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NVIDIA NCP-ADS Exam Syllabus Topics:

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

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q19-Q24):

NEW QUESTION # 19
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. int32 - Uses less memory and can store high-precision timestamps efficiently.
B. bool - Provides a highly efficient way to store timestamps as binary values.
C. datetime64[ns] - Optimizes storage and computation for timestamp data in RAPIDS.
D. object - Allows flexibility in storing timestamps as strings for easier parsing.

Answer: C

NEW QUESTION # 20
Which of the following can DLProf specifically help identify when profiling a deep learning model on Nvidia GPUs?

A. Training dataset bias.
B. Hyperparameter tuning results.
C. GPU utilization and memory usage.
D. Number of model parameters.

Answer: C

NEW QUESTION # 21
You are working with a deep learning model for real-time inference on an NVIDIA GPU. Your objective is to optimize inference speed while maintaining acceptable model accuracy.
Which of the following techniques provides the best balance between inference performance and accuracy?

A. Increase the number of neurons in each layer to improve the model's accuracy
B. Use TensorRT for model optimization, including precision quantization and layer fusion
C. Use double-precision (FP64) computations to prevent numerical instability in inference
D. Disable batch inference to ensure each sample is processed independently for better accuracy

Answer: B

NEW QUESTION # 22
You are working on a data science project that requires processing a large-scale dataset stored in CSV format. The dataset contains hundreds of millions of rows, and you want to load it efficiently into NVIDIA RAPIDS cuDF for accelerated processing on a GPU.
Which of the following approaches is the most optimal way to load the dataset?

A. import dask_cudf 2. df = dask_cudf.read_csv("large_dataset.csv")
B. import pandas as pd 2. df = pd.read_csv("large_dataset.csv")
C. import cudf 2. df = cudf.DataFrame.from_pandas(pd.read_csv("large_dataset.csv"))
D. import cudf 2. df = cudf.read_csv("large_dataset.csv", chunksize=100000)

Answer: A

NEW QUESTION # 23
A machine learning engineer is tasked with deploying a real-time image classification model as part of an MLOps pipeline. The model requires low-latency inference and must handle high-throughput requests efficiently.
Which of the following deployment strategies is the most suitable for leveraging GPU acceleration?

A. Running the model inference on a multi-core CPU server with batch processing enabled.
B. Using an Apache Spark cluster with distributed CPU-based inference.
C. Deploying the model using NVIDIA Triton Inference Server with TensorRT optimizations.
D. Using a REST API wrapper to load the model dynamically into memory for each request.

Answer: C

NEW QUESTION # 24
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