NCP-ADS NVIDIA-Certified-Professional Accelerated Data Science Free Demo Questions – Try Before You Buy

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?

A

df_scaled = df / df.max()

B

df_scaled = (df - df.min()) / (df.max() - df.min())

C

scaler = cuml.preprocessing.StandardScaler()

D

df_scaled = scaler.fit_transform(df)

E

df_scaled = df.apply(lambda x: x / np.linalg.norm(x))

Correct Answer: C

Question 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)

A

CPU-based machine learning methods are always more effective for small datasets, regardless of the algorithm used.

B

The size of the dataset in terms of rows and columns is irrelevant when determining if it qualifies as big data.

C

GPU acceleration is beneficial when the dataset can be divided into independent chunks that can be processed in parallel.

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.

E

The dataset must be over 100GB in size to qualify as big data and warrant GPU acceleration.

Correct Answer: C, D

Question 3

A data scientist is working on training a deep learning model in a cloud-based environment. The dataset is large, and model convergence is taking too long on a standard CPU instance. To optimize performance through GPU acceleration, which of the following strategies should the data scientist implement?

A

Use a cloud instance with multiple GPUs and enable mixed-precision training.

B

Store all training data in RAM and load it directly to the CPU for processing.

C

Disable CUDA and use only OpenMP to parallelize computations across CPU cores.

D

Demo NVIDIA NCP-ADS Exam Questions

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?

df_scaled = df / df.max()

df_scaled = (df - df.min()) / (df.max() - df.min())

scaler = cuml.preprocessing.StandardScaler()

df_scaled = scaler.fit_transform(df)

Correct Answer: C

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)

CPU-based machine learning methods are always more effective for small datasets, regardless of the algorithm used.

The size of the dataset in terms of rows and columns is irrelevant when determining if it qualifies as big data.

GPU acceleration is beneficial when the dataset can be divided into independent chunks that can be processed in parallel.

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.

The dataset must be over 100GB in size to qualify as big data and warrant GPU acceleration.

Correct Answer: C, D

Use a cloud instance with multiple GPUs and enable mixed-precision training.

Store all training data in RAM and load it directly to the CPU for processing.

Disable CUDA and use only OpenMP to parallelize computations across CPU cores.

Increase the number of CPU cores and distribute training across multiple CPU threads.

Correct Answer: A

Demo Practice Mode

Demo NVIDIA NCP-ADS Exam Questions

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?

df_scaled = df / df.max()

df_scaled = (df - df.min()) / (df.max() - df.min())

scaler = cuml.preprocessing.StandardScaler()

df_scaled = scaler.fit_transform(df)

Correct Answer: C

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)

CPU-based machine learning methods are always more effective for small datasets, regardless of the algorithm used.

The size of the dataset in terms of rows and columns is irrelevant when determining if it qualifies as big data.

GPU acceleration is beneficial when the dataset can be divided into independent chunks that can be processed in parallel.

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.

The dataset must be over 100GB in size to qualify as big data and warrant GPU acceleration.

Correct Answer: C, D

Use a cloud instance with multiple GPUs and enable mixed-precision training.

Store all training data in RAM and load it directly to the CPU for processing.

Disable CUDA and use only OpenMP to parallelize computations across CPU cores.

Increase the number of CPU cores and distribute training across multiple CPU threads.

Correct Answer: A

Demo Practice Mode

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?