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NVIDIA NCP-ADS Exam Syllabus Topics:

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

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q158-Q163):

NEW QUESTION # 158
A financial analyst is working with an irregularly spaced time-series dataset containing cryptocurrency transactions. The timestamps are not evenly distributed, with some periods having dense data and others having sparse entries. The analyst wants to visualize the data efficiently using GPU acceleration.
What is the best preprocessing approach before visualization?

A. Resample the time-series to a fixed frequency using cuDF.resample() and fill missing values.
B. Convert the dataset to Pandas and use df.resample() to aggregate by fixed time intervals.
C. Sort the data by timestamp and drop all sparse regions using df.dropna().
D. Ignore the irregularity and plot the raw timestamps directly without any preprocessing.

Answer: A

NEW QUESTION # 159
A data scientist is working on a customer segmentation model using NVIDIA RAPIDS on GPUs. The dataset contains millions of customer records with features such as transaction history, age, location, and frequency of visits.
To optimize feature engineering using NVIDIA technologies, what is the best approach?

A. Convert all categorical variables into string representations to preserve their original format for later analysis.
B. Use cuDF to perform feature transformations like normalization and one-hot encoding directly on the GPU.
C. Perform feature engineering on CPUs using pandas before transferring data to the GPU for training.
D. Store all numerical features in float64 format to prevent rounding errors during transformations.

Answer: B

NEW QUESTION # 160
Which feature of NVIDIA MLFlow integration with Triton Inference Server allows for the seamless deployment and monitoring of models in production?

A. MLFlow only supports deployment on CPUs.
B. MLFlow handles only the training and not the deployment of models.
C. MLFlow allows for version control and automated model rollout.
D. MLFlow prevents models from being updated once deployed to production.

Answer: C

NEW QUESTION # 161
You are working with a large-scale social network dataset and need to analyze relationships between users to detect communities using the Louvain algorithm. Given the benefits of GPU acceleration, you decide to use cuGraph for this task.
Which of the following statements best describes why cuGraph is beneficial for this workload?

A. cuGraph primarily accelerates deep learning tasks and is not optimized for graph-based algorithms.
B. Running graph algorithms on a GPU leads to higher latency due to the overhead of data movement between CPU and GPU.
C. cuGraph enables parallel computation on the GPU, significantly speeding up community detection tasks compared to traditional CPU-based implementations.
D. cuGraph does not support weighted graphs, making it less useful for real-world social network analysis.

Answer: C

NEW QUESTION # 162
You need to generate synthetic data to augment an imbalanced dataset using RAPIDS™ and cuDF.
Which of the following strategies would be most effective in producing high-quality synthetic data for the minority class?

A. Create synthetic data by applying random transformations to the minority class, such as scaling, rotation, or flipping, using cuDF.
B. Generate synthetic data by duplicating entries from the minority class using cudf.DataFrame.sample().
C. Use only the majority class data to train a model and generate synthetic data using a GAN (Generative Adversarial Network) in the RAPIDS ecosystem.
D. Use synthetic data generation libraries like SDV (Synthetic Data Vault) in conjunction with cuDF to create synthetic data that mimics the distribution of the minority class.

Answer: D

NEW QUESTION # 163
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