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NVIDIA NCP-ADS Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Machine Learning	15%	- Deep learning frameworks integration 1. Overfitting vs underfitting concepts 2. Using RAPIDS with TensorFlow and PyTorch - Feature engineering and hyperparameter tuning 1. Batching and memory-efficient training methods 2. Hyperparameter tuning techniques 3. Feature engineering for ML models - Model training with GPU acceleration 1. Training models using cuML and GPU-accelerated XGBoost 2. Selection of appropriate algorithms for GPU execution 3. Multi-GPU training strategies
Topic 2: Data Analysis	14%	- Visualization 1. Visualizing data using Plotly and Matplotlib 2. Selecting appropriate plots for different analysis goals - Graph analytics 1. Node importance evaluation and network relationship visualization 2. Creating and analyzing graph data using cuGraph - Time-series analysis 1. Time-series data handling and forecasting 2. Anomaly detection in time-series datasets - Exploratory data analysis 1. Performing EDA on GPU-accelerated datasets 2. Descriptive statistics and summary analysis
Topic 3: Data Manipulation and Software Literacy	19%	- 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. Groupby, apply, and aggregation operations 2. cuDF vs pandas API mapping and usage 3. Data integration, joining, merging, and filtering - Software literacy and development tools 1. RAPIDS ecosystem (cuDF, cuML, cuGraph, cuPy) 2. Python, NumPy, pandas, Jupyter proficiency
Topic 4: GPU and Cloud Computing	16%	- GPU resource management 1. Efficient GPU resource allocation and scheduling - Performance optimization 1. Mixed precision and bottleneck analysis 2. Single and multi-GPU performance optimization 3. Memory profiling with DLProf - GPU architecture and fundamentals 1. GPU architecture fundamentals for data science 2. CPU vs GPU workloads and memory transfer optimization - Cloud GPU environments 1. Containerized workflow deployment on cloud 2. Cloud-based GPU instance configuration
Topic 5: MLOps	19%	- Model deployment and serving 1. Model saving, loading, and prediction generation 2. Production deployment strategies - Containerization and environment management 1. Docker for reproducible GPU-accelerated workflows 2. Conda environment management - Model monitoring and management 1. Monitoring production models for drift and performance degradation 2. Managing model artifacts and configurations for reproducibility - Experiment tracking 1. MLflow, Weights & Biases, and custom tracking tools 2. Benchmarking workflows and selecting optimal hardware
Topic 6: 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. NVIDIA DALI for high-performance data loading 2. Handling class imbalance and generating synthetic data - Feature engineering 1. Dimensionality reduction and data sampling 2. Feature engineering for numerical and categorical variables

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q184-Q189):

NEW QUESTION # 184
You are tasked with optimizing a data science workflow to scale across multiple GPUs using Dask.
Which of the following approaches would be most effective for implementing data parallelism in this scenario? (Select two)

A. Use Dask's distributed scheduler along with dask_cuda to handle GPU resources for parallel processing
B. Use dask_cuda's CUDACluster to manage the multi-GPU setup and parallelize computations
C. Run the computation on a single GPU and let Dask handle CPU parallelism automatically
D. Manually partition the dataset and assign each partition to a specific GPU using dask.delayed
E. Use Dask's default scheduler to distribute work across GPUs without any specific configuration

Answer: A,B

NEW QUESTION # 185
A machine learning engineer is training a convolutional neural network (CNN) on an NVIDIA GPU and needs to maximize throughput while avoiding OOM errors.
Which of the following techniques is the most effective way to balance memory efficiency and training speed?

A. Using dynamic batch sizing based on available GPU memory
B. Allocating a fixed batch size without monitoring memory usage
C. Loading all dataset samples into GPU memory at the start of training
D. Using a batch size of 1 to minimize memory usage

Answer: A

NEW QUESTION # 186
You are designing an accelerated ETL pipeline to process large-scale datasets in a data science workflow.
Which of the following are key considerations when selecting the right tools and methods for implementing this pipeline? (Select two)

A. Leveraging parallel processing and distributed computing frameworks like Apache Spark to speed up the transformation phase.
B. Using GPU-accelerated libraries such as RAPIDS for data transformation to enhance processing speed.
C. Using a single storage location for both raw and transformed data to simplify the workflow.
D. Relying on traditional single-threaded processing for the extraction phase to reduce complexity.
E. Ensuring the ETL pipeline uses only batch processing for data ingestion.

Answer: A,B

NEW QUESTION # 187
You are working with a dataset containing billions of records stored in a Parquet file. You need to load this dataset efficiently into an NVIDIA-accelerated RAPIDS environment for feature engineering.
Which of the following is the best approach?

A. Load the Parquet file directly into a cuDF DataFrame using cudf.read_parquet()
B. Load the Parquet file into Dask and then convert it into a cuDF DataFrame for parallel processing
C. Convert the dataset into a CSV format and use cudf.read_csv() to load it into RAPIDS
D. Use pandas.read_parquet() to load the dataset and then convert it to a cuDF DataFrame

Answer: A

NEW QUESTION # 188
A data scientist is working with a large dataset that contains string-based numeric values that need to be converted to floating-point numbers for further analysis. The dataset is stored as a cuDF DataFrame, and the scientist needs to ensure the conversion is performed optimally on a GPU.
Which of the following is the best method for converting string-based numeric values to floating-point numbers using NVIDIA-accelerated processing?

A. Convert the cuDF DataFrame to a Pandas DataFrame first, then apply astype(float) and convert it back to cuDF.
B. Use pandas.to_numeric() since pandas automatically handles type conversion.
C. Use cudf.DataFrame.astype(float) to convert string values to floating-point numbers efficiently on a GPU.
D. Use NumPy's astype(float) method after converting the cuDF DataFrame into a NumPy array.

Answer: C

NEW QUESTION # 189
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