AWS Certified ML Engineer Associate : MLA-C01 Practice Tests

AWS Certified ML Engineer Associate : MLA-C01 Practice Tests free download

Prepare confidently with latest questions on AWS MLA-C01 exam. Detailed explanations provided for all answer options.

** This is the ONLY course you need to ace the MLA-C01 exam in the first attempt **

May 18th, 2025: 18 NEW questions added to the course.

Nov 16th, 2024: 17 NEW questions added to the course. 

Welcome to the AWS Certified Machine Learning Associate MLA-C01 - Practice Test Course!

Are you preparing for the AWS MLA-C01 certification exam? This course is designed to help you succeed by providing high-quality practice tests that closely mirror the real exam.

What You'll Get:

• 130 Latest exam questions with detailed explanations to each answer
  

• Realistic Exam Simulation: My practice tests are designed to reflect the format, style, and difficulty of the official AWS Certified Machine Learning Engineer exam. This ensures you get a realistic testing experience.
  

• Comprehensive Coverage: The practice tests cover all the domains and objectives of the MLA-C01 exam:

  • Domain 1: Data Preparation for Machine Learning (ML)

  • Domain 2: ML Model Development

  • Domain 3: Deployment and Orchestration of ML Workflows

  • Domain 4: ML Solution Monitoring, Maintenance, and Security
    

• Detailed Explanations: Each question comes with a detailed explanation to help you understand the concepts and reasoning behind the correct answers. This is crucial for deepening your knowledge and ensuring you're fully prepared. For each question, I have explained why an answer is correct and have also explained why other options are incorrect. You will also find supporting reference links for a quick read.
  

• Variety of Questions: You'll find a mix of multiple-choice, multiple-response, and scenario-based questions to fully prepare you for what to expect on exam day.
  

• Performance Tracking: Keep track of your progress with the test review feature. Identify your strengths and areas for improvement to focus your study efforts effectively.

Sneak peak into what you will get inside the course:

Q1:

A company has deployed an XGBoost prediction model in production to predict if a customer is likely to cancel a subscription. The company uses Amazon SageMaker Model Monitor to detect deviations in the F1 score.

During a baseline analysis of model quality, the company recorded a threshold for the F1 score. After several months of no change, the model’s F1 score decreases significantly.

What could be the reason for the reduced F1 score?

• A. Concept drift occurred in the underlying customer data that was used for predictions.

• B. The model was not sufficiently complex to capture all the patterns in the original baseline data.

• C. The original baseline data had a data quality issue of missing values.

• D. Incorrect ground truth labels were provided to Model Monitor during the calculation of the baseline.

Option A is CORRECT because a significant decrease in the F1 score over time is often attributed to concept drift. Concept drift occurs when the statistical properties of the target variable change over time, leading to a model's predictions becoming less accurate. This means that the patterns or relationships the model learned during training no longer apply to the new data, resulting in a decline in performance metrics like the F1 score.

Example:

Scenario:

A company has deployed an XGBoost model in production to predict whether a customer is likely to cancel their subscription. The model was trained on historical customer data, which included features such as the number of support tickets a customer raised, their usage frequency, and the duration of their subscription. The company used Amazon SageMaker Model Monitor to keep an eye on the model’s F1 score, which was recorded as part of a baseline during the model’s initial deployment.

Initial Model Performance:

Initially, the model performed well, achieving a high F1 score. This score indicates a good balance between precision (correctly predicting customers who will cancel) and recall (identifying most customers who will cancel). The baseline F1 score served as a reference point for future performance monitoring.

Concept Drift Example:

After several months, the company notices a significant drop in the F1 score. What could have happened?

• Change in Customer Behavior: Suppose during the initial training phase, the main predictor for cancellation was "low usage frequency." The model learned that customers who seldom used the service were likely to cancel. Over time, the company introduced new features, promotions, or services that significantly increased customer engagement across the board. As a result, even customers who previously had low usage frequency are now more engaged and less likely to cancel. This change in behavior is known as concept drift—the underlying patterns in the data that the model relies on have shifted, leading to inaccurate predictions and a drop in the F1 score.

• Impact on Model Performance: Due to concept drift, the model continues to weigh "low usage frequency" heavily, but this feature no longer correlates with cancellation as strongly as it did before. The model might now incorrectly predict that engaged customers will cancel, lowering its precision and recall, and thus, the F1 score.

Option B is INCORRECT because if the model was not sufficiently complex to capture the patterns in the original data, it would have shown poor performance from the beginning, rather than experiencing a significant drop in the F1 score after several months.

Option C is INCORRECT because data quality issues, such as missing values in the original baseline data, would likely have caused problems from the outset. These issues would not cause a sudden decline in the F1 score after a period of stability.

Option D is INCORRECT because providing incorrect ground truth labels during the baseline calculation would have resulted in an inaccurate baseline metric from the start, rather than causing a gradual or sudden decline in the F1 score after months of consistent performance.

Q2:

An ML engineer needs to process thousands of existing CSV objects and new CSV objects that are uploaded. The CSV objects are stored in a central Amazon S3 bucket and have the same number of columns. One of the columns is a transaction date. The ML engineer must query the data based on the transaction date.

Which solution will meet these requirements with the LEAST operational overhead?

• A. Use an Amazon Athena CREATE TABLE AS SELECT (CTAS) statement to create a table based on the transaction date from data in the central S3 bucket. Query the objects from the table.

• B. Create a new S3 bucket for processed data. Set up S3 replication from the central S3 bucket to the new S3 bucket. Use S3 Object Lambda to query the objects based on transaction date.

• C. Create a new S3 bucket for processed data. Use AWS Glue for Apache Spark to create a job to query the CSV objects based on transaction date. Configure the job to store the results in the new S3 bucket. Query the objects from the new S3 bucket.

