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NVIDIA NCP-ADS Exam Syllabus Topics:

NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. A data engineer is designing an Extract, Transform, Load (ETL) pipeline for a retail analytics platform that processes millions of customer transactions per day. The primary objective is to accelerate data ingestion, transformation, and storage while ensuring efficient scalability.
Which of the following approaches would be the most effective for optimizing this ETL workflow using NVIDIA-accelerated ETL tools?

A) Implement ETL processes using only SQL-based transformations within a relational database system.
B) Use NVIDIA RAPIDS cuDF for data transformations and Dask-cuDF for parallelized processing across multiple GPUs.
C) Use Apache Spark with CPU-based processing instead of leveraging GPU acceleration.
D) Perform all transformations using Pandas DataFrames and then use multiprocessing to parallelize the workload on CPUs.

2. In the context of cloud computing, what are the key benefits of using GPUs for data science tasks?
(Select two)

A) Lower energy consumption compared to CPUs
B) Efficient handling of matrix operations in machine learning models
C) Lower cost of cloud infrastructure
D) Better for memory-intensive workloads
E) Faster parallel processing for large datasets

3. A data scientist is working with large-scale ETL (Extract, Transform, Load) pipelines on GPU- accelerated infrastructure using RAPIDS. The workload involves frequent shuffle operations, which significantly impact performance.
What is the best approach using NVIDIA technologies to reduce shuffle overhead and improve performance?

A) Use RAPIDS cuDF's GPU memory caching to store intermediate DataFrames and avoid redundant shuffle operations.
B) Enable CUDA Unified Memory to automatically optimize shuffle performance without manual intervention.
C) Store all intermediate shuffle data in CPU memory using pandas to ensure persistence and reduce GPU load.
D) Use RAPIDS cuML to replace shuffle-intensive operations with an ML model that predicts data distribution.

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

5. A research team is analyzing a large transportation network and wants to identify groups of locations that are closely connected based on travel routes.
Which NVIDIA cuGraph function would be the most effective for this task?

A) Use cuGraph's louvain() function to detect communities within the graph.
B) Use cuGraph's pagerank() to rank the most connected locations.
C) Use cuGraph's shortest_path() to compute all shortest paths and derive clusters manually.
D) Apply cuGraph's bfs() (Breadth-First Search) to find clusters of locations.

Solutions: