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Free AWS Certified Machine Learning Engineer - Associate MLA-C01 Exam Braindumps
AWS Certified Machine Learning Engineer - Associate MLA-C01 Questions 121–128 (page: 17)

Updated On: 15-Apr-2026
Page 17 of 33
PDF with 270 Questions

HOTSPOT

An ecommerce company is using Amazon SageMaker Clarify Foundation Model Evaluations (FMEval) to evaluate ML models.

Select the correct model evaluation task from the following list for each ecommerce use case. Each model evaluation task should be selected one time.

Classification evaluation
Open-ended generation
Question answering
Text summarization



Hot Area:

  1. See Explanation section for answer.

Answer(s): A

Explanation:




Categorize customer reviews as positive, neutral, or negative sentiment Classification evaluation Sentiment categorization is a classification task.

Create concise product descriptions from complete manufacturer details Text summarization Condensing manufacturer details into product descriptions is a summarization task.

Recommend products based on a user's browsing history Open-ended generation Generating recommendations requires open-ended generative modeling.

Respond to specific customer queries about product features Question answering Handling direct customer queries about features aligns with question answering.



A company wants to launch a new internal generative AI interface to answer user questions. The interface will be based on a popular open source large language model (LLM).

Which combination of steps will deploy the interface with the LEAST operational overhead? (Choose two.)

  1. Use Amazon SageMaker JumpStart to deploy the LLM.
  2. Download the LLM as a .zip file. Deploy the LLM on a GPU-based Amazon EC2 instance.
  3. Create a frontend HTML interface that uses an Amazon API Gateway WebSocket API with AWS Lambda functions to handle the user interaction.
  4. Use Amazon QuickSight to create a UI to handle the user interaction.
  5. Use Amazon Lex to create a UI to handle the user interaction.

Answer(s): A,C

Explanation:

The least operational overhead comes from using Amazon SageMaker JumpStart to quickly deploy the open source LLM without needing to manage infrastructure, and building a lightweight frontend HTML interface with API Gateway WebSocket API and Lambda to handle user interactions efficiently. This avoids the manual setup of EC2 or unrelated services like QuickSight or Lex.



A company wants to build a real-time analytics application that uses streaming data from social media. An ML engineer must implement a solution that ingests and transforms 5 GB of data each minute. The solution also must load the data into a data store that supports fast queries for the real-time analytics.

Which solution will meet these requirements?

  1. Use Amazon EventBridge to ingest the social media data. Use AWS Glue to transform the data. Store the transformed data in Amazon ElastiCache (Memcached).
  2. Use Amazon Simple Queue Service (Amazon SQS) to ingest the social media data. Use AWS Lambda to transform the data. Store the transformed data in Amazon S3.
  3. Use Amazon Simple Notification Service (Amazon SNS) to ingest the social media data. Use Amazon EMR to transform the data. Store the transformed data in Amazon RDS.
  4. Use Amazon Kinesis Data Streams to ingest the social media data. Use Amazon Managed Service for Apache Flink to transform the data. Store the transformed data in Amazon DynamoDB.

Answer(s): D

Explanation:

Amazon Kinesis Data Streams is designed for high-throughput ingestion of streaming data such as social media feeds. Amazon Managed Service for Apache Flink enables real-time transformations on that data.
Amazon DynamoDB provides low-latency reads and writes, making it suitable for fast queries in real-time analytics. This combination fully meets the scale and speed requirements.



A company stores training data as a .csv file in an Amazon S3 bucket. The company must encrypt the data and must control which applications have access to the encryption key.

Which solution will meet these requirements?

  1. Create a new SSH access key. Use the AWS Encryption CLI with a reference to the new access key to encrypt the file.
  2. Create a new API key by using the Amazon API Gateway CreateApiKey API operation. Use the AWS CLI with a reference to the new API key to encrypt the file.
  3. Create a new IAM role. Attach a policy that allows the AWS Key Management Service (AWS KMS) GenerateDataKey action. Use the role to encrypt the file.
  4. Create a new AWS Key Management Service (AWS KMS) key. Use the AWS Encryption CLI with a reference to the new KMS key to encrypt the file.

