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MLA-C01 Examcollection Dumps Torrent | High Pass-Rate MLA-C01 100% Correct Answers: AWS Certified Machine Learning Engineer - Associate

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Free PDF Quiz MLA-C01 - Fantastic AWS Certified Machine Learning Engineer - Associate Examcollection Dumps Torrent

All exam questions that contained in our Amazon MLA-C01 study engine you should know are written by our professional specialists with three versions to choose from: the PDF, the Software and the APP online. In case there are any changes happened to the Amazon MLA-C01 Exam, the experts keep close eyes on trends of it and compile new updates constantly.

Amazon MLA-C01 Exam Syllabus Topics:

Topic
Details

Topic 1

Deployment and Orchestration of ML Workflows: This section of the exam measures skills of Forensic Data Analysts and focuses on deploying machine learning models into production environments. It covers choosing the right infrastructure, managing containers, automating scaling, and orchestrating workflows through CI
CD pipelines. Candidates must be able to build and script environments that support consistent deployment and efficient retraining cycles in real-world fraud detection systems.

Topic 2

ML Solution Monitoring, Maintenance, and Security: This section of the exam measures skills of Fraud Examiners and assesses the ability to monitor machine learning models, manage infrastructure costs, and apply security best practices. It includes setting up model performance tracking, detecting drift, and using AWS tools for logging and alerts. Candidates are also tested on configuring access controls, auditing environments, and maintaining compliance in sensitive data environments like financial fraud detection.

Topic 3

Data Preparation for Machine Learning (ML): This section of the exam measures skills of Forensic Data Analysts and covers collecting, storing, and preparing data for machine learning. It focuses on understanding different data formats, ingestion methods, and AWS tools used to process and transform data. Candidates are expected to clean and engineer features, ensure data integrity, and address biases or compliance issues, which are crucial for preparing high-quality datasets in fraud analysis contexts.

Topic 4

ML Model Development: This section of the exam measures skills of Fraud Examiners and covers choosing and training machine learning models to solve business problems such as fraud detection. It includes selecting algorithms, using built-in or custom models, tuning parameters, and evaluating performance with standard metrics. The domain emphasizes refining models to avoid overfitting and maintaining version control to support ongoing investigations and audit trails.

Amazon AWS Certified Machine Learning Engineer - Associate Sample Questions (Q36-Q41):

NEW QUESTION # 36
A company is using an AWS Lambda function to monitor the metrics from an ML model. An ML engineer needs to implement a solution to send an email message when the metrics breach a threshold.
Which solution will meet this requirement?

A. Log the metrics from the Lambda function to Amazon CloudFront. Configure an Amazon CloudWatch alarm to send the email message.
B. Log the metrics from the Lambda function to Amazon CloudWatch. Configure an Amazon CloudFront rule to send the email message.
C. Log the metrics from the Lambda function to Amazon CloudWatch. Configure a CloudWatch alarm to send the email message.
D. Log the metrics from the Lambda function to AWS CloudTrail. Configure a CloudTrail trail to send the email message.

Answer: B

Explanation:
Logging the metrics to Amazon CloudWatch allows the metrics to be tracked and monitored effectively.
CloudWatch Alarms can be configured to trigger when metrics breach a predefined threshold.
The alarm can be set to notify through Amazon Simple Notification Service (SNS), which can send email messages to the configured recipients.
This is the standard and most efficient way to achieve the desired functionality.

NEW QUESTION # 37
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. 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.
B. 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.
C. 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.
D. 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.

Answer: D

Explanation:
Scenario:The ML engineer needs a low-overhead solution to query thousands of existing and new CSV objects stored in Amazon S3 based on a transaction date.
Why Athena?
* Serverless:Amazon Athena is a serverless query service that allows direct querying of data stored in S3 using standard SQL, reducing operational overhead.
* Ease of Use:By using the CTAS statement, the engineer can create a table with optimized partitions based on the transaction date. Partitioning improves query performance and minimizes costs by scanning only relevant data.
* Low Operational Overhead:No need to manage or provision additional infrastructure. Athena integrates seamlessly with S3, and CTAS simplifies table creation and optimization.
Steps to Implement:
* Organize Data in S3:Store CSV files in a bucket in a consistent format and directory structure if possible.
* Configure Athena:Use the AWS Management Console or Athena CLI to set up Athena to point to the S3 bucket.
* Run CTAS Statement:
CREATE TABLE processed_data
WITH (
format = 'PARQUET',
external_location = 's3://processed-bucket/',
partitioned_by = ARRAY['transaction_date']
) AS
SELECT *
FROM input_data;
This creates a new table with data partitioned by transaction date.
* Query the Data:Use standard SQL queries to fetch data based on the transaction date.
References:
* Amazon Athena CTAS Documentation
* Partitioning Data in Athena

