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NVIDIA NCP-ADS Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: GPU and Cloud Computing	16%	- GPU Optimization and Infrastructure 1. CRISP-DM workflow execution 2. Benchmarking GPU workflows 3. Docker and Conda environment management
Topic 2: Data Manipulation and Software Literacy	19%	- ETL and Data Processing Workflows 1. Distributed data processing frameworks (Dask) 2. GPU-accelerated ETL design and implementation 3. Data caching and performance optimization
Topic 3: MLOps	19%	- Deployment and Monitoring 1. Memory and capacity evaluation 2. Performance benchmarking and optimization 3. Model deployment in production environments
Topic 4: Machine Learning	15%	- Model Development and Optimization 1. Multi-GPU training comparison 2. Feature engineering 3. Hyperparameter tuning 4. Memory optimization techniques (mixed precision, batching)
Topic 5: Data Preparation	17%	- Data Cleaning and Transformation 1. Synthetic data generation with RAPIDS 2. Data normalization and standardization 3. cuDF and pandas data preprocessing
Topic 6: Data Analysis	14%	- Exploratory Data Analysis (EDA) 1. Perform time series analysis and visualization 2. Use cuGraph for graph analytics 3. Detect anomalies in time series datasets

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q168-Q173):

NEW QUESTION # 168
A data engineer is designing an Extract, Transform, Load (ETL) pipeline for a retail analytics platform that processes millions of customer transactions per day. The primary objective is to accelerate data ingestion, transformation, and storage while ensuring efficient scalability.
Which of the following approaches would be the most effective for optimizing this ETL workflow using NVIDIA-accelerated ETL tools?

A. Use NVIDIA RAPIDS cuDF for data transformations and Dask-cuDF for parallelized processing across multiple GPUs.
B. Use Apache Spark with CPU-based processing instead of leveraging GPU acceleration.
C. Implement ETL processes using only SQL-based transformations within a relational database system.
D. Perform all transformations using Pandas DataFrames and then use multiprocessing to parallelize the workload on CPUs.

Answer: A

NEW QUESTION # 169
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. Run the computation on a single GPU and let Dask handle CPU parallelism automatically
C. Manually partition the dataset and assign each partition to a specific GPU using dask.delayed
D. Use Dask's default scheduler to distribute work across GPUs without any specific configuration
E. Use dask_cuda's CUDACluster to manage the multi-GPU setup and parallelize computations

Answer: A,E

NEW QUESTION # 170
Which of the following is the most efficient method for processing big data in a distributed environment using NVIDIA technologies?

A. Using cloud-based, non-GPU infrastructure for data processing
B. Utilizing a distributed data processing framework like Apache Spark with GPU acceleration from the NVIDIA RAPIDS library.
C. Relying on traditional CPU-only processing frameworks for big data tasks.
D. Using a single-node GPU-based solution for data processing.

Answer: B

NEW QUESTION # 171
A machine learning engineer wants to deploy a GPU-accelerated inference model in a containerized environment while ensuring compatibility with NVIDIA libraries.
Which of the following is the best approach for managing dependencies?

A. Use the official NVIDIA Docker base images (nvidia/cuda) and install RAPIDS AI libraries within the container to ensure GPU compatibility.
B. Install GPU drivers directly inside the container instead of on the host system to avoid dependency conflicts.
C. Run Docker containers without any special configurations, as Docker automatically detects and utilizes GPUs.
D. Disable the --gpus flag when running Docker containers, as RAPIDS AI libraries do not require explicit GPU selection.

Answer: A

NEW QUESTION # 172
A data scientist is training a deep learning model on an NVIDIA GPU-accelerated platform. The model is suffering from overfitting, leading to poor generalization on unseen data.
Which of the following techniques is the most effective for reducing overfitting in this scenario?

A. Applying dropout regularization
B. Reducing the learning rate
C. Increasing the number of layers in the model
D. Removing data augmentation techniques

Answer: A

NEW QUESTION # 173
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