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MLA-C01 β€” questions

Page 2 of 12 Β· 226 total questions.

MLA-C01 Question 21

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Topic 1 Β· Question 21

A company needs to run a batch data-processing job on Amazon EC2 instances. The job will run during the weekend and will take 90 minutes to finish running. The processing can handle interruptions. The company will run the job every weekend for the next 6 months. Which EC2 instance purchasing option will meet these requirements MOST cost-effectively?

  • ASpot Instances (correct answer)
  • BReserved Instances
  • COn-Demand Instances
  • DDedicated Instances
Reveal answer & explanation
Correct answer: A

The correct answer is A. Option A: Spot Instances

Explanation

Spot Instances use spare capacity at a large discount, ideal for fault-tolerant or interruptible workloads. This option delivers the requirement at the lowest cost.

MLA-C01 Question 22

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Topic 1 Β· Question 22

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 Π’ in the same Region. Which solution will meet this requirement with the LEAST development effort?

  • AUse Amazon S3 to make a copy of the model. Transfer the copy to Account B.
  • BCreate a resource-based IAM policy. Use the Amazon Comprehend ImportModel API operation to copy the model to Account B. (correct answer)
  • CUse AWS DataSync to replicate the model from Account A to Account B.
  • DCreate an AWS Site-to-Site VPN connection between Account A and Account Π’ to transfer the model.
Reveal answer & explanation
Correct answer: B

The correct answer is B. Option B: Create a resource-based IAM policy. Use the Amazon Comprehend ImportModel API operation to copy the model to Account B.

Explanation

Amazon Comprehend extracts insights and entities from text using NLP. AWS IAM controls authenticated and authorized access to AWS resources with fine-grained policies. This option needs the least custom development effort.

MLA-C01 Question 23

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Topic 1 Β· Question 23 Β· Select all that apply

An ML engineer is training a simple neural network model. The ML engineer tracks the performance of the model over time on a validation dataset. The model's performance improves substantially at first and then degrades after a specific number of epochs. Which solutions will mitigate this problem? (Choose two.)

  • AEnable early stopping on the model. (correct answer)
  • BIncrease dropout in the layers. (correct answer)
  • CIncrease the number of layers.
  • DIncrease the number of neurons.
  • EInvestigate and reduce the sources of model bias.
Reveal answer & explanation
Correct answer: A, B

The correct answer is A, B. Option A: Enable early stopping on the model. Option B: Increase dropout in the layers.

MLA-C01 Question 24

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Topic 1 Β· Question 24

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?

  • AUse an AWS Batch job to process the files and generate embeddings. Use AWS Glue to store the embeddings. Use SQL queries to perform the semantic searches.
  • BUse a custom Amazon SageMaker notebook to run a custom script to generate embeddings. Use SageMaker Feature Store to store the embeddings. Use SQL queries to perform the semantic searches.
  • CUse the Amazon Kendra S3 connector to ingest the documents from the S3 bucket into Amazon Kendra. Query Amazon Kendra to perform the semantic searches. (correct answer)
  • DUse an Amazon Textract asynchronous job to ingest the documents from the S3 bucket. Query Amazon Textract to perform the semantic searches.
Reveal answer & explanation
Correct answer: C

The correct answer is C. Option C: Use the Amazon Kendra S3 connector to ingest the documents from the S3 bucket into Amazon Kendra. Query Amazon Kendra to perform the semantic searches.

Explanation

Amazon S3 provides durable, scalable object storage that is fully managed. Amazon Kendra provides intelligent enterprise search.

MLA-C01 Question 25

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Topic 1 Β· Question 25

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?

  • ACreate a new model by using Amazon SageMaker Autopilot. Report the model's achieved performance. (correct answer)
  • BImplement custom scripts to perform data pre-processing, multiple linear regression, and performance evaluation. Run the scripts on Amazon EC2 instances.
  • CConfigure Amazon Macie to analyze the dataset and to create a model. Report the model's achieved performance.
  • DSelect a model from Amazon Bedrock. Tune the model with the data. Report the model's achieved performance.
Reveal answer & explanation
Correct answer: A

The correct answer is A. Option A: Create a new model by using Amazon SageMaker Autopilot. Report the model's achieved performance.

