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NVIDIA NCP-ADS Exam Syllabus Topics:

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

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q217-Q222):

NEW QUESTION # 217
A data science team wants to deploy a GPU-accelerated pipeline using cuGraph to analyze graph data on cloud infrastructure. They are evaluating different cloud-based GPU solutions.
Which of the following factors should they consider when selecting a cloud-based GPU instance for running cuGraph efficiently?

A. Cloud-based GPUs are only useful for rendering graphics, not for running cuGraph algorithms.
B. The availability of NVIDIA CUDA-enabled GPUs, as cuGraph requires CUDA for acceleration.
C. The choice of GPU instance does not affect cuGraph performance since all GPUs execute graph algorithms at the same speed.
D. cuGraph runs equally well on CPU-based virtual machines, making GPU instances unnecessary.

Answer: B

NEW QUESTION # 218
You are working on a large-scale graph analysis project using NVIDIA cuGraph for accelerated computations. Your dataset consists of millions of nodes and edges representing social network interactions. You need to efficiently compute PageRank while minimizing memory usage.
Which of the following techniques would be the most effective?

A. Use Pandas DataFrames to manage the graph structure and leverage Pandas indexing for efficient queries.
B. Convert the graph into an adjacency list format and store it in Python dictionaries for faster lookups.
C. Manually unroll loops and precompute node ranks in a NumPy array before feeding them into cuGraph.
D. Use cuGraph's sparse matrix representation to store graph data and perform computations efficiently.

Answer: D

NEW QUESTION # 219
You are working on a financial fraud detection system using NVIDIA RAPIDS cuML. You have a large time-series dataset of transaction amounts over time, and you need to identify anomalies such as unusual spikes in transactions.
Which of the following approaches is the most appropriate for detecting anomalies using NVIDIA technologies?

A. Train a deep learning model with TensorFlow and deploy it using NVIDIA Triton Inference Server for anomaly detection
B. Apply cuML's PCA-based anomaly detection to detect outliers in the principal component space
C. Use cuML's DBSCAN clustering to identify anomalies based on density differences
D. Run standard Pandas-based anomaly detection methods on CPU for better precision

Answer: B

NEW QUESTION # 220
Which of the following techniques are commonly used to identify and acquire datasets for machine learning and data science projects? (Select three)

A. Public APIs
B. Manual data entry
C. Web scraping
D. Crowdsourcing
E. Data augmentation

Answer: A,C,D

NEW QUESTION # 221
A financial analyst is working with an irregularly spaced time-series dataset containing cryptocurrency transactions. The timestamps are not evenly distributed, with some periods having dense data and others having sparse entries. The analyst wants to visualize the data efficiently using GPU acceleration.
What is the best preprocessing approach before visualization?

A. Convert the dataset to Pandas and use df.resample() to aggregate by fixed time intervals.
B. Ignore the irregularity and plot the raw timestamps directly without any preprocessing.
C. Resample the time-series to a fixed frequency using cuDF.resample() and fill missing values.
D. Sort the data by timestamp and drop all sparse regions using df.dropna().

Answer: C

NEW QUESTION # 222
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