Practice Exams AWS Machine Learning Speciality Tests 2024

AWS Certified Machine Learning - Specialty (MLS-C01) Exam Preparation

The AWS Certified Machine Learning - Specialty (MLS-C01) exam is designed for data scientists and machine learning engineers who leverage the AWS Cloud for AI/ML development. This certification validates your ability to design, build, deploy, optimize, train, tune, and maintain machine learning solutions to solve real-world business challenges. Demonstrate your expertise in utilizing AWS services to create scalable, cost-effective, reliable, and secure ML solutions.

Key Skills & Domains

The exam assesses your proficiency across these core domains:

• Data Engineering (20%): Focuses on preparing data for machine learning, including data ingestion, transformation, and storage using services like Amazon S3, AWS Glue, and Amazon Kinesis.
• Exploratory Data Analysis (24%): Tests your ability to analyze data to identify patterns and insights, including techniques like tf-idf matrix creation.
• Modeling (36%): Covers various machine learning algorithms and techniques, including model selection, training, and evaluation. Understanding concepts like learning rate and batch size is crucial.
• Machine Learning Implementation and Operations (20%): Evaluates your skills in deploying, monitoring, and maintaining ML models in production using services like Amazon SageMaker.

Sample Questions

Here are a few sample questions to help you prepare for the AWS MLS-C01 exam:

1. Accelerating Model Training: A machine learning team is experiencing slow training times for models on large datasets stored in Amazon S3 using Amazon SageMaker Linear Learner. What's the best way to speed up the training process?

   • A) Use Amazon SageMaker Pipe mode.
   • B) Use Amazon Machine Learning to train the models.
   • C) Use Amazon Kinesis to stream the data to Amazon SageMaker.
   • D) Use AWS Glue to transform the CSV dataset to the JSON format.

2. tf-idf Matrix Dimensions: Given two sentences: "Please call the number below." and "Please do not call us.", what are the dimensions of the tf-idf matrix using unigrams and bigrams?

   • A) (2, 16)
   • B) (2, 8)
   • C) (2, 10)
   • D) (8, 10)

3. Automated Data Transformation: A company needs to automate data transformation and maintain a metadata catalog for datasets in Amazon S3. Which solution offers the least setup and maintenance?

   • A) Create an Amazon EMR cluster with Apache Hive. Then, create a Hive metastore and a script to run transformation jobs on a schedule.
   • B) Create an AWS Glue crawler to populate the AWS Glue Data Catalog. Then, author an AWS Glue ETL job, and set up a schedule for data transformation jobs.
   • C) Create an Amazon EMR cluster with Apache Spark. Then, create an Apache Hive metastore and a script to run transformation jobs on a schedule.
   • D) Create an Amazon SageMaker Jupyter notebook instance that transforms the data. Then, create an Apache Hive metastore and a script to run transformation jobs on a schedule.

4. Optimizing Model Training: During machine learning model training, a data scientist observes that the loss function converges to different, stable values with identical parameters. What should be done to improve the training process?

   • A) Increase the learning rate. Keep the batch size the same.
   • B) Decrease the learning rate. Reduce the batch size.
   • C) Decrease the learning rate. Keep the batch size the same.
   • D) Do not change the learning rate. Increase the batch size.

5. Confusion Matrix Evaluation: A data scientist is evaluating binary classification models where false positives are 5 times more costly than false negatives. The models must have at least 80% recall and a false positive rate of 10% or less. Which confusion matrix represents the best model?

   • A) TN = 91, FP = 9 FN = 22, TP = 78
   • B) TN = 99, FP = 1 FN = 21, TP = 79
   • C) TN = 96, FP = 4 FN = 10, TP = 90
   • D) TN = 98, FP = 2 FN = 18, TP