Free Microsoft 70-774 Exam Practice Questions & Explanations
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You have a trained model in Azure Machine Learning Studio that scores customer transactions for fraud. Every night a scheduled Azure Data Factory pipeline must submit a 2 GB CSV file of transactions stored in Azure Blob storage and write the scored results back to blob storage. Latency for an individual record is not important, but the solution must handle the full file in one submission.
What should you do?
The Batch Execution Service is designed for asynchronous, high-volume scoring. You submit a job that references input data in Azure Blob storage and specify a blob output location; the service scores the whole file and writes results back, which is ideal for a nightly Data Factory-driven pipeline where per-record latency does not matter.
RRS is intended for low-latency, single-row (or small batch) synchronous scoring and calling it millions of times is inefficient and costly. You must publish the predictive experiment (with Web Service Input/Output modules and the saved trained model), not the training experiment. Manually running an experiment each night is not an automatable deployment.
You trained a Two-Class Decision Forest to predict loan default. Training accuracy is 0.98 but accuracy on a held-out test set is 0.71. The dataset is imbalanced, with only 9 percent defaults. You need to obtain a more reliable estimate of generalization performance and a metric that is not misleading under class imbalance.
What should you do?
Cross Validate Model partitions the data into 10 folds, trains and scores on each fold, and reports per-fold plus mean and standard deviation of metrics. This gives a far more stable estimate of generalization than a single split and helps confirm the overfitting indicated by the training/test gap. AUC (area under the ROC curve) is threshold independent and far less misleading than accuracy when 91 percent of rows are the negative class.
Adding trees increases model capacity and typically worsens overfitting. A 95 percent training split leaves a tiny, high-variance test set. Permutation Feature Importance explains feature contribution, and RMSE is a regression metric, not appropriate for binary classification.
A manufacturing company streams sensor telemetry from turbines. Fewer than 0.2 percent of readings correspond to a known failure, and historical labels for failures are essentially unavailable. The data science team must flag readings that deviate strongly from normal operating behavior so engineers can investigate.
Which algorithm should you select in Azure Machine Learning?
One-Class Support Vector Machine is the anomaly detection algorithm in Azure Machine Learning designed for exactly this case: it is trained on data representing normal behavior and flags observations that fall outside the learned boundary, so labeled failure examples are not required.
Two-Class Boosted Decision Tree and Multiclass Logistic Regression are supervised and require a meaningful labeled set, which does not exist here and would be crippled by the extreme class imbalance. K-Means with 2 centroids partitions all data into two groups of comparable structure and is not an anomaly detector; a rare 0.2 percent event would simply be absorbed into a nearby cluster.
You are building a model that predicts hotel booking cancellations. The raw data contains a single free-text column with customer review comments, plus a categorical column named Country that has 180 distinct values, and a highly skewed numeric column named LeadTimeDays.
You must convert the review text into numeric features that weight terms by how distinctive they are across documents, reduce the dimensionality of Country for a linear model, and reduce the skew of LeadTimeDays.
Which three actions should you perform?
Extract N-Gram Features from Text with TF-IDF weighting (or Feature Hashing) converts free text into numeric vectors where terms are weighted by inverse document frequency, exactly matching the requirement. Grouping rare country levels into an Other bucket before indicator encoding controls the dimensionality explosion caused by 180 categories in a linear model. Apply Math Operation with a natural log (Ln) reduces right skew in LeadTimeDays.
Convert to Indicator Values is for categorical columns, not free text, and would create one column per unique review. Min-Max normalization rescales values but does not change the shape of a skewed distribution.
You are exploring a customer churn dataset in Azure Machine Learning Studio. The dataset has 40 numeric columns and 250,000 rows. Before modeling, you must identify which numeric features are highly correlated with each other so you can remove redundancy, and you also need the mean, median, min, max, missing value count and unique value count for every column.
Which combination of modules should you use?
Summarize Data produces a row per column containing count, missing value count, unique value count, min, max, mean, median, standard deviation and other descriptive statistics, which satisfies the profiling requirement. Compute Linear Correlation returns a Pearson correlation matrix across all numeric columns, letting you spot redundant highly correlated pairs.
Apply SQL Transformation could theoretically compute statistics but is not the designed profiling module, and Filter Based Feature Selection scores features against a label, not against each other. Edit Metadata and Normalize Data change column attributes and scale, not statistics. Clean Missing Data plus PCA transforms the data rather than reporting correlations, and PCA gives components, not pairwise correlation values.