Explanation

Amazon SageMaker builds, trains and deploys machine learning models in a fully managed service. This option needs the least custom development effort.

MLA-C01 Question 26

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Topic 1 Β· Question 26

A company wants to predict the success of advertising campaigns by considering the color scheme of each advertisement. An ML engineer is preparing data for a neural network model. The dataset includes color information as categorical data. Which technique for feature engineering should the ML engineer use for the model?

  • AApply label encoding to the color categories. Automatically assign each color a unique integer.
  • BImplement padding to ensure that all color feature vectors have the same length.
  • CPerform dimensionality reduction on the color categories.
  • DOne-hot encode the color categories to transform the color scheme feature into a binary matrix. (correct answer)
Reveal answer & explanation
Correct answer: D

The correct answer is D. Option D: One-hot encode the color categories to transform the color scheme feature into a binary matrix.

MLA-C01 Question 27

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Topic 1 Β· Question 27

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?

  • ADeploy the model on Amazon SageMaker. Create a set of AWS Lambda functions to identify and remove the sensitive data.
  • BDeploy 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.
  • CUse Amazon Macie to identify the sensitive data. Create a set of AWS Lambda functions to remove the sensitive data. (correct answer)
  • DUse Amazon Comprehend to identify the sensitive data. Launch Amazon EC2 instances to remove the sensitive data.
Reveal answer & explanation
Correct answer: C

The correct answer is C. Option C: Use Amazon Macie to identify the sensitive data. Create a set of AWS Lambda functions to remove the sensitive data.

Explanation

AWS Lambda runs code without provisioning servers and scales automatically, which minimizes operational overhead. Amazon Macie discovers and protects sensitive data such as PII in S3.

MLA-C01 Question 28

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Topic 1 Β· Question 28

An ML engineer needs to create data ingestion pipelines and ML model deployment pipelines on AWS. All the raw data is stored in Amazon S3 buckets. Which solution will meet these requirements?

  • AUse Amazon Data Firehose to create the data ingestion pipelines. Use Amazon SageMaker Studio Classic to create the model deployment pipelines.
  • BUse AWS Glue to create the data ingestion pipelines. Use Amazon SageMaker Studio Classic to create the model deployment pipelines. (correct answer)
  • CUse Amazon Redshift ML to create the data ingestion pipelines. Use Amazon SageMaker Studio Classic to create the model deployment pipelines.
  • DUse Amazon Athena to create the data ingestion pipelines. Use an Amazon SageMaker notebook to create the model deployment pipelines.
Reveal answer & explanation
Correct answer: B

The correct answer is B. Option B: Use AWS Glue to create the data ingestion pipelines. Use Amazon SageMaker Studio Classic to create the model deployment pipelines.

Explanation

AWS Glue is a serverless ETL and data-catalog service that prepares and integrates data. Amazon SageMaker builds, trains and deploys machine learning models in a fully managed service.

MLA-C01 Question 29

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Topic 1 Β· Question 29

A company that has hundreds of data scientists is using Amazon SageMaker to create ML models. The models are in model groups in the SageMaker Model Registry. The data scientists are grouped into three categories: computer vision, natural language processing (NLP), and speech recognition. An ML engineer needs to implement a solution to organize the existing models into these groups to improve model discoverability at scale. The solution must not affect the integrity of the model artifacts and their existing groupings. Which solution will meet these requirements?

  • ACreate a custom tag for each of the three categories. Add the tags to the model packages in the SageMaker Model Registry.
  • BCreate a model group for each category. Move the existing models into these category model groups.
  • CUse SageMaker ML Lineage Tracking to automatically identify and tag which model groups should contain the models.
  • DCreate a Model Registry collection for each of the three categories. Move the existing model groups into the collections. (correct answer)
Reveal answer & explanation
Correct answer: D

The correct answer is D. Option D: Create a Model Registry collection for each of the three categories. Move the existing model groups into the collections.

