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NVIDIA NCP-ADS Exam Syllabus Topics:

Section	Weight	Objectives
GPU and Cloud Computing	16%	- GPU Optimization and Infrastructure 1. Docker and Conda environment management 2. Benchmarking GPU workflows 3. CRISP-DM workflow execution
MLOps	19%	- Deployment and Monitoring 1. Memory and capacity evaluation 2. Performance benchmarking and optimization 3. Model deployment in production environments
Data Manipulation and Software Literacy	19%	- ETL and Data Processing Workflows 1. Distributed data processing frameworks (Dask) 2. Data caching and performance optimization 3. GPU-accelerated ETL design and implementation
Data Analysis	14%	- Exploratory Data Analysis (EDA) 1. Detect anomalies in time series datasets 2. Perform time series analysis and visualization 3. Use cuGraph for graph analytics
Machine Learning	15%	- Model Development and Optimization 1. Memory optimization techniques (mixed precision, batching) 2. Feature engineering 3. Hyperparameter tuning 4. Multi-GPU training comparison
Data Preparation	17%	- Data Cleaning and Transformation 1. cuDF and pandas data preprocessing 2. Synthetic data generation with RAPIDS 3. Data normalization and standardization

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q135-Q140):

NEW QUESTION # 135
A data engineer is designing an Extract, Transform, Load (ETL) pipeline for a retail analytics platform that processes millions of customer transactions per day. The primary objective is to accelerate data ingestion, transformation, and storage while ensuring efficient scalability.
Which of the following approaches would be the most effective for optimizing this ETL workflow using NVIDIA-accelerated ETL tools?

A. Use Apache Spark with CPU-based processing instead of leveraging GPU acceleration.
B. Perform all transformations using Pandas DataFrames and then use multiprocessing to parallelize the workload on CPUs.
C. Use NVIDIA RAPIDS cuDF for data transformations and Dask-cuDF for parallelized processing across multiple GPUs.
D. Implement ETL processes using only SQL-based transformations within a relational database system.

Answer: C

NEW QUESTION # 136
A data scientist is working with a large dataset for a machine learning model and wants to accelerate feature engineering using a GPU.
Which of the following approaches will provide the most significant performance boost when using GPU acceleration?

A. Reducing dataset size by randomly removing data points without considering class balance.
B. Using a single-threaded feature extraction approach to avoid overhead from parallelization.
C. Using RAPIDS cuDF and cuML libraries to perform feature transformations on a GPU.
D. Using traditional pandas DataFrames and NumPy operations optimized for CPU processing.

Answer: C

NEW QUESTION # 137
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. Downsample the dataset and analyze a subset using Pandas for efficiency.
B. Use RAPIDS cuDF functions like .describe() and .value_counts() to perform statistical summaries directly on the GPU.
C. Perform all analysis on the CPU to avoid potential GPU memory limitations.
D. Convert the dataset to a Pandas DataFrame for easier visualization and use .describe() to summarize statistics.

Answer: B

NEW QUESTION # 138
You are building a real-time recommendation system that processes high-frequency transactional data from millions of users.
The system must:
- Ingest and preprocess data efficiently
- Perform similarity computations for user-item recommendations
- Scale to handle rapid incoming transactions
Which of the following NVIDIA technologies is the best choice for this use case?

A. RAPIDS cuGraph
B. NVIDIA NVTabular
C. NVIDIA Triton Inference Server
D. CUDA Kernels with Custom C++ Code

Answer: B

NEW QUESTION # 139
You are working on an MLOps pipeline that involves loading a large dataset for training a deep learning model on an NVIDIA GPU. Before training, you need to ensure that the dataset fits within the available GPU memory.
Which of the following commands in Python using the pandas and numpy libraries can correctly determine the memory size of a dataset?

A. sys.getsizeof(df)
B. df.info(memory_usage='deep')
C. np.array(df).nbytes
D. df.memory_usage(deep=True).sum()

Answer: D

NEW QUESTION # 140
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