Learning NVIDIA NCP-ADS Materials, NCP-ADS Exam Tests

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NVIDIA NCP-ADS Exam Syllabus Topics:

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

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q231-Q236):

NEW QUESTION # 231
You are processing a large dataset using RAPIDS cuDF and Dask-cuDF on an NVIDIA GPU. Your profiling indicates that data transfer times between CPU and GPU are significantly slowing down your pipeline.
What is the most effective way to reduce this bottleneck?

A. Convert the dataset into a CSV format before transferring it to the GPU
B. Use cudf.read_parquet() instead of Pandas to load data directly into GPU memory
C. Transfer data in multiple smaller chunks to the GPU instead of larger batches
D. Increase the CPU RAM allocation to store more data before transferring to the GPU

Answer: B

NEW QUESTION # 232
A machine learning team has deployed a fraud detection model in production, and they want to monitor its performance over time to detect model drift and performance degradation.
Which monitoring strategy is most effective for this use case?

A. Logging only inference latency without tracking model predictions
B. Implementing model performance tracking with drift detection metrics
C. Disabling logging to reduce system overhead and avoid storage costs
D. Using a static dataset for monitoring instead of real-time production data

Answer: B

NEW QUESTION # 233
You are consulting for a retail company that collects data from daily sales transactions, customer interactions, and inventory tracking across multiple locations. They are unsure whether their dataset qualifies as big data and which processing method would be most suitable.
Which of the following characteristics best indicate that the dataset requires big data processing and acceleration techniques?

A. The dataset includes a mix of numerical and categorical variables, requiring additional preprocessing
B. The dataset is stored in multiple relational database tables, making querying inefficient
C. The dataset contains more than 1 million records, making it impossible to process using pandas
D. The dataset exceeds the memory capacity of a single machine and requires distributed or GPU- accelerated processing

Answer: D

NEW QUESTION # 234
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('int32')
B. df['item_id'] = df['item_id'].astype('float64')
C. df['item_id'] = df['item_id'].astype('int64')
D. df['item_id'] = df['item_id'].astype('string')

Answer: C

NEW QUESTION # 235
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. Persisting intermediate results in distributed GPU memory using df.persist() ensures that Dask does not recompute partitions, reducing shuffle overhead.
D. Using .compute() on each partition immediately forces Dask to materialize results and store them in memory, ensuring shuffle operations are avoided.

Answer: C

NEW QUESTION # 236
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