Amazon AWS Certified Machine Learning Engineer - Associate - MLA-C01 FREE EXAM DUMPS QUESTIONS & ANSWERS
An ML engineer develops a neural network model to predict whether customers will continue to subscribe to a service. The model performs well on training data. However, the accuracy of the model decreases significantly on evaluation data.
The ML engineer must resolve the model performance issue.
Which solution will meet this requirement?
The ML engineer must resolve the model performance issue.
Which solution will meet this requirement?
Correct Answer: C
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An ML engineer wants to use, prepare, and load data from Amazon S3 for analytics. The ML engineer must run an extract, transform, and load (ETL) job to discover the schema of the data and to store the metadata.
Which solution will meet these requirements with the LEAST manual effort?
Which solution will meet these requirements with the LEAST manual effort?
Correct Answer: B
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A company runs an ML model on Amazon SageMaker AI. The company uses an automatic process that makes API calls to create training jobs for the model. The company has new compliance rules that prohibit the collection of aggregated metadata from training jobs.
Which solution will prevent SageMaker AI from collecting metadata from the training jobs?
Which solution will prevent SageMaker AI from collecting metadata from the training jobs?
Correct Answer: C
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A company uses a batching solution to process data analytics each day. The company wants to build an analytics platform to provide near real-time updates. The company wants to use open source technology and does not want to manage or scale the infrastructure.
Which solution will meet these requirements?
Which solution will meet these requirements?
Correct Answer: D
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An ML engineer has trained an ML model by using Amazon SageMaker AI. The ML engineer determines that the model is overfitting and that the training data contains unnecessary features. The ML engineer must reduce the overfitting and the impact of the unnecessary features.
Which solution will meet these requirements?
Which solution will meet these requirements?
Correct Answer: C
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An airline company deploys ML models to one dozen Amazon SageMaker Al inference endpoints. The inference endpoints must be able to handle different types of workloads in a cost-effective way.
Select the correct inference option from the following list to handle each type of workload. Select each inference option one time. (Select FOUR.)
* Asynchronous inference
* Batch inference
* Real-time inference
* Serverless inference
Select the correct inference option from the following list to handle each type of workload. Select each inference option one time. (Select FOUR.)
* Asynchronous inference
* Batch inference
* Real-time inference
* Serverless inference
Correct Answer:
* Provide flight departure, arrival, and delay information, and provide updates for low-latency workloads# Real-time inference
* Advertise holiday travel promotional deals to millions of users in multiple markets before holiday seasons for spiky workloads# Serverless inference
* Generate quarterly and annual flight reports and insights for trend analysis of large datasets# Batch inference
* Generate online image and audio stories for passengers to watch or listen to while waiting at an airport# Asynchronous inference
* The correct mapping depends on latency requirement, traffic pattern, payload size, processing duration, and whether the workload needs a persistent endpoint.
* Real-time inference is the right choice for flight departure, arrival, and delay updates because this is an online user-facing workload that requires low latency. AWS states that SageMaker real-time inference is ideal for online inference workloads with low-latency or high-throughput requirements and uses a persistent fully managed endpoint. That fits flight status information because passengers and airline systems expect immediate responses.
* Serverless inference is the best choice for holiday promotional deals because this traffic is spiky, seasonal, and unpredictable. AWS describes SageMaker Serverless Inference as suitable for intermittent or unpredictable traffic patterns. It is cost-effective because SageMaker manages the infrastructure and scales down when there are no requests, so the company does not pay for idle endpoint capacity.
* Batch inference is correct for quarterly and annual flight reports because this workload analyzes large datasets offline and does not need an always-running endpoint. AWS says SageMaker batch transform is used to get inferences from large datasets and when a persistent endpoint is not required. Reports and trend analysis are scheduled, non-real-time analytics workloads, so batch inference is the most cost- effective option.
* Asynchronous inference is the right choice for generating online image and audio stories. These requests can have larger payloads and longer processing times than normal low-latency API calls. AWS states that SageMaker Asynchronous Inference queues incoming requests and is ideal for large payloads, long processing times, and near-real-time latency requirements. Image and audio generation can take seconds or minutes, so asynchronous inference is more appropriate than real-time inference.
A company is developing an application that reads animal descriptions from user prompts and generates images based on the information in the prompts. The application reads a message from an Amazon Simple Queue Service (Amazon SQS) queue. Then the application uses Amazon Titan Image Generator on Amazon Bedrock to generate an image based on the information in the message. Finally, the application removes the message from SQS queue.
Which IAM permissions should the company assign to the application ' s IAM role? (Select TWO.)
Which IAM permissions should the company assign to the application ' s IAM role? (Select TWO.)
Correct Answer: C,E
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A company has a Retrieval Augmented Generation (RAG) application that uses a vector database to store embeddings of documents. The company must migrate the application to AWS and must implement a solution that provides semantic search of text files. The company has already migrated the text repository to an Amazon S3 bucket.
Which solution will meet these requirements?
Which solution will meet these requirements?
Correct Answer: C
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A company is planning to create several ML prediction models. The training data is stored in Amazon S3. The entire dataset is more than 5 ## in size and consists of CSV, JSON, Apache Parquet, and simple text files.
