Detecting anomalies in time-series datasets: Practice Questions — Data Analysis (NVIDIA-Certified Professional: Accelerated Data Science)

Practice Questions: Detecting Anomalies in Time-Series Datasets

These multiple-choice questions are designed to help candidates prepare for the NVIDIA-Certified Professional: Accelerated Data Science exam, focusing on the detection of anomalies in time-series datasets. Each question includes four options, the correct answer, and a brief explanation.

1. Which of the following methods is most appropriate for detecting sudden spikes or drops in a univariate time-series dataset?

   • A. Principal Component Analysis (PCA)
   • B. Moving Average with Thresholding
   • C. K-Means Clustering
   • D. Linear Regression

   Correct Answer: B

   Explanation: Moving average smoothing combined with thresholding is commonly used to identify sudden deviations (spikes or drops) in time-series data by comparing current values against a smoothed baseline.

2. When using NVIDIA RAPIDS cuML for anomaly detection in time-series data, which algorithm is specifically designed to handle temporal dependencies?

   • A. Isolation Forest
   • B. ARIMA (AutoRegressive Integrated Moving Average)
   • C. DBSCAN
   • D. PCA

   Correct Answer: B

   Explanation: ARIMA models explicitly capture temporal dependencies and are widely used for forecasting and anomaly detection in time-series datasets.

3. In the context of time-series anomaly detection, what is the primary purpose of using a rolling window technique?

   • A. To reduce dimensionality of the dataset
   • B. To calculate statistics that adapt over time for dynamic thresholding
   • C. To cluster data points into groups
   • D. To visualize data in a scatter plot

   Correct Answer: B

   Explanation: Rolling windows compute statistics such as mean and standard deviation over a moving subset of data, enabling dynamic thresholds that adapt to changing data behavior.

4. Which metric is most suitable for evaluating the performance of an anomaly detection model on imbalanced time-series data?

   • A. Accuracy
   • B. Precision-Recall AUC
   • C. Mean Squared Error
   • D. R-squared

   Correct Answer: B

   Explanation: Precision-Recall AUC is preferred for imbalanced datasets because it focuses on the model’s ability to correctly identify anomalies (rare events) without being biased by the majority normal class.

5. Which of the following is a common challenge when detecting anomalies in multivariate time-series data using cuGraph?

   • A. Handling missing data points
   • B. Visualizing temporal patterns
   • C. Computing eigenvalues
   • D. Applying linear regression

   Correct Answer: A

   Explanation: Missing data points can disrupt graph construction and analysis in multivariate time-series; preprocessing steps to handle missing values are essential before using cuGraph for anomaly detection.

6. Which visualization technique is most effective for identifying seasonal anomalies in time-series data?

   • A. Scatter plot
   • B. Heatmap of time vs. value
   • C. Box plot grouped by season or month
   • D. Histogram

   Correct Answer: C

   Explanation: Box plots grouped by season or month highlight distribution changes and outliers in seasonal patterns, making them effective for detecting seasonal anomalies.

7. In anomaly detection workflows accelerated by NVIDIA GPUs, which library is primarily used for scalable graph-based analysis of time-series data?

   • A. cuDF
   • B. cuML
   • C. cuGraph
   • D. RAPIDS AI

   Correct Answer: C

   Explanation: cuGraph is the RAPIDS library specialized for GPU-accelerated graph analytics, enabling efficient evaluation of graph-structured time-series data for anomaly detection.

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