabus Topics:

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

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q163-Q168):

NEW QUESTION # 163
A data scientist is working on a dataset where the numerical features have different ranges, and they need to ensure uniformity across features before training a machine learning model.
Which of the following approaches, utilizing NVIDIA technologies, would best achieve this goal?

A. Apply cuDF's normalize() function to scale each feature between 0 and 1.
B. Apply cuML's RobustScaler() to center the data using median and scale using the interquartile range.
C. Use cuML's PCA to directly remove the need for standardization by reducing dimensionality.
D. Use cuML's StandardScaler() to transform the features to have zero mean and unit variance.

Answer: D

NEW QUESTION # 164
You are training a deep learning model on a large dataset and are deciding whether to use a single GPU or multiple GPUs.
Which of the following are true considerations when comparing single-GPU and multi-GPU training setups? (Select two)

A. Multi-GPU training can significantly reduce training time when the dataset is large and the model is computationally intensive.
B. Multi-GPU training requires modifications to the model architecture to make it compatible with parallel processing.
C. Single-GPU training is limited by the VRAM (video memory) on the GPU, so larger models or datasets may require multi-GPU setups.
D. Multi-GPU setups perform better only when the batch size is reduced.
E. Single-GPU training is generally more cost-effective and should be preferred unless scaling is absolutely necessary.

Answer: A,C

NEW QUESTION # 165
You are processing large-scale datasets in Dask-cuDF and observe that your computation involves excessive data shuffling, which slows down performance. You decide to implement data caching to reduce shuffle overhead.
Which of the following best describes a technique to reduce shuffle costs in a Dask-cuDF workflow?

A. Persisting intermediate results in distributed GPU memory using df.persist() ensures that Dask does not recompute partitions, reducing shuffle overhead.
B. Disabling lazy execution using dask.config.set(lazy=False) forces all computations to execute eagerly, thereby avoiding shuffle.
C. Splitting the DataFrame into multiple smaller DataFrames and recomputing each separately reduces shuffle operations in Dask-cuDF.
D. Using .compute() on each partition immediately forces Dask to materialize results and store them in memory, ensuring shuffle operations are avoided.

Answer: A

NEW QUESTION # 166
Which of the following scenarios are most appropriate for using GPU acceleration when working with large-scale datasets in machine learning? (Select two)

A. Running a deep learning model for image classification with millions of labeled images.
B. Training a large-scale natural language processing (NLP) model on text data with billions of words.
C. Using decision trees for a classification task with a dataset of 1 million rows and 20 features.
D. Running a large ensemble of simple models (e.g., random forests) on a dataset of 10 million rows.
E. Performing exploratory data analysis (EDA) on a dataset of 100,000 rows with 10 features.

Answer: A,B

NEW QUESTION # 167
You are tasked with implementing data caching to reduce shuffle in an accelerated machine learning pipeline using NVIDIA technologies. You need to cache intermediate results after a shuffle operation in a distributed setting.
Which of the following is the best approach to minimize shuffle overhead and maximize performance?

A. Use DALI to perform preprocessing and cache the output before the shuffle operation, thereby eliminating the need for shuffle.
B. Use RAPIDS cuDF to cache the shuffled data on disk and then re-load it from disk during subsequent stages.
C. Implement Spark's default disk caching to store shuffle results, allowing the GPU to access disk data directly.
D. Cache the data in GPU memory using RAPIDS cuDF for faster access, and leverage GPU-based partitioning to reduce shuffle size.

Answer: B

NEW QUESTION # 168
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