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

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q261-Q266):

NEW QUESTION # 261
You are designing an ETL workflow to process large-scale financial transaction data using GPU acceleration. The dataset is stored in a Parquet file and contains millions of records.
Which of the following approaches is the most efficient for performing extract, transform, and load (ETL) operations using NVIDIA RAPIDS technologies?

A. Use Pandas DataFrame for transformation, and then convert the dataset to cuDF before writing to storage.
B. Use Apache Spark with CPU-based processing for ETL, then convert the results into cuDF for accelerated analytics.
C. Store all data as CSV files and perform ETL operations using traditional row-based processing.
D. Load the Parquet file directly into a cuDF DataFrame and use cuDF's built-in functions for transformations before writing the results back to storage.

Answer: D

NEW QUESTION # 262
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. Manually partition the dataset and assign each partition to a specific GPU using dask.delayed
B. Use Dask's default scheduler to distribute work across GPUs without any specific configuration
C. Run the computation on a single GPU and let Dask handle CPU parallelism automatically
D. Use dask_cuda's CUDACluster to manage the multi-GPU setup and parallelize computations
E. Use Dask's distributed scheduler along with dask_cuda to handle GPU resources for parallel processing

Answer: D,E

NEW QUESTION # 263
You need to deploy a containerized machine learning model that utilizes NVIDIA GPUs on a cloud- based Kubernetes cluster.
Which of the following steps is essential for ensuring proper GPU utilization inside a Docker container?

A. Use a standard Python-based container image instead of an NVIDIA GPU-optimized image
B. Install GPU drivers inside the container to ensure access to the host's hardware
C. Use the NVIDIA Container Toolkit (nvidia-docker2) to allow GPU access within Docker containers
D. Run the container using the default Docker runtime without any additional configurations

Answer: C

NEW QUESTION # 264
A data scientist is analyzing sales data for an e-commerce company that experiences strong seasonal trends (e.g., increased sales during holiday seasons). The goal is to accurately forecast future sales using GPU-accelerated data science techniques.
Which approach would be the most effective?

A. Compute a rolling average and manually adjust for seasonality based on previous peak sales months.
B. Apply k-Means clustering to identify seasonal patterns and extrapolate future values.
C. Train a Decision Tree model using cuML to classify future sales trends.
D. Use a Seasonal Autoregressive Integrated Moving Average (SARIMA) model and optimize it using NVIDIA RAPIDS.

Answer: D

NEW QUESTION # 265
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. Use pandas.read_parquet() to load the dataset and then convert it to a cuDF DataFrame
D. Convert the dataset into a CSV format and use cudf.read_csv() to load it into RAPIDS

Answer: A

NEW QUESTION # 266
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