NEW QUESTION # 38
A company stores historical data in .csv files in Amazon S3. Only some of the rows and columns in the .csv files are populated. The columns are not labeled. An ML engineer needs to prepare and store the data so that the company can use the data to train ML models.
Select and order the correct steps from the following list to perform this task. Each step should be selected one time or not at all. (Select and order three.)
* Create an Amazon SageMaker batch transform job for data cleaning and feature engineering.
* Store the resulting data back in Amazon S3.
* Use Amazon Athena to infer the schemas and available columns.
* Use AWS Glue crawlers to infer the schemas and available columns.
* Use AWS Glue DataBrew for data cleaning and feature engineering.

Answer:

Explanation:

Explanation:
Step 1: Use AWS Glue crawlers to infer the schemas and available columns.Step 2: Use AWS Glue DataBrew for data cleaning and feature engineering.Step 3: Store the resulting data back in Amazon S3.
* Step 1: Use AWS Glue Crawlers to Infer Schemas and Available Columns
* Why?The data is stored in .csv files with unlabeled columns, and Glue Crawlers can scan the raw data in Amazon S3 to automatically infer the schema, including available columns, data types, and any missing or incomplete entries.
* How?Configure AWS Glue Crawlers to point to the S3 bucket containing the .csv files, and run the crawler to extract metadata. The crawler creates a schema in the AWS Glue Data Catalog, which can then be used for subsequent transformations.
* Step 2: Use AWS Glue DataBrew for Data Cleaning and Feature Engineering
* Why?Glue DataBrew is a visual data preparation tool that allows for comprehensive cleaning and transformation of data. It supports imputation of missing values, renaming columns, feature engineering, and more without requiring extensive coding.
* How?Use Glue DataBrew to connect to the inferred schema from Step 1 and perform data cleaning and feature engineering tasks like filling in missing rows/columns, renaming unlabeled columns, and creating derived features.
* Step 3: Store the Resulting Data Back in Amazon S3
* Why?After cleaning and preparing the data, it needs to be saved back to Amazon S3 so that it can be used for training machine learning models.
* How?Configure Glue DataBrew to export the cleaned data to a specific S3 bucket location. This ensures the processed data is readily accessible for ML workflows.
Order Summary:
* Use AWS Glue crawlers to infer schemas and available columns.
* Use AWS Glue DataBrew for data cleaning and feature engineering.
* Store the resulting data back in Amazon S3.
This workflow ensures that the data is prepared efficiently for ML model training while leveraging AWS services for automation and scalability.

NEW QUESTION # 39
A company uses Amazon Athena to query a dataset in Amazon S3. The dataset has a target variable that the company wants to predict.
The company needs to use the dataset in a solution to determine if a model can predict the target variable.
Which solution will provide this information with the LEAST development effort?

A. Create a new model by using Amazon SageMaker Autopilot. Report the model's achieved performance.
B. Select a model from Amazon Bedrock. Tune the model with the data. Report the model's achieved performance.
C. Configure Amazon Macie to analyze the dataset and to create a model. Report the model's achieved performance.
D. Implement custom scripts to perform data pre-processing, multiple linear regression, and performance evaluation. Run the scripts on Amazon EC2 instances.

Answer: A

Explanation:
Amazon SageMaker Autopilot automates the process of building, training, and tuning machine learning models. It provides insights into whether the target variable can be effectively predicted by evaluating the model's performance metrics. This solution requires minimal development effort as SageMaker Autopilot handles data preprocessing, algorithm selection, and hyperparameter optimization automatically, making it the most efficient choice for this scenario.

NEW QUESTION # 40
A company is creating an application that will recommend products for customers to purchase. The application will make API calls to Amazon Q Business. The company must ensure that responses from Amazon Q Business do not include the name of the company's main competitor.
Which solution will meet this requirement?

A. Configure an Amazon Kendra retriever for Amazon Q Business to build indexes that exclude the competitor's name.
B. Configure an Amazon Q Business retriever to exclude the competitor's name.
C. Configure the competitor's name as a blocked phrase in Amazon Q Business.
D. Configure document attribute boosting in Amazon Q Business to deprioritize the competitor's name.

Answer: C

Explanation:
Amazon Q Business allows configuring blocked phrases to exclude specific terms or phrases from the responses. By adding the competitor's name as a blocked phrase, the company can ensure that it will not appear in the API responses, meeting the requirement efficiently with minimal configuration.

NEW QUESTION # 41
......

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