• D. Create a new S3 bucket for processed data. Use Amazon Data Firehose to transfer the data from the central S3 bucket to the new S3 bucket. Configure Firehose to run an AWS Lambda function to query the data based on transaction date.

Option A is CORRECT because using Amazon Athena with a CREATE TABLE AS SELECT (CTAS) statement allows the ML engineer to create a table based on the transaction date from the data in the central S3 bucket. Athena supports querying data in S3 with minimal operational overhead, and by using SQL-like queries, the ML engineer can easily filter the CSV objects based on the transaction date. This solution avoids the need to move or replicate data and provides a serverless, low-maintenance way to query data directly in S3.

Example Scenario:

Suppose you have a central S3 bucket named s3://my-transaction-data/ where thousands of CSV files are stored. Each CSV file has the following columns: transaction_id, customer_id, transaction_date, and amount.

You want to query these files based on the transaction_date column to find transactions that occurred on a specific date.

Step 1: Create an External Table in Athena

First, you would create an external table in Athena that points to your CSV files in S3.

CREATE EXTERNAL TABLE IF NOT EXISTS transaction_data (

transaction_id STRING,

customer_id STRING,

transaction_date STRING,

amount DOUBLE

)

ROW FORMAT SERDE 'org.apache.hadoop.hive.serde2.OpenCSVSerde'

WITH SERDEPROPERTIES (

'separatorChar' = ',',

'quoteChar' = '"'

)

LOCATION 's3://my-transaction-data/'

TBLPROPERTIES ('has_encrypted_data'='false');

This statement creates a table transaction_data that maps to the CSV files in your S3 bucket. Athena understands the schema of your CSV files and can now query them.

Step 2: Query the Data Using CTAS

Next, you can use a CREATE TABLE AS SELECT (CTAS) statement to create a new table with only the data you are interested in, such as transactions from a specific date.

CREATE TABLE transactions_on_date AS

SELECT

transaction_id,

customer_id,

transaction_date,

amount

FROM

transaction_data

WHERE

transaction_date = '2024-09-01';

This query filters the data to include only the rows where the transaction_date is 2024-09-01 and stores the result in a new table transactions_on_date within Athena.

Step 3: Query the New Table

You can now query the transactions_on_date table directly:

SELECT * FROM transactions_on_date;

Benefits:

• No Data Movement: The data remains in S3, and Athena reads directly from it.

• Low Operational Overhead: You don't need to manage servers or data pipelines; Athena handles the query execution.

• Scalability: Athena is serverless and scales automatically to handle large datasets.
  

Example Output:

Assuming the original data looks like this:

transaction_id      customer_id      transaction_date         amount

1                                 101                         2024-09-01                  100.00

2                                102                        2024-09-02                 150.00

3                                103                        2024-09-01                  200.00

The transactions_on_date table will have:

transaction_id      customer_id      transaction_date         amount

1                                 101                         2024-09-01                  100.00

3                                103                        2024-09-01                  200.00

This table contains only the transactions from 2024-09-01.

Option B is INCORRECT because S3 replication and S3 Object Lambda are unnecessary for querying data. S3 Object Lambda is used to modify and process data as it is retrieved from S3, which adds complexity and overhead when Athena can handle the query directly.

Option C is INCORRECT because setting up AWS Glue with Apache Spark jobs introduces unnecessary complexity and operational overhead for a task that can be done directly with Amazon Athena. Glue is better suited for more complex ETL processes, while Athena is more efficient for querying structured data in S3.

Option D is INCORRECT because using Amazon Data Firehose and AWS Lambda to process and query the data adds extra layers of complexity and does not provide the simplest or most efficient solution for querying data based on a specific column like transaction date.

Q3:

An ML engineer needs to create data ingestion pipelines and ML model deployment pipelines on AWS. All the raw data is stored in Amazon S3 buckets.

Which solution will meet these requirements?

• A. Use Amazon Data Firehose to create the data ingestion pipelines. Use Amazon SageMaker Studio Classic to create the model deployment pipelines.

• B. Use AWS Glue to create the data ingestion pipelines. Use Amazon SageMaker Studio Classic to create the model deployment pipelines.

• C. Use Amazon Redshift ML to create the data ingestion pipelines. Use Amazon SageMaker Studio Classic to create the model deployment pipelines.

• D. Use Amazon Athena to create the data ingestion pipelines. Use an Amazon SageMaker notebook to create the model deployment pipelines.

Option B is CORRECT because using AWS Glue to create data ingestion pipelines is a common and efficient approach for processing and transforming raw data stored in Amazon S3. AWS Glue is a fully managed ETL (Extract, Transform, Load) service that simplifies data preparation tasks. For ML model deployment pipelines, Amazon SageMaker Studio Classic provides an integrated development environment (IDE) that makes it easy to build, train, and deploy machine learning models, ensuring a seamless workflow from data ingestion to model deployment.

Option A is INCORRECT because Amazon Kinesis Data Firehose is primarily used for real-time data streaming to destinations like S3, Redshift, and Elasticsearch, not for comprehensive data ingestion pipelines that involve complex ETL processes. SageMaker Studio Classic is suitable for model deployment, but the data ingestion part is better handled by AWS Glue.

Option C is INCORRECT because Amazon Redshift ML is designed for running machine learning models directly in the Redshift data warehouse environment, not for building data ingestion pipelines. Also, Redshift ML is not suitable for handling raw data directly from S3 in the context of creating ingestion pipelines.

Option D is INCORRECT because Amazon Athena is a query service for analyzing data in S3 using standard SQL, but it is not designed to create full-fledged data ingestion pipelines. Additionally, while SageMaker notebooks can be used for model deployment, they do not offer the same level of integrated pipeline management as SageMaker Studio Classic.