What are the prerequisites for taking the MLS-C01 exam?

AWS recommends that candidates have at least 1-2 years of hands-on experience developing, architecting, or running machine learning workloads on AWS. You should also have foundational knowledge of ML algorithms, AWS services, and data engineering concepts. There are no formal prerequisites, but the exam is designed for professionals with intermediate to advanced AWS and ML experience.

How long is the MLS-C01 exam and what is the passing score?

The exam is 180 minutes (3 hours) long and consists of 65 questions in a mix of multiple-choice and multiple-response formats. The passing score is 750 out of 1000, which means you need to answer approximately 72% of the questions correctly to pass.

What are the main domains covered in the MLS-C01 exam?

The exam covers four main domains: Data Engineering (24%), Exploratory Data Analysis (20%), Modeling (26%), and Machine Learning Implementation and Operations (30%). Each domain tests your ability to design, build, and deploy ML solutions on AWS using services like SageMaker, Lambda, and various data processing tools.

Which AWS services are most important to know for the MLS-C01 exam?

Amazon SageMaker is the most critical service, covering training, tuning, and deploying ML models. You should also be familiar with AWS Glue, Lambda, DynamoDB, S3, CloudWatch, and IAM for a comprehensive understanding of the ML pipeline. Additionally, knowledge of services like Kinesis, EMR, and Redshift is important for data processing and analytics.

How should I prepare for the MLS-C01 certification exam?

AWS recommends using a combination of official training courses, hands-on practice with SageMaker, study guides, and practice exams. Many candidates use resources like A Cloud Guru, Linux Academy, or the official AWS training portal, combined with real-world experience building ML models. Practice exams are particularly valuable for understanding the question format and identifying weak areas.

Free Amazon MLS-C01 Exam Questions

Q1 MultipleChoice

[Modeling]

A financial company is trying to detect credit card fraud. The company observed that, on average, 2% of credit card transactions were fraudulent. A data scientist trained a classifier on a year's worth of credit card transactions dat

a. The model needs to identify the fraudulent transactions (positives) from the regular ones (negatives). The company's goal is to accurately capture as many positives as possible.

Which metrics should the data scientist use to optimize the model? (Choose two.)

Correct Answer: D, E

Q2 MultipleChoice

[Data Engineering]

A credit card company wants to identify fraudulent transactions in real time. A data scientist builds a machine learning model for this purpose. The transactional data is captured and stored in Amazon S3. The historic data is already labeled with two classes: fraud (positive) and fair transactions (negative). The data scientist removes all the missing data and builds a classifier by using the XGBoost algorithm in Amazon SageMaker. The model produces the following results:

* True positive rate (TPR): 0.700

* False negative rate (FNR): 0.300

* True negative rate (TNR): 0.977

* False positive rate (FPR): 0.023

* Overall accuracy: 0.949

Which solution should the data scientist use to improve the performance of the model?

AApply the Synthetic Minority Oversampling Technique (SMOTE) on the minority class in the training dataset. Retrain the model with the updated training data.

BApply the Synthetic Minority Oversampling Technique (SMOTE) on the majority class in the training dataset. Retrain the model with the updated training data.

CUndersample the minority class.

DOversample the majority class.

Correct Answer: A

Q3 MultipleChoice

[Modeling]

A music streaming company is building a pipeline to extract features. The company wants to store the features for offline model training and online inference. The company wants to track feature history and to give the company's data science teams access to the features.

Which solution will meet these requirements with the MOST operational efficiency?

AUse Amazon SageMaker Feature Store to store features for model training and inference. Create an online store for online inference. Create an offline store for model training. Create an 1AM role for data scientists to access and search through feature groups.

BUse Amazon SageMaker Feature Store to store features for model training and inference. Create an online store for both online inference and model training. Create an 1AM role for data scientists to access and search through feature groups.

CCreate one Amazon S3 bucket to store online inference features. Create a second S3 bucket to store offline model training features. Turn onversioning for the S3 buckets and use tags to specify which tags are for online inference features and which are for offline model training features. Use Amazon Athena to query the S3 bucket for online inference. Connect the S3 bucket for offline model training to a SageMaker training job. Create an 1AM policy that allows data scientists to access both buckets.

DCreate two separate Amazon DynamoDB tables to store online inference features and offline model training features. Use time-based versioning on both tables. Query the DynamoDB table for online inference. Move the data from DynamoDB to Amazon S3 when a new SageMaker training job is launched. Create an 1AM policy that allows data scientists to access both tables.

Correct Answer: A

Q4 MultipleChoice

[Data Engineering]

A data scientist has developed a machine learning translation model for English to Japanese by using Amazon SageMaker's built-in seq2seq algorithm with 500,000 aligned sentence pairs. While testing with sample sentences, the data scientist finds that the translation quality is reasonable for an example as short as five words. However, the quality becomes unacceptable if the sentence is 100 words long.

Which action will resolve the problem?

AChange preprocessing to use n-grams.

BAdd more nodes to the recurrent neural network (RNN) than the largest sentence's word count.

CAdjust hyperparameters related to the attention mechanism.

DChoose a different weight initialization type.

Correct Answer: C

Q5 MultipleChoice

[Modeling]

A Machine Learning Specialist needs to create a data repository to hold a large amount of time-based training data for a new model. In the source system, new files are added every hour Throughout a single 24-hour period, the volume of hourly updates will change significantly. The Specialist always wants to train on the last 24 hours of the data

Which type of data repository is the MOST cost-effective solution?

AAn Amazon EBS-backed Amazon EC2 instance with hourly directories

BAn Amazon RDS database with hourly table partitions

CAn Amazon S3 data lake with hourly object prefixes

DAn Amazon EMR cluster with hourly hive partitions on Amazon EBS volumes

Correct Answer: C

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