NVIDIA NCP-ADS : NVIDIA-Certified-Professional Accelerated Data Science

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NVIDIA NCP-ADS Exam Syllabus Topics:

NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. When utilizing GPU instances in a cloud environment for data science, which of the following are common considerations? (Select two)

A) Cost-effective GPU instances always provide the best performance
B) Cloud providers offer dedicated GPU instances for high-performance needs
C) GPUs are suitable for both training and inference tasks but not for preprocessing
D) GPU performance can be degraded by network latency in distributed training

2. You have a large-scale dataset consisting of IoT sensor readings collected at one-minute intervals across multiple locations. The dataset contains missing values and requires scaling before applying a machine learning model. You plan to use NVIDIA RAPIDS to preprocess and analyze the time-series data efficiently on GPUs.
Which of the following preprocessing steps is the most efficient approach using NVIDIA RAPIDS?

A) Use pandas for missing value imputation, then normalize the data using NumPy before converting to cuDF.
B) Use pandas to fill missing values and scale the data, then convert it to cuDF for training.
C) Use cuDF to handle missing values with GPU-accelerated interpolation and apply cuML's StandardScaler for feature scaling.
D) Use Dask for distributed missing value imputation and train a model using TensorFlow's CPU-based estimator.

3. You are tasked with optimizing a data science workflow to scale across multiple GPUs using Dask.
Which of the following approaches would be most effective for implementing data parallelism in this scenario? (Select two)

A) Use Dask's default scheduler to distribute work across GPUs without any specific configuration
B) Run the computation on a single GPU and let Dask handle CPU parallelism automatically
C) Use Dask's distributed scheduler along with dask_cuda to handle GPU resources for parallel processing
D) Manually partition the dataset and assign each partition to a specific GPU using dask.delayed
E) Use dask_cuda's CUDACluster to manage the multi-GPU setup and parallelize computations

4. A data scientist is preprocessing a dataset containing several types of features:
A timestamp column storing millisecond-resolution timestamps.
A column with binary categorical values (Yes/No).
A column containing large continuous numerical values.
A column containing product category codes ranging from 0 to 5000.
Which of the following data type choices is the most optimal for maximizing GPU processing efficiency using NVIDIA cuDF?

A) Convert timestamps to datetime64[ms], encode binary values as bool, use float32 for continuous values, and int16 for product category codes.
B) Store timestamps as int64, encode binary values as float16, use float64 for continuous numerical values, and use int8 for product category codes.
C) float32 is the best choice for large continuous numerical values, balancing precision and GPU efficiency.
D) Binary categorical values (Yes/No) should be stored as bool, which takes up minimal space.
E) Use float64 for timestamps, int8 for binary categorical values, float32 for continuous numerical values, and int32 for product category codes.
F) Use string data type for timestamps, int32 for binary values, float16 for continuous numerical values, and int64 for product category codes.
G) Product category codes (range: 0-5000) fit within int16 (which can hold values from -32,768 to
32,767), making it more memory-efficient than int32.

5. You need to deploy a containerized machine learning model that utilizes NVIDIA GPUs on a cloud- based Kubernetes cluster.
Which of the following steps is essential for ensuring proper GPU utilization inside a Docker container?

A) Use the NVIDIA Container Toolkit (nvidia-docker2) to allow GPU access within Docker containers
B) Run the container using the default Docker runtime without any additional configurations
C) Install GPU drivers inside the container to ensure access to the host's hardware
D) Use a standard Python-based container image instead of an NVIDIA GPU-optimized image

Solutions: