Latest NVIDIA NCP-ADS Actual Free Exam Questions & Community Discussion, Page 5

Question #45

In Python, when working with large datasets using pandas, which of the following methods are best for improving performance and efficiency when applying operations on DataFrames? (Select two)

A. Using apply() function over DataFrame rows

B. Using map() function to apply a function element-wise

C. Using iterrows() for iterating through DataFrame rows

D. Using for loops to apply operations row by row

E. Using vectorized operations (e.g., element-wise arithmetic)

Discussion 0

Correct Answer: B,E Vote an answer

Question #46

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.read_csv("data.parquet")

B. df = cudf.to_gpu(pd.read_parquet("data.parquet"))

C. df = cudf.read_parquet("data.parquet")

D. df = pd.read_parquet("data.parquet")

Discussion 0

Correct Answer: C Vote an answer

Question #47

A machine learning engineer wants to accelerate the training of a large deep learning model while optimizing GPU utilization.
Which of the following strategies provides the best performance improvement using NVIDIA technologies?

A. Disable mixed-precision training since it reduces numerical accuracy and does not significantly impact performance.

B. Use NVIDIA DALI (Data Loading Library) to preprocess and augment data on the GPU before feeding it to the model.

C. Use CPU-based data augmentation techniques before transferring the data to the GPU for training.

D. Load all training data into GPU memory at once to minimize CPU-GPU data transfer time.

Discussion 0

Correct Answer: B Vote an answer

Question #48

You are working with a cuDF DataFrame and need to convert a column named sales from float64 to int32 to save memory.
Which of the following is the correct and most efficient way to perform this conversion in cuDF?

A. df['sales'].apply(lambda x: int(x))

B. df['sales'].convert_dtypes('int32')

C. df['sales'] = df['sales'].astype('int32')

D. df['sales'] = df['sales'].to_numeric('int32')

Discussion 0

Correct Answer: C Vote an answer

Question #49

Which of the following data normalization techniques is most appropriate when the dataset contains outliers, and you want to minimize the influence of those outliers on the model performance?

A. Log Transformation

B. Z-score Standardization

C. Robust Scaling

D. Min-Max Scaling

Discussion 0

Correct Answer: C Vote an answer

Question #50

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. df_scaled = scaler.fit_transform(df)

D. scaler = cuml.preprocessing.StandardScaler()

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

Discussion 0

Correct Answer: D Vote an answer

Question #51

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

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

E. 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.

Discussion 0

Correct Answer: D,E Vote an answer

Question #52

You are developing an accelerated ETL workflow that requires data transformations such as filtering, aggregating, and joining large datasets. You decide to leverage NVIDIA GPUs to accelerate the transformation phase of your ETL pipeline.
Which of the following approaches will provide the greatest performance improvements when working with large-scale tabular datasets?

A. Performing transformations using SQL-based queries on CPU

B. Relying on traditional pandas for in-memory transformations

C. Using RAPIDS cuDF to perform transformations on a GPU

D. Using TensorFlow for data transformation tasks

Discussion 0

Correct Answer: C Vote an answer

Question #53

A data scientist is working with a large dataset containing millions of records and aims to accelerate the data preprocessing workflow using NVIDIA technologies.
Which of the following approaches is the most effective for optimizing data preprocessing performance using GPUs?

A. Using Dask to distribute processing across multiple CPU cores

B. Using NumPy for array computations and parallelizing with Python's multiprocessing

C. Running data transformations in a multi-threaded CPU environment

D. Using RAPIDS cuDF to replace pandas operations

Discussion 0

Correct Answer: D Vote an answer

Question #54

A data scientist is using RAPIDS cuML to build a predictive model on a large dataset containing numerical and categorical features.
To optimize feature engineering for accelerated GPU processing, which of the following is the best approach?

A. Convert all numerical features to float64 to maximize precision for feature transformation.

B. Apply one-hot encoding on high-cardinality categorical features directly in cuDF without any transformation.

C. Convert all categorical variables to string format for efficient processing in cuDF.

D. Normalize numerical features using min-max scaling implemented with cuDF, leveraging GPU acceleration.

Discussion 0

Correct Answer: D Vote an answer

Question #55

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.read_csv("data.parquet")

B. df = cudf.to_gpu(pd.read_parquet("data.parquet"))

C. df = cudf.read_parquet("data.parquet")

D. df = pd.read_parquet("data.parquet")

Discussion 0

Correct Answer: C Vote an answer

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