MLA-C01 Question 30

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Topic 1 Β· Question 30

A company runs an Amazon SageMaker domain in a public subnet of a newly created VPC. The network is configured properly, and ML engineers can access the SageMaker 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?

  • ACreate a security group inbound rule to deny traffic from the specific IP address. Assign the security group to the domain.
  • BCreate a network ACL inbound rule to deny traffic from the specific IP address. Assign the rule to the default network Ad for the subnet where the domain is located. (correct answer)
  • CCreate a shadow variant for the domain. Configure SageMaker Inference Recommender to send traffic from the specific IP address to the shadow endpoint.
  • DCreate a VPC route table to deny inbound traffic from the specific IP address. Assign the route table to the domain.
Reveal answer & explanation
Correct answer: B

The correct answer is B. Option B: Create a network ACL inbound rule to deny traffic from the specific IP address. Assign the rule to the default network Ad for the subnet where the domain is located.

MLA-C01 Question 31

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Topic 1 Β· Question 31

A company is gathering audio, video, and text data in various languages. The company needs to use a large language model (LLM) to summarize the gathered data that is in Spanish. Which solution will meet these requirements in the LEAST amount of time?

  • ATrain and deploy a model in Amazon SageMaker to convert the data into English text. Train and deploy an LLM in SageMaker to summarize the text.
  • BUse Amazon Transcribe and Amazon Translate to convert the data into English text. Use Amazon Bedrock with the Jurassic model to summarize the text. (correct answer)
  • CUse Amazon Rekognition and Amazon Translate to convert the data into English text. Use Amazon Bedrock with the Anthropic Claude model to summarize the text.
  • DUse Amazon Comprehend and Amazon Translate to convert the data into English text. Use Amazon Bedrock with the Stable Diffusion model to summarize the text.
Reveal answer & explanation
Correct answer: B

The correct answer is B. Option B: Use Amazon Transcribe and Amazon Translate to convert the data into English text. Use Amazon Bedrock with the Jurassic model to summarize the text.

Explanation

Amazon Transcribe converts speech to text. Amazon Translate provides neural machine translation. Amazon Bedrock provides access to foundation models through a managed API.

MLA-C01 Question 32

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Topic 1 Β· Question 32

A financial company receives a high volume of real-time market data streams from an external provider. The streams consist of thousands of JSON records every second. The company needs to implement a scalable solution on AWS to identify anomalous data points. Which solution will meet these requirements with the LEAST operational overhead?

  • AIngest real-time data into Amazon Kinesis data streams. Use the built-in RANDOM_CUT_FOREST function in Amazon Managed Service for Apache Flink to process the data streams and to detect data anomalies. (correct answer)
  • BIngest real-time data into Amazon Kinesis data streams. Deploy an Amazon SageMaker endpoint for real-time outlier detection. Create an AWS Lambda function to detect anomalies. Use the data streams to invoke the Lambda function.
  • CIngest real-time data into Apache Kafka on Amazon EC2 instances. Deploy an Amazon SageMaker endpoint for real-time outlier detection. Create an AWS Lambda function to detect anomalies. Use the data streams to invoke the Lambda function.
  • DSend real-time data to an Amazon Simple Queue Service (Amazon SQS) FIFO queue. Create an AWS Lambda function to consume the queue messages. Program the Lambda function to start an AWS Glue extract, transform, and load (ETL) job for batch processing and anomaly detection.
Reveal answer & explanation
Correct answer: A

The correct answer is A. Option A: Ingest real-time data into Amazon Kinesis data streams. Use the built-in RANDOM_CUT_FOREST function in Amazon Managed Service for Apache Flink to process the data streams and to detect data anomalies.

Explanation

Kinesis Data Streams ingests and processes high-volume real-time streaming data. Amazon Kinesis ingests and processes real-time streaming data at scale.

