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NVIDIA NCP-ADS Exam Syllabus Topics:

Section	Weight	Objectives
Machine Learning	15%	- Feature engineering and hyperparameter tuning 1. Batching and memory-efficient training methods 2. Feature engineering for ML models 3. Hyperparameter tuning techniques - Model training with GPU acceleration 1. Multi-GPU training strategies 2. Training models using cuML and GPU-accelerated XGBoost 3. Selection of appropriate algorithms for GPU execution - Deep learning frameworks integration 1. Using RAPIDS with TensorFlow and PyTorch 2. Overfitting vs underfitting concepts
Data Manipulation and Software Literacy	19%	- Software literacy and development tools 1. RAPIDS ecosystem (cuDF, cuML, cuGraph, cuPy) 2. Python, NumPy, pandas, Jupyter proficiency - Distributed computing with Dask 1. Scaling data operations across multiple GPUs 2. Dask-cuDF for parallel data processing - GPU-accelerated data manipulation using cuDF 1. Data integration, joining, merging, and filtering 2. Groupby, apply, and aggregation operations 3. cuDF vs pandas API mapping and usage
GPU and Cloud Computing	16%	- GPU architecture and fundamentals 1. CPU vs GPU workloads and memory transfer optimization 2. GPU architecture fundamentals for data science - Cloud GPU environments 1. Containerized workflow deployment on cloud 2. Cloud-based GPU instance configuration - GPU resource management 1. Efficient GPU resource allocation and scheduling - Performance optimization 1. Single and multi-GPU performance optimization 2. Memory profiling with DLProf 3. Mixed precision and bottleneck analysis
Data Analysis	14%	- Time-series analysis 1. Time-series data handling and forecasting 2. Anomaly detection in time-series datasets - Visualization 1. Visualizing data using Plotly and Matplotlib 2. Selecting appropriate plots for different analysis goals - Graph analytics 1. Creating and analyzing graph data using cuGraph 2. Node importance evaluation and network relationship visualization - Exploratory data analysis 1. Descriptive statistics and summary analysis 2. Performing EDA on GPU-accelerated datasets
MLOps	19%	- Containerization and environment management 1. Conda environment management 2. Docker for reproducible GPU-accelerated workflows - Model monitoring and management 1. Monitoring production models for drift and performance degradation 2. Managing model artifacts and configurations for reproducibility - Model deployment and serving 1. Production deployment strategies 2. Model saving, loading, and prediction generation - Experiment tracking 1. Benchmarking workflows and selecting optimal hardware 2. MLflow, Weights & Biases, and custom tracking tools
Data Preparation	17%	- Data cleaning and quality handling 1. Handling missing values and data quality issues 2. Data governance and compliance - GPU-accelerated ETL workflows 1. RAPIDS-based ETL pipelines 2. Efficient processing and storage with Parquet - Data loading and preprocessing 1. Handling class imbalance and generating synthetic data 2. NVIDIA DALI for high-performance data loading - Feature engineering 1. Feature engineering for numerical and categorical variables 2. Dimensionality reduction and data sampling

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q15-Q20):

NEW QUESTION # 15
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. Spark cannot utilize GPUs, whereas Dask has built-in GPU acceleration by default.
B. Spark is better suited for structured and semi-structured data, while Dask excels at unstructured data processing.
C. Dask is optimized for in-memory processing, while Spark requires disk-based storage for computation.
D. Dask is a more lightweight solution, often preferred for Python-centric workflows, whereas Spark provides broader ecosystem integrations and supports SQL-like operations natively.

Answer: D

NEW QUESTION # 16
When scaling data parallelism using Dask with multiple Nvidia GPUs, what is the key consideration to avoid memory issues when distributing large datasets?

A. Split the dataset into smaller partitions that fit into each GPU's memory to prevent out-of-memory errors, and let Dask manage data distribution.
B. Ensure that each GPU's memory usage is manually monitored and adjusted, as Dask does not manage memory allocation automatically across GPUs.
C. Allow Dask to allocate data chunks dynamically without partitioning the dataset first, letting the system handle memory distribution automatically.
D. Use dask_gpu instead of dask_cuda to manage memory automatically across GPUs.

Answer: A

NEW QUESTION # 17
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. Use cuGraph's sparse matrix representation to store graph data and perform computations efficiently.
C. Convert the graph into an adjacency list format and store it in Python dictionaries for faster lookups.
D. Manually unroll loops and precompute node ranks in a NumPy array before feeding them into cuGraph.

Answer: B

NEW QUESTION # 18
You are working on a machine learning project that requires training a large XGBoost model on a dataset containing millions of records. Due to the dataset size, training on a CPU-based environment takes an excessively long time. To accelerate the training process, you decide to use NVIDIA RAPIDS.
Which of the following is the best approach to leverage GPU acceleration for training the XGBoost model?

A. Install and use dask-xgboost, which automatically optimizes XGBoost training using GPU acceleration.
B. Use XGBoost with the "tree_method": "gpu_hist" parameter to enable GPU acceleration.
C. Use cuML to replace scikit-learn's XGBoost implementation, as cuML supports GPU-accelerated XGBoost training.
D. Store the dataset in Apache Parquet format and load it using pandas to improve training performance.

Answer: B

NEW QUESTION # 19
You are tasked with cleansing a dataset containing numerical data that has significant outliers.
You're using pandas to identify and appropriately handle these outliers before applying CuDF for accelerated downstream analysis.
Which method effectively manages the numerical outliers while preserving the dataset's integrity for subsequent accelerated analytics?

A. Remove outliers completely using pandas.DataFrame.drop().
B. Fill outlier values with mean using pandas.Series.fillna().
C. Clip outliers using pandas.Series.clip() based on percentile thresholds (e.g., 5th and 95th percentile).
D. Replace outliers with zero using pandas.Series.replace().

Answer: C

NEW QUESTION # 20
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