NVIDIA NCP-ADS Actual Free Exam Questions & Community Discussion

Exam Code/Number: NCP-ADS
Exam Name/Title: NVIDIA-Certified-Professional Accelerated Data Science
Certification Provider: NVIDIA
Corresponding Certification: NVIDIA-Certified Professional

Exam Questions: 303
Updated On: Jul 16, 2026

Question #34

You are working with large datasets in cuDF and have noticed significant performance bottlenecks due to repeated computation and excessive shuffling in your workflow. You want to use data caching to optimize the execution plan and reduce redundant operations.
Which of the following is the best way to implement data caching in cuDF to avoid repeated recomputation and excessive shuffling?

A. Use the .reset_index() method after performing transformations to force materialization of the dataset and cache results.

B. Use the .persist() method on a cuDF DataFrame to store intermediate results in GPU memory, reducing redundant calculations.

C. Store intermediate DataFrames as temporary CSV files on disk and reload them when needed to simulate a cache.

D. Use the .to_pandas() method to convert the cuDF DataFrame into a Pandas DataFrame, leveraging CPU caching instead of GPU caching.

Discussion 0

Correct Answer: B Vote an answer

Question #35

You are working on a machine learning pipeline using NVIDIA RAPIDS cuML and need to standardize the dataset to ensure that all features have a mean of 0 and a standard deviation of 1.
Which of the following methods should you use to achieve this in cuML?

A. cuml.preprocessing.LabelEncoder()

B. cuml.preprocessing.StandardScaler()

C. cuml.preprocessing.OneHotEncoder()

D. cuml.preprocessing.MinMaxScaler()

Discussion 0

Correct Answer: B Vote an answer

Question #36

You are working on a medium-sized dataset (~500,000 rows, 20 columns) and need to perform fast exploratory data analysis (EDA) with filtering, aggregations, and transformations.
Which of the following Python libraries would be the most efficient choice for this task?

A. Dask

B. Pandas

C. Vaex

D. PySpark

Discussion 0

Correct Answer: B Vote an answer

Question #37

Your data science team is performing exploratory data analysis (EDA) on a large GPU-accelerated environment using cuDF and Dask-cuDF. During analysis, queries on categorical columns are performing poorly.
Which approach will most effectively improve query performance for categorical data in GPU-accelerated DataFrames?

A. Use CPU-based DataFrames (pandas) instead of GPU-based DataFrames

B. Convert categorical columns to integer codes

C. Store data in compressed file formats like Parquet

D. Increase GPU memory allocation

Discussion 0

Correct Answer: B Vote an answer

Question #38

You are conducting rapid experimentation on an NVIDIA GPU to determine the best trade-off between model accuracy and inference latency.
Which approach is the most efficient for systematically evaluating multiple configurations?

A. Train each possible model variation from scratch to evaluate accuracy and performance differences

B. Use automated hyperparameter tuning tools like Optuna or Ray Tune with mixed precision training

C. Reduce training epochs significantly to save time, even if the model is underfitting

D. Test different model configurations on a CPU first before moving to the GPU for final evaluation

Discussion 0

Correct Answer: B Vote an answer

Question #39

A data scientist is working with large-scale ETL (Extract, Transform, Load) pipelines on GPU- accelerated infrastructure using RAPIDS. The workload involves frequent shuffle operations, which significantly impact performance.
What is the best approach using NVIDIA technologies to reduce shuffle overhead and improve performance?

A. Enable CUDA Unified Memory to automatically optimize shuffle performance without manual intervention.

B. Store all intermediate shuffle data in CPU memory using pandas to ensure persistence and reduce GPU load.

C. Use RAPIDS cuML to replace shuffle-intensive operations with an ML model that predicts data distribution.

D. Use RAPIDS cuDF's GPU memory caching to store intermediate DataFrames and avoid redundant shuffle operations.

Discussion 0

Correct Answer: D Vote an answer

Question #40

A data scientist is training a deep learning model on an NVIDIA GPU and wants to profile the model to identify performance bottlenecks. The scientist chooses to use NVIDIA DLProf.
Which of the following steps is the most effective way to profile the model using DLProf?

A. Rely on general CPU profiling tools like perf and gprof to analyze GPU performance.

B. Run the model using dlprof --mode profile to collect performance metrics and generate a report.

C. Use nvprof instead of DLProf since it provides more detailed profiling for deep learning workloads.

D. Modify the training script to manually insert timing functions for each layer and compare execution times.

Discussion 0

Correct Answer: B Vote an answer

Question #41

You are deploying an NVIDIA GPU-accelerated machine learning model in a Docker container and want to ensure that your application can leverage the GPU efficiently.
What is the best way to manage CUDA dependencies and avoid compatibility issues inside your Docker container?

A. Use a base Ubuntu image and install TensorFlow, PyTorch, and CUDA using pip install inside the container.

B. Use NVIDIA's official Docker images from NVIDIA GPU Cloud (NGC), which come with pre-installed CUDA and AI frameworks.

C. Disable GPU acceleration in Docker and force computations on the CPU to avoid CUDA compatibility issues.

D. Manually install CUDA and cuDNN inside the container by downloading them from NVIDIA's website and setting environment variables.

Discussion 0

Correct Answer: B Vote an answer

Question #42

You are building a fraud detection system and have a dataset that includes a feature representing transaction amounts. The values range from a few cents to several thousand dollars.
What is the most appropriate data type for this feature?

A. Boolean (True for transactions above $100, False otherwise).

B. Integer (rounding all transaction amounts to whole dollars).

C. Floating-point (with precision to store cents or fractions of a cent).

D. Categorical (grouping transactions into bins such as "low", "medium", "high").

Discussion 0

Correct Answer: C Vote an answer

Question #43

When comparing the required memory with the available memory on a GPU for an MLOps deployment using NVIDIA technologies, which of the following is the best method to optimize memory usage while training large models?

A. Use mixed-precision training to reduce memory requirements by using half-precision floating-point numbers.

B. Use a higher number of GPUs to distribute the model and memory load across the GPUs.

C. Increase the input data size to fully utilize available memory and improve training performance.

D. Decrease the number of training iterations to reduce memory consumption.

Discussion 0

Correct Answer: A Vote an answer

Question #44

When utilizing GPU instances in a cloud environment for data science, which of the following are common considerations? (Select two)

A. GPUs are suitable for both training and inference tasks but not for preprocessing

B. Cost-effective GPU instances always provide the best performance

C. Cloud providers offer dedicated GPU instances for high-performance needs

D. GPU performance can be degraded by network latency in distributed training

Discussion 0

Correct Answer: C,D Vote an answer

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