Snowflake Updated MLA-C01 Exam Questions and Answers by zayyan

Linear regression model whose coefficients should shrink but not become zero

Answer: L2 regularization

AWS says L2 produces smaller overall weight values and is the right fit when coefficients should be reduced without being forced to zero.

Polynomial regression model with irrelevant polynomial terms that should be eliminated

Answer: L1 regularization

AWS says L1 reduces the number of features used by pushing small weights to zero, which matches elimination of irrelevant terms.

Logistic regression model that has highly correlated features to eliminate highly redundant predictors

Answer: L1 regularization

This is the nuanced one. AWS says L2 stabilizes weights when there is high correlation between features, but because the question explicitly says eliminate highly redundant predictors, L1 is the better match since it creates sparsity and removes predictors by zeroing coefficients.

For Amazon EMR, the primary node and core nodes handle the critical functions of the cluster, including data storage (HDFS) and processing. Running them on On-Demand Instances ensures high availability and prevents data loss, as Spot Instances can be interrupted. The task nodes, which handle additional processing but do not store data, can use Spot Instances to reduce costs without compromising the cluster ' s resilience or data integrity. This configuration balances cost-effectiveness and reliability.

Option B is correct because the bank needs a SageMaker-supported algorithm and the resulting model must be explainable for regulators. AWS documentation states that the Amazon SageMaker AI linear learner algorithm is a built-in algorithm that provides a solution for classification and regression problems. Since determining whether customers qualify for a new product is a classification-style decision problem, linear learner fits the business requirement directly.

The explainability requirement strongly favors a simpler and more interpretable model family. AWS Prescriptive Guidance on model interpretability states that simple models are considered more interpretable because the effects of input features on the output are easier to understand. It specifically gives linear regression as an example where the magnitudes of coefficients provide a clear global feature-importance signal. While the question does not explicitly mention SageMaker Clarify, AWS documentation also says SageMaker offers explainability features to help interpret predictions, which complements inherently interpretable model types such as linear models.

The other options are weaker fits. Object2Vec is mainly for learning embeddings and similarity relationships rather than a straightforward explainable eligibility model. A neural network may work technically, but it is generally less interpretable for regulatory review. k-means is an unsupervised clustering algorithm and is not suitable for predicting whether a customer qualifies for a product. Because the bank wants a directly supported SageMaker algorithm and a model that is easier to explain to regulators, the best AWS-aligned answer is B, linear learner.

For real-time anomaly detection on streaming data, AWS recommends using Amazon Managed Service for Apache Flink, which provides serverless, scalable stream processing with built-in ML functions.

Apache Flink includes a native RANDOM_CUT_FOREST (RCF) function for unsupervised anomaly detection. This eliminates the need to deploy, manage, or scale custom ML endpoints. When combined with Amazon Kinesis Data Streams, the solution can process thousands of records per second with very low latency.

Option B introduces multiple services and custom logic, increasing operational overhead. Option C requires managing EC2 infrastructure and Kafka clusters. Option D is batch-oriented and does not meet real-time requirements.

AWS documentation explicitly highlights Kinesis + Managed Flink + RCF as the lowest-overhead solution for real-time anomaly detection.

Therefore, Option A is the correct and AWS-aligned choice.