MLA-C01 Question 33

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Topic 1 Β· Question 33

A company has a large collection of chat recordings from customer interactions after a product release. An ML engineer needs to create an ML model to analyze the chat data. The ML engineer needs to determine the success of the product by reviewing customer sentiments about the product. Which action should the ML engineer take to complete the evaluation in the LEAST amount of time?

  • AUse Amazon Rekognition to analyze sentiments of the chat conversations.
  • BTrain a Naive Bayes classifier to analyze sentiments of the chat conversations.
  • CUse Amazon Comprehend to analyze sentiments of the chat conversations. (correct answer)
  • DUse random forests to classify sentiments of the chat conversations.
Reveal answer & explanation
Correct answer: C

The correct answer is C. Option C: Use Amazon Comprehend to analyze sentiments of the chat conversations.

Explanation

Amazon Comprehend extracts insights and entities from text using NLP.

MLA-C01 Question 34

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Topic 1 Β· Question 34

A company has a conversational AI assistant that sends requests through Amazon Bedrock to an Anthropic Claude large language model (LLM). Users report that when they ask similar questions multiple times, they sometimes receive different answers. An ML engineer needs to improve the responses to be more consistent and less random. Which solution will meet these requirements?

  • AIncrease the temperature parameter and the top_k parameter.
  • BIncrease the temperature parameter. Decrease the top_k parameter.
  • CDecrease the temperature parameter. Increase the top_k parameter.
  • DDecrease the temperature parameter and the top_k parameter. (correct answer)
Reveal answer & explanation
Correct answer: D

The correct answer is D. Option D: Decrease the temperature parameter and the top_k parameter.

MLA-C01 Question 35

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Topic 1 Β· Question 35

A company is using ML to predict the presence of a specific weed in a farmer's field. The company is using the Amazon SageMaker linear learner built-in algorithm with a value of multiclass_dassifier for the predictorjype hyperparameter. What should the company do to MINIMIZE false positives?

  • ASet the value of the weight decay hyperparameter to zero.
  • BIncrease the number of training epochs.
  • CIncrease the value of the target_precision hyperparameter. (correct answer)
  • DChange the value of the predictorjype hyperparameter to regressor.
Reveal answer & explanation
Correct answer: C

The correct answer is C. Option C: Increase the value of the target_precision hyperparameter.

MLA-C01 Question 36

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Topic 1 Β· Question 36

A company has implemented a data ingestion pipeline for sales transactions from its ecommerce website. The company uses Amazon Data Firehose to ingest data into Amazon OpenSearch Service. The buffer interval of the Firehose stream is set for 60 seconds. An OpenSearch linear model generates real-time sales forecasts based on the data and presents the data in an OpenSearch dashboard. The company needs to optimize the data ingestion pipeline to support sub-second latency for the real-time dashboard. Which change to the architecture will meet these requirements?

  • AUse zero buffering in the Firehose stream. Tune the batch size that is used in the PutRecordBatch operation. (correct answer)
  • BReplace the Firehose stream with an AWS DataSync task. Configure the task with enhanced fan-out consumers.
  • CIncrease the buffer interval of the Firehose stream from 60 seconds to 120 seconds.
  • DReplace the Firehose stream with an Amazon Simple Queue Service (Amazon SQS) queue.
Reveal answer & explanation
Correct answer: A

The correct answer is A. Option A: Use zero buffering in the Firehose stream. Tune the batch size that is used in the PutRecordBatch operation.

Explanation

AWS Batch schedules and runs batch computing jobs at any scale without managing clusters. This option meets the real-time / low-latency performance requirement.

MLA-C01 Question 37

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Topic 1 Β· Question 37

A company has trained an ML model in Amazon SageMaker. The company needs to host the model to provide inferences in a production environment. The model must be highly available and must respond with minimum latency. The size of each request will be between 1 KB and 3 MB. The model will receive unpredictable bursts of requests during the day. The inferences must adapt proportionally to the changes in demand. How should the company deploy the model into production to meet these requirements?

