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Guide for NVIDIA-Certified Professional: Accelerated Data Science (NCP-ADS)

Quiz NVIDIA-Certified Professional: Accelerated Data Science (NCP-ADS)

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Quiz

1/10
A data scientist is training a deep learning model on an NVIDIA GPU but is encountering out-of-memory
(OOM) errors. To optimize GPU memory usage while maintaining efficient training performance, which of
the following strategies should they prioritize?
Select the answer
1 correct answer
A.
Increasing batch size without adjusting the optimizer settings
B.
Using mixed precision training with automatic loss scaling
C.
Storing all training data in GPU memory at once
D.
Using single-precision (FP32) calculations for better accuracy

Quiz

2/10
You are training a large-scale random forest model on a dataset with millions of rows and hundreds of
features. The training time is significantly high when using traditional CPU-based machine learning
frameworks. Which NVIDIA technology should you use to accelerate training while maintaining
compatibility with common ML frameworks like scikit-learn?
Select the answer
1 correct answer
A.
NVIDIA DeepStream to preprocess tabular data and optimize random forest model execution.
B.
NVIDIA RAPIDS cuML to accelerate random forest training using GPU-optimized implementations.
C.
NVIDIA Triton Inference Server to distribute random forest model training across multiple GPUs.
D.
NVIDIA TensorRT to accelerate random forest model training by optimizing tree-based algorithms.

Quiz

3/10
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?
Select the answer
1 correct answer
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.
Increase the number of CPU cores and distribute training across multiple CPU threads.

Quiz

4/10
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?
Select the answer
1 correct answer
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))

Quiz

5/10
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)
Select the answer
2 correct answers
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.

Quiz

6/10
You are tasked with implementing a multi-GPU data pipeline using Dask-CUDA to process large
datasets stored in Parquet format. Your goal is to achieve optimal GPU memory utilization and minimize
inter-GPU communication overhead. Which of the following approaches best aligns with these goals?
Select the answer
1 correct answer
A.
Set dask.config.set({'distributed.worker.memory.target': 0.9}) to allocate 90% of the total CPU memory for GPU operations.
B.
Use dask_cudf.read_parquet() with split_row_groups=True to evenly distribute data across GPUs.
C.
Use dask.persist() instead of dask.compute() to force immediate execution of tasks before distribution to GPUs.
D.
Use dask.array instead of dask_cudf because it provides better performance for structured tabular data.

Quiz

7/10
A machine learning engineer wants to evaluate the performance of NVIDIA RAPIDS cuDF and Apache
Spark for large-scale data processing on a GPU-enabled cluster. Which of the following strategies is the
most effective for obtaining a fair and comprehensive benchmark?
Select the answer
1 correct answer
A.
Run Spark on a CPU cluster while running RAPIDS on a GPU to compare real-world scenarios.
B.
Limit the benchmark to small datasets since GPUs excel at parallel processing.
C.
Execute identical ETL workflows on cuDF and Spark-RAPIDS and measure execution time and resource utilization.
D.
Focus only on processing speed without considering resource consumption differences between frameworks.

Quiz

8/10
When performing benchmarking and optimization for GPU-accelerated workflows, which of the following
tools is best suited for analyzing the memory utilization and computational efficiency of deep learning
models running on Nvidia GPUs?
Select the answer
1 correct answer
A.
Nvidia Nsight Compute
B.
Nvidia TensorRT
C.
Nvidia CUDA Profiler
D.
Nvidia Riva

Quiz

9/10
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?
Select the answer
1 correct answer
A.
Log Transformation
B.
Z-score Standardization
C.
Min-Max Scaling
D.
Robust Scaling

Quiz

10/10
You are working on a data processing pipeline using NVIDIA GPUs for accelerating computations. You
need to monitor the pipeline's performance to identify bottlenecks. Which of the following tools or
techniques can be used to efficiently recognize bottlenecks in such a GPU-accelerated pipeline? (Select
two)
Select the answer
2 correct answers
A.
NVIDIA DLA (Deep Learning Accelerator)
B.
NVIDIA CUDA Profiler (nvprof)
C.
NVIDIA Nsight Systems
D.
NVIDIA nvidia-smi
E.
NVIDIA TensorRT Profiling
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  • Quiz name:NVIDIA-Certified Professional: Accelerated Data Science (NCP-ADS)
  • Total number of questions:300
  • Number of questions for the test:50
  • Pass score:80%

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