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NVIDIA NCP-ADS Exam Syllabus Topics:

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

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q290-Q295):

NEW QUESTION # 290
In a typical MLOps pipeline, which of the following practices are essential to ensuring robust deployment and monitoring of machine learning models in production? (Select two)

A. Post-deployment data drift detection to assess model performance degradation.
B. Automated hyperparameter tuning during inference to optimize model performance.
C. Continuous integration and continuous deployment (CI/CD) pipelines for model updates.
D. Use of manual intervention for every model update to ensure accuracy.

Answer: A,C

NEW QUESTION # 291
You are working with a large dataset in RAPIDS cuDF and plan to standardize the numerical features using cuml.preprocessing.StandardScaler(). However, some columns contain missing values.
What is the best approach to handle the missing values before applying standardization?

A. Use cudf.DataFrame.fillna(method='ffill') to forward-fill missing values.
B. Use cuml.impute.SimpleImputer(strategy='mean') to replace missing values with the column mean.
C. cuml.impute.KNNImputer() to replace missing values based on k-nearest neighbors.
D. Replace missing values with zero before standardization.

Answer: B

NEW QUESTION # 292
You are working with a dataset containing 2 billion rows of financial transactions, and you need to perform exploratory data analysis (EDA) before building a predictive model.
Which of the following approaches is the most appropriate for handling this data efficiently?

A. Load the entire dataset into a Pandas DataFrame and analyze it using Pandas built-in methods.
B. Use SQLite to store the data locally and run queries sequentially to minimize memory consumption.
C. Convert the dataset to JSON format and use Python's built-in json module to parse and analyze it efficiently.
D. Use an accelerated data science framework such as RAPIDS cuDF or Dask to distribute computations across GPUs.

Answer: D

NEW QUESTION # 293
You are tasked with processing a large dataset using multiple GPUs to accelerate computation. You decide to use Dask to implement data parallelism with NVIDIA's RAPIDS framework to maximize GPU utilization.
Which of the following steps is essential for efficiently distributing the workload across multiple GPUs in Dask?

A. Use dask_cuda.LocalCUDACluster() to create a multi-GPU cluster and dask.distributed.Client() to manage execution.
B. Use dask.dataframe.repartition() to distribute data evenly across multiple GPUs.
C. Manually allocate GPU memory using cupy for each worker instead of using Dask's scheduler.
D. Set up a single Dask dataframe without partitioning and rely on automatic workload balancing.

Answer: A

NEW QUESTION # 294
You are managing a data processing pipeline that utilizes NVIDIA RAPIDS on GPUs for accelerated data transformations. During execution, you notice that the pipeline is not achieving expected performance gains.
What is the most effective approach to monitor and diagnose bottlenecks in this pipeline using NVIDIA technologies?

A. Reduce the dataset size and rerun the pipeline without profiling tools to check for performance improvements.
B. Use NVIDIA Nsight Systems to profile kernel execution times and memory transfers.
C. Enable RAPIDS memory pool logging to check for memory fragmentation and out-of-memory errors.
D. Run the pipeline on CPU instead of GPU to compare execution times.

Answer: B

NEW QUESTION # 295
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