Domain 4: Unsupervised Learning with SAS

SAS AI and Machine Learning · 46 questions

• A data scientist runs PROC HPCLUS in SAS to perform k-means clustering on customer data. Which option specifies the maximum number of iterations for the algorithm to converge?
• When comparing k-means clustering and hierarchical clustering for a dataset with 500,000 observations in SAS, which statement is most accurate?
• A data scientist wants to reduce the dimensionality of a dataset with 50 numeric variables before clustering. In SAS, which procedure is most commonly used to perform Principal Component Analysis?
• In PROC HPCLUS, a data scientist wants SAS to automatically determine the optimal number of clusters. Which option enables this capability?
• A data scientist uses PROC HPDS2 in SAS Viya to score new data against an anomaly detection model. What is the primary role of PROC HPDS2 in this workflow?
• A data scientist applies the elbow method to choose k for PROC HPCLUS. The within-cluster sum of squares (WCSS) values are: k=2: 1200, k=3: 700, k=4: 500, k=5: 480, k=6: 465. Which k value does the elbow method suggest?
• A data scientist is evaluating whether to use PROC HPCLUS or PROC CLUSTER for a dataset with 2 million rows. Which statement best justifies the choice of PROC HPCLUS?
• A data scientist applies PCA via PROC PRINCOMP and retains components that explain at least 80% of total variance. The cumulative proportion output shows components 1-4 explain 82% of variance. How many components should be retained?
• In PROC HPCLUS, which initialization method assigns initial cluster seeds by selecting observations that are maximally separated from each other?
• A data scientist applies hierarchical clustering using PROC CLUSTER with METHOD=WARD. What does Ward's linkage criterion minimize at each merge step?
• A data scientist runs PROC FACTOR with METHOD=PRINCIPAL ROTATE=VARIMAX on a 20-variable dataset. What is the purpose of ROTATE=VARIMAX?
• In SAS, a data scientist applies PROC HPCLUS to segment customers and wants to profile each cluster by computing mean values of key variables. Which post-clustering approach accomplishes this most efficiently?
• A data engineer uses the t-SNE algorithm output from SAS to visualize high-dimensional customer segments. What is a key limitation of t-SNE that the engineer should communicate to stakeholders?
• A SAS analyst uses PROC HPCLUS to cluster 1 million customers. After obtaining cluster assignments, they want to score new customers arriving each week using the cluster centroids. Which approach is most appropriate?
• A data scientist applies PROC FACTOR with NFACTORS=4 and uses the SCREE plot output to decide the final number of factors. The eigenvalues are: Factor 1: 5.2, Factor 2: 3.1, Factor 3: 1.8, Factor 4: 1.1, Factor 5: 0.4. Using the Kaiser criterion (eigenvalue > 1), how many factors should be retained?
• A data scientist applies PROC HPCLUS and obtains cluster centroids. They want to interpret which input variables drive the most separation between clusters. Which approach best identifies the most discriminating variables?
• A data scientist applies PROC HPCLUS to 800,000 observations with k=8 clusters and converges in 15 iterations. The data scientist then wants to understand within-cluster variability. Which statistic output by PROC HPCLUS directly measures within-cluster variability?
• A data scientist uses PROC PRINCOMP in SAS with OUTSTAT= to save the component structure. Later, they want to score new data by projecting it onto the saved principal components. Which step applies the saved PCA transformation to new data?
• In a SAS k-means clustering workflow using PROC HPCLUS, the pseudo-F statistic (Calinski-Harabasz index) is computed across k=2 through k=10. The pseudo-F values are: k=3: 482, k=4: 716, k=5: 698, k=6: 534. Which k value does the pseudo-F criterion suggest?
• A data scientist runs PROC CLUSTER with METHOD=AVERAGE (unweighted average linkage) and then uses PROC TREE to create cluster assignments. What does average linkage use to compute the distance between two clusters?
• A data scientist builds an anomaly detection pipeline in SAS Viya. They want to flag observations whose Mahalanobis distance from the cluster center exceeds a threshold. Which SAS procedure or action set is most appropriate for computing Mahalanobis distances?
• A data scientist applies PROC HPCLUS with k=5 to customer transaction data. After initial cluster assignment, the data scientist suspects cluster 3 contains outliers. Which SAS approach best identifies outlier observations within a cluster?
• A data scientist runs PROC FACTOR and examines the communality estimates in the output. What does a communality value of 0.25 for a variable named 'age' indicate?
• After running PROC HPCLUS with NCLUST=4, two clusters appear very similar. Which metric best quantifies the separation between these two specific clusters?
• PROC FACTOR is run with ROTATE=VARIMAX. What is the purpose of Varimax rotation?
• Which PROC PRINCOMP option saves the principal component scores for each observation to a SAS dataset?
• PROC STDIZE is run with METHOD=MIDRANGE before clustering. What transformation does this apply?
• In hierarchical clustering with PROC CLUSTER METHOD=COMPLETE, how is the between-cluster distance defined?
• What advantage does PROC FASTCLUS offer for very large datasets compared to PROC HPCLUS?
• In isolation forests for anomaly detection, why are anomalies isolated in fewer random splits than normal points?
• A SAS PROC VARCLUS output shows that a cluster has R-squared with its own component = 0.45 but next-nearest cluster R-squared = 0.48. What does this imply?
• A researcher uses Gower distance for clustering a mixed dataset. Which PROC DISTANCE option computes Gower dissimilarity in SAS?
• The gap statistic is used to select the optimal number of clusters k. What is the reference distribution against which the observed log(WSS) is compared?
• How does fuzzy C-means clustering differ from k-means in its representation of cluster membership?
• After running PROC CLUSTER METHOD=WARD, a dendrogram is produced. Which PROC TREE option cuts the dendrogram at a specific number of clusters?
• A SAS analyst runs PROC PRINCOMP and requests the first 3 principal components. The cumulative variance explained is 45%, 62%, and 74%. Which statement about the 3rd principal component is correct?
• PROC CLUSTER with METHOD=AVERAGE (average linkage) computes the distance between two clusters being merged. How is this distance defined under average linkage?
• PROC DISTANCE with METHOD=MAHALANOBIS requires an additional option to compute Mahalanobis distance. What does Mahalanobis distance account for that Euclidean distance does not?
• PROC FASTCLUS is run on a 5-million-row dataset with MAXCLUSTERS=8 and MAXITER=20. Which characteristic makes PROC FASTCLUS more efficient than PROC HPCLUS for very large datasets when a fixed number of clusters is required?
• PROC ACECLUS is run as a preprocessing step before PROC CLUSTER. What does PROC ACECLUS estimate that improves subsequent clustering?
• PROC MODECLUS is applied to a multivariate dataset. What type of clustering algorithm does PROC MODECLUS implement?
• A data scientist uses PROC DISTANCE with METHOD=EUCLID on a dataset where predictors have very different scales (e.g., age: 18-80, income: 20000-500000). What problem does this create for clustering?
• PROC HPCLUS is run with NCLUST=5 on a 10-million-row dataset. The IMPUTE option is specified. What does IMPUTE do in PROC HPCLUS?
• A data scientist runs PROC PRINCOMP and uses the OUTSTAT= option. What information does the OUTSTAT= dataset contain?
• An isolation forest is trained to detect anomalies in a network traffic dataset. An observation receives an anomaly score close to 1.0. What does this score indicate?
• A practitioner uses PROC STDIZE with METHOD=RANGE to standardize a variable with min=10 and max=90. After standardization, a raw value of 50 maps to what standardized value?