AWS Certified Machine Learning - Specialty (MLS-C01) Exam Dumps with Solutions

A smart manufacturing analytics company must ingest machine telemetry from 25,000 CNC machines across 18 countries where each machine sends a record every 20 seconds containing 55 fields with a maximum size of 1 KB, the company already ingests the stream with Amazon Kinesis Data Streams and requires hourly summaries of the ingested records that will be queried in Amazon Athena targeting 8 to 12 of the fields; which solution will meet these requirements with the least amount of customization to transform and store the ingested data?

A data scientist at a mobile gaming company stores in-game purchase records in an Amazon DynamoDB table (12 GB) and ad-impression logs as JSON files in Amazon S3 (6 GB) and needs to prepare a single training dataset by performing a simple join on player_id with up to a 5-minute event timestamp alignment window to train a conversion prediction model in Amazon SageMaker with the least administrative overhead; which approach should be used to perform the necessary transformation?

A data scientist at a fintech startup needs to run a PySpark 3.3 script that performs 7-day and 30-day rolling window aggregations over 2 TB of Parquet data in Amazon S3 to generate training and test datasets and must systematically compare model performance across preprocessing variants by toggling the number of engineered features (e.g., 40, 80, 160) and sample sizes (e.g., 100k, 500k, 1M records), while capturing the parameters used and resulting evaluation metrics for each run without managing a long-running Spark cluster and ensuring outputs are versioned in S3 and easily consumed by downstream SageMaker training; which approach should the data scientist use to determine the ideal data transformations for the model?

A digital media startup stores 3 TB of structured CSV subscription records and 1 TB of semi-structured JSON clickstream logs in Amazon S3 under hourly partitions (s3://company-data/{csv,logs}/dt=YYYY-MM-DD-HH/), and an ML specialist wants to run ad hoc ANSI SQL queries across both datasets within the next day with the least operational effort, no servers to manage, and pay-per-query—what is the simplest solution to query this data?

A ride-sharing platform plans to build a driver-risk classification model to predict whether a newly onboarded driver will have a safety incident within the first 30 days, and after joining logs from 12 internal/external sources they now have about 8,200 raw attributes where many feature pairs show Pearson correlation > 0.95, baseline training of a gradient-boosted classifier on all features takes over 3 hours per run with clear overfitting (training AUC 0.96 vs. validation AUC 0.79), and the team wants to significantly speed up training without losing much information from the original dataset; which feature engineering technique should they use to meet this objective?