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NVIDIA NCP-ADS Exam Syllabus Topics:

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

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q239-Q244):

NEW QUESTION # 239
You are processing large-scale datasets in Dask-cuDF and observe that your computation involves excessive data shuffling, which slows down performance. You decide to implement data caching to reduce shuffle overhead.
Which of the following best describes a technique to reduce shuffle costs in a Dask-cuDF workflow?

A. Disabling lazy execution using dask.config.set(lazy=False) forces all computations to execute eagerly, thereby avoiding shuffle.
B. Splitting the DataFrame into multiple smaller DataFrames and recomputing each separately reduces shuffle operations in Dask-cuDF.
C. Using .compute() on each partition immediately forces Dask to materialize results and store them in memory, ensuring shuffle operations are avoided.
D. Persisting intermediate results in distributed GPU memory using df.persist() ensures that Dask does not recompute partitions, reducing shuffle overhead.

Answer: D

NEW QUESTION # 240
You are analyzing a time-series dataset that represents temperature readings from an industrial sensor. Your goal is to detect anomalies that may indicate sensor failures or environmental changes.
Which of the following methods would be the most appropriate statistical technique for detecting anomalies in this time-series dataset?

A. Principal Component Analysis (PCA)
B. One-Hot Encoding
C. Moving Average Smoothing
D. Z-score (Standard Score) Analysis

Answer: D

NEW QUESTION # 241
A machine learning engineer wants to accelerate the training of a large deep learning model while optimizing GPU utilization.
Which of the following strategies provides the best performance improvement using NVIDIA technologies?

A. Load all training data into GPU memory at once to minimize CPU-GPU data transfer time.
B. Use CPU-based data augmentation techniques before transferring the data to the GPU for training.
C. Disable mixed-precision training since it reduces numerical accuracy and does not significantly impact performance.
D. Use NVIDIA DALI (Data Loading Library) to preprocess and augment data on the GPU before feeding it to the model.

Answer: D

NEW QUESTION # 242
You are comparing the performance of NVIDIA RAPIDS cuML, TensorFlow, and PyTorch for training and inference on a dataset with millions of records.
To design a fair and effective benchmark, which approach should you take?

A. Use only a CPU baseline for comparison to demonstrate the benefits of GPU acceleration, ignoring GPU-specific optimizations.
B. Run each framework on different GPUs to maximize available resources and compare execution times across different hardware configurations.
C. Ensure all frameworks run on the same GPU, use optimized batch sizes, and measure execution time and memory usage with NVIDIA Nsight Systems.

Answer: C

NEW QUESTION # 243
You are working with a GPU-based cloud environment and need to optimize the memory usage for a dataset that contains a column item_id representing unique product IDs. The item_id values are large integers, and there are over 10 million distinct product IDs.
Which of the following is the most memory-efficient data type choice for this column?

A. df['item_id'] = df['item_id'].astype('int64')
B. df['item_id'] = df['item_id'].astype('float64')
C. df['item_id'] = df['item_id'].astype('int32')
D. df['item_id'] = df['item_id'].astype('string')

Answer: A

NEW QUESTION # 244
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