Answer(s): D

Explanation:

The correct approach is to create a new AWS KMS key and use the AWS Encryption CLI to encrypt the file with that key. This ensures the data in S3 is encrypted, and access to the encryption key can be controlled through KMS key policies and IAM permissions, meeting both encryption and access control requirements.



A company needs to perform feature engineering, aggregation, and data preparation. After the features are produced, the company must implement a solution on AWS to process and store the features.

Which solution will meet these requirements?

  1. Use Amazon SageMaker Feature Processing to process and ingest the data. Use SageMaker Feature Store to manage and store the features.
  2. Use Amazon SageMaker Model Monitor to automatically ingest and transform the data. Create an Amazon S3 bucket to store the features in JSON format.
  3. Use Amazon Managed Service for Apache Flink to transform the data and to ingest the data directly into Amazon SageMaker Feature Store. Use Feature Store to manage and store the features.
  4. Use an Amazon SageMaker batch transform job to analyze, transform, and ingest the data. Create an Amazon DynamoDB table to store the features.

Answer(s): A

Explanation:

Amazon SageMaker Feature Processing (via processing jobs) is used to perform feature engineering and data preparation. The engineered features can then be ingested into SageMaker Feature Store, which is a purpose- built service to manage and store ML features for reuse across training and inference. This combination directly addresses the company's requirements.



A company is developing a new online application to gather information from customers. An ML engineer has developed a new ML model that will determine a score for each customer. The model will use the score to determine which product to display to the customer. The ML engineer needs to minimize response-time latency for the model.

How should the ML engineer deploy the application in Amazon SageMaker to meet these requirements?

  1. Configure batch transform.
  2. Configure a real-time inference endpoint.
  3. Configure a serverless inference endpoint.
  4. Configure an asynchronous inference endpoint.

Answer(s): B

Explanation:

To minimize response-time latency, the ML model should be deployed to a real-time inference endpoint in Amazon SageMaker. This provides low-latency predictions by keeping the model loaded and ready to handle incoming requests, which is critical for an online application serving customers in real time.



A company is using Amazon EMR. The company has a large dataset in Amazon S3 that needs to be ingested into Amazon SageMaker Feature Store. The dataset contains historical data and real-time streaming data.

The company must ensure that the Feature Store online store is updated with the most recent data as soon as the data becomes available. The company also must maintain a complete Feature Store offline store for batch processing.

Which solution will meet these requirements?

  1. Use the PutRecord API in Feature Store Runtime to ingest all the data into the online store.
  2. Use the PutRecord API in Feature Store Runtime to ingest all the data into the offline store.
  3. Use the Feature Store Spark connector to ingest the data as Spark DataFrames with the online store and offline store enabled.
  4. Use the Feature Store Spark connector to ingest the data as Spark DataFrames with only the online store enabled.

Answer(s): C

Explanation:

The SageMaker Feature Store Spark connector allows ingestion of large-scale data from Amazon EMR into Feature Store as Spark DataFrames. Enabling both the online store ensures real-time updates for the latest data, while the offline store maintains the full historical dataset for batch analytics. This setup meets both low- latency and historical processing requirements.



An ML engineer needs to deploy four ML models in an Amazon SageMaker inference pipeline. The models were built with different frameworks. The ML engineer also needs to give clients the ability to use the invoke_endpoint call to perform inference for each model.

Which solution will meet these requirements MOST cost-effectively?

  1. Create a SageMaker multi-model endpoint.
  2. Create a SageMaker multi-container endpoint.
  3. Create multiple SageMaker single-model endpoints.
  4. Run a SparkML job to generate multiple endpoints.

Answer(s): B

Explanation:

A SageMaker multi-container endpoint allows deployment of multiple models built with different frameworks in a single endpoint. Each container can host a model with its required framework, and clients can use the same invoke_endpoint call while specifying the target container. This meets the requirement for framework diversity and is more cost-effective than running separate single-model endpoints.



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