  • ACreate a SageMaker real-time inference endpoint. Configure auto scaling. Configure the endpoint to present the existing model. (correct answer)
  • BDeploy the model on an Amazon Elastic Container Service (Amazon ECS) cluster. Use ECS scheduled scaling that is based on the CPU of the ECS cluster.
  • CInstall SageMaker Operator on an Amazon Elastic Kubernetes Service (Amazon EKS) cluster. Deploy the model in Amazon EKS. Set horizontal pod auto scaling to scale replicas based on the memory metric.
  • DUse Spot Instances with a Spot Fleet behind an Application Load Balancer (ALB) for inferences. Use the ALBRequestCountPerTarget metric as the metric for auto scaling.
Reveal answer & explanation
Correct answer: A

The correct answer is A. Option A: Create a SageMaker real-time inference endpoint. Configure auto scaling. Configure the endpoint to present the existing model.

Explanation

Auto Scaling adjusts capacity automatically to match demand, improving availability and cost efficiency. Amazon SageMaker builds, trains and deploys machine learning models in a fully managed service. AWS Config tracks resource configuration changes and evaluates compliance. By spanning multiple Availability Zones / adding redundancy, this option provides the high availability and resilience required.

MLA-C01 Question 38

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Topic 1 Β· Question 38

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?

  • ARun the primary node, core nodes, and task nodes on On-Demand Instances.
  • BRun the primary node, core nodes, and task nodes on Spot Instances.
  • CRun the primary node on an On-Demand Instance. Run the core nodes and task nodes on Spot Instances.
  • DRun the primary node and core nodes on On-Demand Instances. Run the task nodes on Spot Instances. (correct answer)
Reveal answer & explanation
Correct answer: D

The correct answer is D. Option D: Run the primary node and core nodes on On-Demand Instances. Run the task nodes on Spot Instances.

Explanation

Spot Instances use spare capacity at a large discount, ideal for fault-tolerant or interruptible workloads. This option delivers the requirement at the lowest cost.

MLA-C01 Question 39

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Topic 1 Β· Question 39 Β· Select all that apply

A company wants to improve the sustainability of its ML operations. Which actions will reduce the energy usage and computational resources that are associated with the company's training jobs? (Choose two.)

  • AUse Amazon SageMaker Debugger to stop training jobs when non-converging conditions are detected. (correct answer)
  • BUse Amazon SageMaker Ground Truth for data labeling.
  • CDeploy models by using AWS Lambda functions.
  • DUse AWS Trainium instances for training. (correct answer)
  • EUse PyTorch or TensorFlow with the distributed training option.
Reveal answer & explanation
Correct answer: A, D

The correct answer is A, D. Option A: Use Amazon SageMaker Debugger to stop training jobs when non-converging conditions are detected. Option D: Use AWS Trainium instances for training.

Explanation

Amazon SageMaker builds, trains and deploys machine learning models in a fully managed service.

MLA-C01 Question 40

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Topic 1 Β· Question 40

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 Π’Π’ 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?

  • AProcess data at each step by using Amazon SageMaker Data Wrangler. Automate the process by using Data Wrangler jobs.
  • BUse Amazon SageMaker notebooks for each data processing step. Automate the process by using Amazon EventBridge.
  • CProcess data at each step by using AWS Lambda functions. Automate the process by using AWS Step Functions and Amazon EventBridge.
  • DUse Amazon SageMaker Pipelines to create a pipeline of data processing steps. Automate the pipeline by using Amazon EventBridge. (correct answer)
Reveal answer & explanation
Correct answer: D

The correct answer is D. Option D: Use Amazon SageMaker Pipelines to create a pipeline of data processing steps. Automate the pipeline by using Amazon EventBridge.

Explanation

Amazon EventBridge routes events between services with rules, enabling event-driven, decoupled architectures. Amazon SageMaker builds, trains and deploys machine learning models in a fully managed service.

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