The data must be processed in several consecutive steps. The steps include complex manipulations that can take hours to finish running. Some of the processing involves natural language processing (NLP) transformations. The entire process must be automated.
Which solution will meet these requirements?
The data must be processed in several consecutive steps. The steps include complex manipulations that can take hours to finish running. Some of the processing involves natural language processing (NLP) transformations. The entire process must be automated.
Which solution will meet these requirements?
Correct Answer: C
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A company uses a batching solution to process daily analytics. The company wants to provide near real-time updates, use open-source technology, and avoid managing or scaling infrastructure.
Which solution will meet these requirements?
Which solution will meet these requirements?
Correct Answer: C
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An ML engineer needs to use AWS services to identify and extract meaningful unique keywords from documents.
Which solution will meet these requirements with the LEAST operational overhead?
Which solution will meet these requirements with the LEAST operational overhead?
Correct Answer: D
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A company plans to use Amazon SageMaker AI to build image classification models. The company has 6 TB of training data stored on Amazon FSx for NetApp ONTAP. The file system is in the same VPC as SageMaker AI.
An ML engineer must make the training data accessible to SageMaker AI training jobs.
Which solution will meet these requirements?
An ML engineer must make the training data accessible to SageMaker AI training jobs.
Which solution will meet these requirements?
Correct Answer: B
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A company wants to host an ML model on Amazon SageMaker. An ML engineer is configuring a continuous integration and continuous delivery (Cl/CD) pipeline in AWS CodePipeline to deploy the model. The pipeline must run automatically when new training data for the model is uploaded to an Amazon S3 bucket.
Select and order the pipeline ' s correct steps from the following list. Each step should be selected one time or not at all. (Select and order three.)
* An S3 event notification invokes the pipeline when new data is uploaded.
* S3 Lifecycle rule invokes the pipeline when new data is uploaded.
* SageMaker retrains the model by using the data in the S3 bucket.
* The pipeline deploys the model to a SageMaker endpoint.
* The pipeline deploys the model to SageMaker Model Registry.
Select and order the pipeline ' s correct steps from the following list. Each step should be selected one time or not at all. (Select and order three.)
* An S3 event notification invokes the pipeline when new data is uploaded.
* S3 Lifecycle rule invokes the pipeline when new data is uploaded.
* SageMaker retrains the model by using the data in the S3 bucket.
* The pipeline deploys the model to a SageMaker endpoint.
* The pipeline deploys the model to SageMaker Model Registry.
Correct Answer:
Explanation:
Step 1: An S3 event notification invokes the pipeline when new data is uploaded.
Step 2: SageMaker retrains the model by using the data in the S3 bucket.
Step 3: The pipeline deploys the model to a SageMaker endpoint.
Step 1: An S3 Event Notification Invokes the Pipeline When New Data is Uploaded Why? The CI/CD pipeline should be triggered automatically whenever new training data is uploaded to Amazon S3. S3 event notifications can be configured to send events to AWS services like Lambda, which can then invoke AWS CodePipeline.
How? Configure the S3 bucket to send event notifications (e.g., s3:ObjectCreated:*) to AWS Lambda, which in turn triggers the CodePipeline.
Step 2: SageMaker Retrains the Model by Using the Data in the S3 Bucket Why? The uploaded data is used to retrain the ML model to incorporate new information and maintain performance. This step is critical to updating the model with fresh data.
How? Define a SageMaker training step in the CI/CD pipeline, which reads the training data from the S3 bucket and retrains the model.
Step 3: The Pipeline Deploys the Model to a SageMaker Endpoint
Why? Once retrained, the updated model must be deployed to a SageMaker endpoint to make it available for real-time inference.
How? Add a deployment step in the CI/CD pipeline, which automates the creation or update of the SageMaker endpoint with the retrained model.
Order Summary:
An S3 event notification invokes the pipeline when new data is uploaded.
SageMaker retrains the model by using the data in the S3 bucket.
The pipeline deploys the model to a SageMaker endpoint.
This configuration ensures an automated, efficient, and scalable CI/CD pipeline for continuous retraining and deployment of the ML model in Amazon SageMaker.
A hospital wants to predict patient outcomes for the coming year An ML engineer must improve several existing ML models that currently perform poorly.
Select the correct regularization method from the following list to improve each model Select each regularization method one time, more than one time, or not at all. (Select THREE.)
* L1 regularization
* L2 regularization
* Early stopping
Select the correct regularization method from the following list to improve each model Select each regularization method one time, more than one time, or not at all. (Select THREE.)
* L1 regularization
* L2 regularization
* Early stopping
Correct Answer:
Explanation:
Linear regression model whose coefficients should shrink but not become zero The answer: L2 regularization AWS says L2 produces smaller overall weight values and is the right fit when coefficients should be reduced without being forced to zero.
Polynomial regression model with irrelevant polynomial terms that should be eliminated The answer: L1 regularization AWS says L1 reduces the number of features used by pushing small weights to zero, which matches elimination of irrelevant terms.
Logistic regression model that has highly correlated features to eliminate highly redundant predictors The answer: L1 regularization This is the nuanced one. AWS says L2 stabilizes weights when there is high correlation between features, but because the question explicitly says eliminate highly redundant predictors, L1 is the better match since it creates sparsity and removes predictors by zeroing coefficients.