About this exam
NCP-ADS is the NVIDIA exam NVIDIA-Certified-Professional Accelerated Data Science.
Paid practice material for this exam is not available yet, but you can try its free sample questions.
- Exam code
- NCP-ADS
- Vendor
- NVIDIA
- Practice questions
- Not available yet
- Free sample questions
- 5
- Question bank last updated
- Not available yet
Try 2 sample questions
Taken from the free sample set for NCP-ADS.
Question 1
You are training a machine learning model using RAPIDS cuML and need to ensure that all numeric
features are standardized for better model performance.
Which of the following is the best approach for scaling data using RAPIDS?
- AOption A:
df_scaled = df / df.max()
- BOption B:
df_scaled = (df - df.min()) / (df.max() - df.min())
- COption C:
scaler = cuml.preprocessing.StandardScaler()
- DOption D:
df_scaled = scaler.fit_transform(df)
- EOption E:
df_scaled = df.apply(lambda x: x / np.linalg.norm(x))
Show answer and explanation
Correct answer: C
Question 2 (choose 2)
When deciding whether to use GPU acceleration or a traditional CPU approach for a machine learning
task, which of the following factors should be considered to determine if the data qualifies as "big data"
and whether GPU acceleration is beneficial? (Select two)
- AOption A:
CPU-based machine learning methods are always more effective for small datasets, regardless of the
algorithm used.
- BOption B:
The size of the dataset in terms of rows and columns is irrelevant when determining if it qualifies as
big data.
- COption C:
GPU acceleration is beneficial when the dataset can be divided into independent chunks that can be
processed in parallel.
- DOption D:
The complexity of the algorithm being used plays a crucial role in deciding whether to use GPU
acceleration, with more complex algorithms benefiting from parallel computation.
- EOption E:
The dataset must be over 100GB in size to qualify as big data and warrant GPU acceleration.
Show answer and explanation
Correct answer: C, D
See all 5 free sample questions