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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q59-Q64):

NEW QUESTION # 59
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. Binary categorical values (Yes/No) should be stored as bool, which takes up minimal space.
B. Convert timestamps to datetime64[ms], encode binary values as bool, use float32 for continuous values, and int16 for product category codes.
C. Use float64 for timestamps, int8 for binary categorical values, float32 for continuous numerical values, and int32 for product category codes.
D. float32 is the best choice for large continuous numerical values, balancing precision and GPU efficiency.
E. 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.
F. Store timestamps as int64, encode binary values as float16, use float64 for continuous numerical values, and use int8 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.

Answer: B

NEW QUESTION # 60
You are working with a large dataset in NVIDIA RAPIDS cuDF and notice that the data processing pipeline is taking longer than expected.
Which of the following tools or techniques can help you identify and analyze bottlenecks in the pipeline?

A. Use cudf.DataFrame.astype(str) to convert numerical values to strings for easier debugging.
B. Use nvtx.annotate() to label sections of code and analyze performance in Nsight Systems.
C. Enable pandas compatibility mode in cudf to improve performance.
D. Use cuml.preprocessing.StandardScaler() to normalize the dataset before processing.

Answer: B

NEW QUESTION # 61
You are managing a data processing pipeline that utilizes NVIDIA RAPIDS on GPUs for accelerated data transformations. During execution, you notice that the pipeline is not achieving expected performance gains.
What is the most effective approach to monitor and diagnose bottlenecks in this pipeline using NVIDIA technologies?

A. Run the pipeline on CPU instead of GPU to compare execution times.
B. Reduce the dataset size and rerun the pipeline without profiling tools to check for performance improvements.
C. Enable RAPIDS memory pool logging to check for memory fragmentation and out-of-memory errors.
D. Use NVIDIA Nsight Systems to profile kernel execution times and memory transfers.

Answer: D

NEW QUESTION # 62
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 map() function to apply a function element-wise
B. Using for loops to apply operations row by row
C. Using apply() function over DataFrame rows
D. Using iterrows() for iterating through DataFrame rows
E. Using vectorized operations (e.g., element-wise arithmetic)

Answer: A,E

NEW QUESTION # 63
You are considering using a multi-GPU setup to accelerate training a large deep learning model.
Which of the following are important factors to consider when deciding whether to use single-GPU or multi-GPU training? (Select two)

A. The performance of multi-GPU training scales linearly with the number of GPUs, meaning adding more GPUs will always result in a proportional reduction in training time.
B. The success of multi-GPU training depends heavily on the ability to increase the batch size without exceeding memory limits.
C. Multi-GPU setups require proper load balancing and efficient gradient synchronization, as uneven distribution of work can lead to suboptimal performance.
D. The increase in training time from using a multi-GPU setup is negligible, as the overhead from communication and synchronization is minimal.

Answer: B,C

NEW QUESTION # 64
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