Exam Name: NVIDIA-Certified-Professional Accelerated Data Science
Certification Provider: NVIDIA
Corresponding Certification: NVIDIA-Certified Professional
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NVIDIA NCP-ADS Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Preparation | 17% | - Data Cleaning and Transformation
|
| Topic 2: Data Manipulation and Software Literacy | 19% | - ETL and Data Processing Workflows
|
| Topic 3: Machine Learning | 15% | - Model Development and Optimization
|
| Topic 4: GPU and Cloud Computing | 16% | - GPU Optimization and Infrastructure
|
| Topic 5: MLOps | 19% | - Deployment and Monitoring
|
| Topic 6: Data Analysis | 14% | - Exploratory Data Analysis (EDA)
|
NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:
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('float32')
- B. df['timestamp'] = df['timestamp'].astype('datetime64[ms]')
- C. df['timestamp'] = df['timestamp'].astype('int32')
- D. df['timestamp'] = df['timestamp'].astype('int64')
Correct Answer: C 🗳️
A machine learning engineer is training a convolutional neural network (CNN) on an NVIDIA GPU and needs to maximize throughput while avoiding OOM errors.
Which of the following techniques is the most effective way to balance memory efficiency and training speed?
- A. Loading all dataset samples into GPU memory at the start of training
- B. Using a batch size of 1 to minimize memory usage
- C. Using dynamic batch sizing based on available GPU memory
- D. Allocating a fixed batch size without monitoring memory usage
Correct Answer: C 🗳️
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. 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. - B. float32 is the best choice for large continuous numerical values, balancing precision and GPU efficiency.
- C. Convert timestamps to datetime64[ms], encode binary values as bool, use float32 for continuous values, and int16 for product category codes.
- D. Store timestamps as int64, encode binary values as float16, use float64 for continuous numerical values, and use int8 for product category codes.
- E. Binary categorical values (Yes/No) should be stored as bool, which takes up minimal space.
- F. Use float64 for timestamps, int8 for binary categorical values, float32 for continuous numerical values, and int32 for product category codes.
- G. Use string data type for timestamps, int32 for binary values, float16 for continuous numerical values, and int64 for product category codes.
Correct Answer: C 🗳️
You are working on a large-scale data processing pipeline that involves multi-GPU acceleration using Dask. The dataset is too large to fit into the memory of a single GPU, so you decide to distribute the workload across multiple GPUs using Dask-CUDA.
Which of the following steps is necessary to implement efficient data parallelism across multiple GPUs in a Dask-based workflow?
- A. Use dask.dataframe.read_parquet() to load the dataset and let Dask automatically distribute computations across multiple GPUs.
- B. Convert Dask DataFrames into Pandas DataFrames to leverage GPU acceleration through Pandas' built-in multi-threading support.
- C. Use the DaskExecutor from RAPIDS cuML to offload data processing tasks to distributed GPU workers.
- D. Explicitly initialize a LocalCUDACluster with multiple workers, ensuring each worker is assigned a single GPU.
Correct Answer: D 🗳️
You are working on an accelerated data science project and need to acquire a large dataset stored in a Parquet file format and load it efficiently for GPU processing using NVIDIA RAPIDS.
Which of the following approaches is the most efficient way to load the dataset into a GPU-accelerated DataFrame?
- A. df = cudf.to_gpu(pd.read_parquet("data.parquet"))
- B. df = cudf.read_csv("data.parquet")
- C. df = pd.read_parquet("data.parquet")
- D. df = cudf.read_parquet("data.parquet")
Correct Answer: D 🗳️
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