AWS Certified Machine Learning - Specialty

Last checked October 22, 2026 against the official AWS exam page.

1. Question 1ML Implementation and Operations

   A deployed model's input data distribution changes over time, and the team wants an alert when it drifts. Which SageMaker capability should be used?

   1. ASageMaker Model Monitor, which detects data quality and drift against a baseline and raises alerts
   2. BSageMaker Debugger, which inspects tensors and training issues during a training job rather than in production
   3. CSageMaker Neo, which compiles models for specific hardware targets and does not monitor production data
   4. DSageMaker Edge Manager, which manages models on devices and does not compare live distributions to a baseline
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   Correct answer: A

   Model Monitor compares production data to a baseline and raises drift alerts. Debugger, Neo and Edge Manager do not monitor live distribution drift.

2. Question 2Modeling

   A team trains a model on a time series of sales. Which validation strategy avoids leakage from the future into training?

   1. AA random shuffle of all rows before splitting, so that every period appears in training and testing
   2. BA time-based split, where training data precedes the validation and test periods
   3. CUsing the test set repeatedly to choose hyperparameters, so that the best model is found quickly
   4. DTraining on the full dataset and reporting the accuracy on the same data used for training the model
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   Correct answer: B

   A time-based split keeps the future out of training, which matches how the model will be used. Random shuffles, repeated test use and training-set evaluation each leak information.

3. Question 3Data Engineering

   A company streams clickstream events into Amazon S3 with Kinesis Data Firehose. Data scientists need the files converted to Parquet and partitioned by date for Athena queries. Which approach fits best?

   1. AStore the events as individual JSON files in one flat prefix and let Athena scan all files for each query
   2. BConfigure Firehose record format conversion to Parquet with an AWS Glue Data Catalog table, and partition the S3 prefix by date
   3. CLoad the events into a single Amazon DynamoDB table and query it with Athena using a federated connector
   4. DWrite a nightly cron job on an EC2 instance that copies the data to a local disk and converts it manually
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   Correct answer: B

   Firehose record format conversion to Parquet with a Glue catalog table, and date-partitioned prefixes, minimises scanned data for Athena. Flat JSON scans everything, DynamoDB is not suited to analytical scans, and manual EC2 conversion adds operational work.

4. Question 4Exploratory Data Analysis

   A dataset has a target class that is only 0.5 percent of the records. Which evaluation approach gives a more meaningful measure of model quality than accuracy?

   1. AThe mean of the training loss over all epochs, which summarises the model's fit to the data
   2. BAccuracy alone, since a model that is correct on most records is always the best model for the business
   3. CPrecision, recall and the F1 score, or the area under the precision-recall curve
   4. DThe number of training records, which indicates how large the dataset is for the learning task
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   Correct answer: C

   With a rare class, precision, recall, F1 and the precision-recall curve reveal performance on the minority class, which accuracy hides. Accuracy, training loss and dataset size do not show minority-class quality.

5. Question 5ML Implementation and Operations

   A team must deploy a new model version to a production endpoint with minimal risk, and roll back quickly if quality drops. Which deployment strategy fits?

   1. AShift a small share of traffic to the new variant, monitor, then increase the share or roll back
   2. BReplace the production model in place with no test traffic, and wait for user complaints to decide on rollback
   3. CDeploy the new model to a separate AWS account only, with no connection to production traffic at all
   4. DStop the endpoint for a day while the new model is copied to it, then start it with the new version
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   Correct answer: A

   Canary or gradual traffic shifting with monitoring allows a fast rollback. In-place replacement, a separate account with no traffic and downtime each carry more risk or no validation.