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NVIDIA NCP-ADS Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Data Analysis	14%	- Distributed and parallel data processing - Data visualization and graph analytics - Time-series analysis and anomaly detection - Exploratory Data Analysis (EDA)
Topic 2: MLOps	19%	- End-to-end workflow management - Pipeline automation and orchestration - Model deployment and serving - Monitoring, logging and maintenance
Topic 3: Data Manipulation and Software Literacy	19%	- Data processing libraries selection and usage - Performance profiling and optimization tools - Dependency management and containerization - GPU-accelerated ETL workflows
Topic 4: Machine Learning	15%	- Distributed training strategies - GPU-accelerated ML frameworks and algorithms - Model training and hyperparameter tuning - Model evaluation and validation
Topic 5: Data Preparation	17%	- Workflow monitoring and bottleneck identification - Data cleaning, preprocessing and transformation - Feature engineering and data type optimization - Data validation and quality assurance
Topic 6: GPU and Cloud Computing	16%	- GPU architecture and acceleration principles - CRISP-DM and data science methodology - Resource management and scaling strategies - Cloud GPU environments and deployment

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q37-Q42):

NEW QUESTION # 37
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. Using a single storage location for both raw and transformed data to simplify the workflow.
B. Ensuring the ETL pipeline uses only batch processing for data ingestion.
C. Relying on traditional single-threaded processing for the extraction phase to reduce complexity.
D. Leveraging parallel processing and distributed computing frameworks like Apache Spark to speed up the transformation phase.
E. Using GPU-accelerated libraries such as RAPIDS for data transformation to enhance processing speed.

Answer: D,E

NEW QUESTION # 38
You are preparing a dataset for training a machine learning model using NVIDIA RAPIDS cuML. The dataset contains a feature representing timestamps in nanoseconds.
To optimize GPU performance while ensuring precision, which data type should you choose?

A. datetime64[ns] - Optimizes storage and computation for timestamp data in RAPIDS.
B. bool - Provides a highly efficient way to store timestamps as binary values.
C. int32 - Uses less memory and can store high-precision timestamps efficiently.
D. object - Allows flexibility in storing timestamps as strings for easier parsing.

Answer: A

NEW QUESTION # 39
You are working on optimizing a deep learning model for inference on an NVIDIA GPU. You decide to use NVIDIA DLProf to profile the model and analyze its performance. After running DLProf, you review the generated reports and find that the GPU Utilization is significantly lower than expected.
Which of the following is the most likely reason for this issue, as indicated by the profiling data?

A. The model contains a large number of small, inefficient kernel launches that introduce overhead.
B. The batch size is too large, leading to excessive memory allocation failures.
C. DLProf detected a high level of tensor core utilization, which generally indicates poor performance.
D. The GPU lacks sufficient VRAM, causing frequent memory swaps to system RAM.

Answer: A

NEW QUESTION # 40
You are training a deep learning model on a large dataset of images stored in an Amazon S3 bucket.
You want to optimize data loading, augmentation, and preprocessing on NVIDIA GPUs to avoid CPU bottlenecks.
Which of the following approaches is the most efficient for GPU-accelerated data preprocessing?

A. Load the dataset using PyTorch's torchvision.transforms and DataLoader, leveraging the CPU for data preprocessing and transferring batches to the GPU before training.
B. Use NVIDIA DALI to decode images, apply transformations such as resizing and normalization, and load batches directly to the GPU for training.
C. Use TensorFlow's tf.data API with tf.image transformations and ensure that the preprocessed images are transferred to GPU memory at the end of the pipeline.
D. Use OpenCV to load and preprocess images on the CPU, then transfer the processed images to the GPU before training.

Answer: B

NEW QUESTION # 41
You are building a predictive model for retail sales forecasting and need a dataset that includes historical sales transactions, customer demographics, and external economic indicators (e.g., inflation rate, unemployment rate).
Which of the following datasets would be the most appropriate for your model?

A. A dataset containing transaction history and customer profiles from a retail company
B. A dataset of product reviews and customer sentiments from an e-commerce website
C. A dataset with global temperature trends over the past decade
D. A public dataset of annual GDP per country

Answer: A

NEW QUESTION # 42
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