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NVIDIA NCP-ADS Exam Syllabus Topics:

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

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q162-Q167):

NEW QUESTION # 162
You are processing a large dataset with UNIX timestamps (seconds since Jan 1, 1970) ranging from Jan 1, 2000, to the present.
What is the most memory-efficient data type for the timestamp column in a GPU-accelerated cloud environment?

A. df['timestamp'] = df['timestamp'].astype('int64')
B. df['timestamp'] = df['timestamp'].astype('float32')
C. df['timestamp'] = df['timestamp'].astype('int32')
D. df['timestamp'] = df['timestamp'].astype('datetime64[ms]')

Answer: C

NEW QUESTION # 163
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. Use NVIDIA DALI to decode images, apply transformations such as resizing and normalization, and load batches directly to the GPU for training.
B. 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.
C. Load the dataset using PyTorch's torchvision.transforms and DataLoader, leveraging the CPU for data preprocessing and transferring batches to the GPU before training.
D. Use OpenCV to load and preprocess images on the CPU, then transfer the processed images to the GPU before training.

Answer: A

NEW QUESTION # 164
You are using NVIDIA DLProf to analyze the performance of a deep learning model deployed on an A100 GPU. The report indicates that compute-bound operations are dominating execution time, and kernel execution efficiency is below 50%.
What is the best action to take based on this insight?

A. Use DLProf's Tensor Core Analysis to check if the model is leveraging Tensor Cores effectively.
B. Increase the batch size to fully utilize available GPU memory and reduce per-sample processing overhead.
C. Reduce the number of layers in the model to decrease computation time.
D. Enable mixed precision training to improve computational efficiency.

Answer: A

NEW QUESTION # 165
You are working on a time-series forecasting project using NVIDIA RAPIDS and GPU-accelerated machine learning. The dataset consists of 10 years of daily stock price data. Your goal is to implement a model that efficiently handles large-scale time-series data while leveraging GPU acceleration for optimal performance.
Which approach best utilizes NVIDIA technologies for efficient forecasting?

A. Use Dask with pandas for data preprocessing, then train a TensorFlow LSTM model on the CPU.
B. Use cuDF to load and preprocess the data, then apply FB Prophet for forecasting.
C. Use cuDF for data preprocessing and train an XGBoost model with GPU acceleration for forecasting.
D. Use PyTorch with CPU acceleration to train a convolutional neural network (CNN) for forecasting.

Answer: C

NEW QUESTION # 166
You are working with a large dataset in NVIDIA RAPIDS cuDF and notice that the data processing pipeline is taking longer than expected.
Which of the following tools or techniques can help you identify and analyze bottlenecks in the pipeline?

A. Use cuml.preprocessing.StandardScaler() to normalize the dataset before processing.
B. Use nvtx.annotate() to label sections of code and analyze performance in Nsight Systems.
C. Enable pandas compatibility mode in cudf to improve performance.
D. Use cudf.DataFrame.astype(str) to convert numerical values to strings for easier debugging.

Answer: B

NEW QUESTION # 167
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