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[General] MLA-C01 Free Practice & Exam MLA-C01 Blueprint

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【General】 MLA-C01 Free Practice & Exam MLA-C01 Blueprint

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Amazon MLA-C01 Exam Syllabus Topics:
TopicDetails
Topic 1
  • 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 2
  • 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.
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
  • 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.

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Amazon AWS Certified Machine Learning Engineer - Associate Sample Questions (Q160-Q165):NEW QUESTION # 160
An ML engineer is building a generative AI application on Amazon Bedrock by using large language models (LLMs).
Select the correct generative AI term from the following list for each description. Each term should be selected one time or not at all. (Select three.)
* Embedding
* Retrieval Augmented Generation (RAG)
* Temperature
* Token

Answer:
Explanation:

Explanation:

* Text representation of basic units of data processed by LLMs: Token
* High-dimensional vectors that contain the semantic meaning of text: Embedding
* Enrichment of information from additional data sources to improve a generated response: Retrieval Augmented Generation (RAG) Comprehensive Detailed Explanation
* Token:
* Description: A token represents the smallest unit of text (e.g., a word or part of a word) that an LLM processes. For example, "running" might be split into two tokens: "run" and "ing."
* Why? Tokens are the fundamental building blocks for LLM input and output processing, ensuring that the model can understand and generate text efficiently.
* Embedding:
* Description: High-dimensional vectors that encode the semantic meaning of text. These vectors are representations of words, sentences, or even paragraphs in a way that reflects their relationships and meaning.
* Why? Embeddings are essential for enabling similarity search, clustering, or any task requiring semantic understanding. They allow the model to "understand" text contextually.
* Retrieval Augmented Generation (RAG):
* Description: A technique where information is enriched or retrieved from external data sources (e.
g., knowledge bases or document stores) to improve the accuracy and relevance of a model's generated responses.
* Why? RAG enhances the generative capabilities of LLMs by grounding their responses in factual and up-to-date information, reducing hallucinations in generated text.
By matching these terms to their respective descriptions, the ML engineer can effectively leverage these concepts to build robust and contextually aware generative AI applications on Amazon Bedrock.

NEW QUESTION # 161
A company has multiple models that are hosted on Amazon SageMaker Al. The models need to be re-trained.
The requirements for each model are different, so the company needs to choose different deployment strategies to transfer all requests to a new model.
Select the correct strategy from the following list for each requirement. Select each strategy one time. (Select THREE.)
. Canary traffic shifting
. Linear traffic shifting guardrail
. All at once traffic shifting

Answer:
Explanation:

Explanation:
1## Simultaneous calls to the endpoint must reach models with the same configuration Correct strategy: All at once traffic shifting Why:
"All at once" replaces the old model with the new model immediately. After the switch, all concurrent requests hit only the new model configuration, guaranteeing configuration consistency across simultaneous calls.
2## The new model must receive only a fraction of the requests for validation before receiving all the traffic Correct strategy: Canary traffic shifting Why:
Canary deployments route a small percentage of traffic (for example, 5% or 10%) to the new model first. This allows validation of correctness and performance before shifting 100% of traffic.
3## Traffic to the new model must increase gradually to ensure that pipelines that rely on the endpoint do not fail because of changes in latency Correct strategy: Linear traffic shifting guardrail Why:
Linear traffic shifting gradually increases traffic in equal increments over time and includes guardrails (such as CloudWatch alarms) to automatically roll back if latency or errors exceed thresholds.

NEW QUESTION # 162
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, core nodes, and task nodes on On-Demand Instances.
  • B. Run the primary node on an On-Demand Instance. Run the core nodes and task nodes on Spot Instances.
  • C. Run the primary node and core nodes on On-Demand Instances. Run the task nodes on Spot Instances.
  • D. Run the primary node, core nodes, and task nodes on Spot Instances.
Answer: C

NEW QUESTION # 163
A company runs an Amazon SageMaker AI domain in a public subnet of a newly created VPC. The network is configured properly, and ML engineers can access the SageMaker AI domain.
Recently, the company discovered suspicious traffic to the domain from a specific IP address. The company needs to block traffic from the specific IP address.
Which update to the network configuration will meet this requirement?
  • A. Create a security group inbound rule to deny traffic from the specific IP address. Assign the security group to the domain.
  • B. Create a shadow variant for the domain. Configure SageMaker Inference Recommender to send traffic from the specific IP address to the shadow endpoint.
  • C. Create a VPC route table to deny inbound traffic from the specific IP address. Assign the route table to the domain.
  • D. Create a network ACL inbound rule to deny traffic from the specific IP address. Assign the rule to the default network ACL for the subnet where the domain is located.
Answer: D
Explanation:
In AWS networking, security groups are stateful and allow-only, meaning they cannot explicitly deny traffic.
As a result, Option A is invalid. Network ACLs (NACLs), on the other hand, are stateless and support both allow and deny rules, making them the correct mechanism for blocking traffic from specific IP addresses.
Because the SageMaker AI domain is deployed in a public subnet, inbound traffic reaches the subnet before it reaches the resource. AWS documentation states that NACLs are evaluated at the subnet level and are ideal for implementing IP-based blocking rules.
Route tables control routing paths, not traffic filtering, so Option D is incorrect. Option C is unrelated to network security and does not block traffic.
AWS best practices clearly recommend using network ACL deny rules when an explicit block is required for a specific IP address at the subnet boundary.
Therefore, Option B is the correct and AWS-aligned solution.

NEW QUESTION # 164
An ML engineer is preparing a dataset that contains medical records to train an ML model to predict the likelihood of patients developing diseases.
The dataset contains columns for patient ID, age, medical conditions, test results, and a "Disease" target column.
How should the ML engineer configure the data to train the model?
  • A. Remove the "Disease" target column.
  • B. Remove the age column.
  • C. Remove the patient ID column.
  • D. Remove the medical conditions and test results columns.
Answer: C
Explanation:
Patient ID is a unique identifier and does not contain predictive information. Including it can cause the model to overfit by memorizing records rather than learning meaningful patterns.
AWS ML best practices recommend removing identifiers that are not causally related to the target variable.
Age, medical conditions, and test results are clinically relevant features and should be retained. The target column must remain for supervised learning.
Therefore, Option A is the correct and AWS-aligned choice.

NEW QUESTION # 165
......
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