Michael Johnson Michael Johnson's Profile Page

Michael Johnson Michael Johnson

0 Course Enrolled • 0 Course Completed

Biography

Pass Guaranteed Quiz MLA-C01 - AWS Certified Machine Learning Engineer - Associate Latest Test Testking

Many candidates find the Amazon MLA-C01 exam preparation difficult. They often buy expensive study courses to start their Amazon MLA-C01 certification exam preparation. However, spending a huge amount on such resources is difficult for many Amazon MLA-C01 Exam applicants.

There are three versions of MLA-C01 training materials for the candidate of you, and different versions have different advantages, you can use it in accordance with your own habit. Free update for each version for one year, namely, you don’t need to buy the same version for many times, and the update version will send to you automatically. You will get the latest version of MLA-C01 Training Materials.

>> MLA-C01 Test Testking <<

Valid Dumps MLA-C01 Pdf | MLA-C01 Test Dumps Free

Our MLA-C01 exam questions are supposed to help you pass the exam smoothly. Don't worry about channels to the best MLA-C01 study materials so many exam candidates admire our generosity of offering help for them. Up to now, no one has ever challenged our leading position of this area. The existence of our MLA-C01 learning guide is regarded as in favor of your efficiency of passing the exam.

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

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 3

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 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 (Q78-Q83):

NEW QUESTION # 78
An ML engineer has an Amazon Comprehend custom model in Account A in the us-east-1 Region. The ML engineer needs to copy the model to Account # in the same Region.
Which solution will meet this requirement with the LEAST development effort?

A. Create an AWS Site-to-Site VPN connection between Account A and Account # to transfer the model.
B. Create a resource-based IAM policy. Use the Amazon Comprehend ImportModel API operation to copy the model to Account B.
C. Use Amazon S3 to make a copy of the model. Transfer the copy to Account B.
D. Use AWS DataSync to replicate the model from Account A to Account B.

Answer: B

Explanation:
Amazon Comprehend provides the ImportModel API operation, which allows you to copy a custom model between AWS accounts. By creating a resource-based IAM policy on the model in Account A, you can grant Account B the necessary permissions to access and import the model. This approach requires minimal development effort and is the AWS-recommended method for sharing custom models across accounts.

NEW QUESTION # 79
An ML engineer normalized training data by using min-max normalization in AWS Glue DataBrew. The ML engineer must normalize the production inference data in the same way as the training data before passing the production inference data to the model for predictions.
Which solution will meet this requirement?

A. Keep the min-max normalization statistics from the training set. Use these values to normalize the production samples.
B. Calculate a new set of min-max normalization statistics from a batch of production samples. Use these values to normalize all the production samples.
C. Calculate a new set of min-max normalization statistics from each production sample. Use these values to normalize all the production samples.
D. Apply statistics from a well-known dataset to normalize the production samples.

Answer: A

Explanation:
To ensure consistency between training and inference, themin-max normalization statistics (min and max values)calculated during training must be retained and applied to normalize production inference data. Using the same statistics ensures that the model receives data in the same scale and distribution as it did during training, avoiding discrepancies that could degrade model performance. Calculating new statistics from production data would lead to inconsistent normalization and affect predictions.

NEW QUESTION # 80
An ML engineer needs to use an Amazon EMR cluster to process large volumes of data in batches. Any data loss is unacceptable.
Which instance purchasing option will meet these requirements MOST cost-effectively?

A. Run the primary node on an On-Demand Instance. Run the core nodes and task nodes on Spot Instances.
B. Run the primary node, core nodes, and task nodes on Spot Instances.
C. Run the primary node, core nodes, and task nodes on On-Demand Instances.
D. Run the primary node and core nodes on On-Demand Instances. Run the task nodes on Spot Instances.

Answer: D

Explanation:
For Amazon EMR, the primary node and core nodes handle the critical functions of the cluster, including data storage (HDFS) and processing. Running them on On-Demand Instances ensures high availability and prevents data loss, as Spot Instances can be interrupted. The task nodes, which handle additionalprocessing but do not store data, can use Spot Instances to reduce costs without compromising the cluster's resilience or data integrity. This configuration balances cost-effectiveness and reliability.

NEW QUESTION # 81
A company uses a hybrid cloud environment. A model that is deployed on premises uses data in Amazon 53 to provide customers with a live conversational engine.
The model is using sensitive data. An ML engineer needs to implement a solution to identify and remove the sensitive data.
Which solution will meet these requirements with the LEAST operational overhead?

A. Deploy the model on an Amazon Elastic Container Service (Amazon ECS) cluster that uses AWS Fargate. Create an AWS Batch job to identify and remove the sensitive data.
B. Deploy the model on Amazon SageMaker. Create a set of AWS Lambda functions to identify and remove the sensitive data.
C. Use Amazon Macie to identify the sensitive data. Create a set of AWS Lambda functions to remove the sensitive data.
D. Use Amazon Comprehend to identify the sensitive data. Launch Amazon EC2 instances to remove the sensitive data.

Answer: C

Explanation:
Amazon Macie is a fully managed data security and privacy service that uses machine learning to discover and classify sensitive data in Amazon S3. It is purpose-built to identify sensitive data with minimal operational overhead. After identifying the sensitive data, you can use AWS Lambda functions to automate the process of removing or redacting the sensitive data, ensuring efficiency and integration with the hybrid cloud environment. This solution requires the least development effort and aligns with the requirement to handle sensitive data effectively.

NEW QUESTION # 82
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. The model was not sufficiently complex to capture all the patterns in the original baseline data.
B. The original baseline data had a data quality issue of missing values.
C. Concept drift occurred in the underlying customer data that was used for predictions.
D. Incorrect ground truth labels were provided to Model Monitor during the calculation of the baseline.

Answer: C

Explanation:
* Problem Description:
* The F1 score, which is a balance of precision and recall, has decreased significantly. This indicates the model's predictions are no longer aligned with the real-world data distribution.
* Why Concept Drift?
* Concept driftoccurs when the statistical properties of the target variable or features change over time. For example, customer behaviors or subscription cancellation patterns may have shifted, leading to reduced model accuracy.
* Signs of Concept Drift:
* Deviation in performance metrics (e.g., F1 score) over time.
* Declining prediction accuracy for certain groups or scenarios.
* Solution:
* Monitor for drift using tools like SageMaker Model Monitor.
* Regularly retrain the model with updated data to account for the drift.
* Why Not Other Options?:
* B: Model complexity is unrelated if the model initially performed well.
* C: Data quality issues would have been detected during baseline analysis.
* D: Incorrect ground truth labels would have resulted in a consistently poor baseline.
Conclusion: The decrease in F1 score is most likely due toconcept driftin the customer data, requiring retraining of the model with new data.

NEW QUESTION # 83
......

The Amazon MLA-C01 certification is on trending nowadays, and many Amazon aspirants are trying to get it. Success in the AWS Certified Machine Learning Engineer - Associate (MLA-C01) test helps you land well-paying jobs. Additionally, the Amazon MLA-C01 certification exam is also beneficial to get promotions in your current company. But the main problem that every applicant faces while preparing for the MLA-C01 Certification test is not finding updated Amazon MLA-C01 practice questions.

Valid Dumps MLA-C01 Pdf: https://www.actual4cert.com/MLA-C01-real-questions.html

Michael Johnson Michael Johnson

Biography

ABOUT

Pages

Contact