Option A is correct because AWS documentation states that regularization helps prevent linear models from overfitting , and specifically that L1 regularization reduces the number of features used in the model by pushing small feature weights to zero . AWS further explains that L1 regularization produces sparse models and reduces noise. That is the exact combination needed in this scenario: the model is overfitting, and the data contains unnecessary features whose impact should be reduced.
AWS guidance on overfitting also says that to reduce model overfitting, you should reduce model flexibility by using feature selection and increasing the amount of regularization . L1 regularization is especially suitable here because it does both in practice: it acts as a regularizer and also effectively performs feature selection by shrinking unhelpful feature coefficients to zero. Even though the option wording says "apply L1 regularization to the training data," the only answer choice that aligns with AWS-documented techniques for both overfitting reduction and unnecessary-feature suppression is L1 regularization with retraining .
The other options do not meet both requirements. SageMaker Debugger monitors and helps analyze training jobs, but it is not the feature that "applies L1 regularization" to a running model. Increasing training iterations can make overfitting worse, not better. Decreasing training iterations may reduce overfitting in some cases, but it does not specifically address the presence of unnecessary features. Therefore, the AWS- aligned choice that best reduces overfitting and the impact of irrelevant features is A .

Option A is correct because the issue is that the lead ML engineer's local notebook environment does not yet contain the latest merged changes from the shared Git repository. In Git, the operation used to fetch remote updates and merge them into the local branch is git pull . AWS documentation for SageMaker notebook collaboration explains that Git repositories can be associated with SageMaker notebook instances so distributed teams can share notebooks and collaborate in a source-control environment.
AWS documentation is even more explicit in the SageMaker Git operations guide: to fetch notebooks committed by other users , you should do a pull from the project repository . That is exactly the situation described in the question. Other team members have already merged their notebook changes into the repository, and the lead engineer needs the latest notebook updates in the local SageMaker notebook environment. Therefore, running !git pull origin master is the correct command among the available choices.
The other options do not solve the problem. git commit records local changes in the user's own repository history; it does not download new remote content. git push origin master uploads local commits to the remote repository; it is used when you want to send your own changes outward, not retrieve teammates' updates. git branch only lists or manages branches and does not synchronize notebook content. Because the requirement is to see the latest merged notebook made by others , the necessary operation is a pull from the remote repository. So the best verified answer is A .

Amazon FSx for NetApp ONTAP allows mounting the file system as a network-attached storage (NAS) volume. Since the FSx for ONTAP file system and SageMaker instance are in the same VPC, you can directly mount the file system to the SageMaker instance. This approach ensures efficient access to the 6 TB of training data without the need to duplicate or transfer the data, meeting the requirements with minimal complexity and operational overhead.

Explanation:
Step 1:
The company commits code changes or new training datasets to a Git repository.
This is the trigger point. A source code or data change initiates the CI/CD pipeline.
Step 2:
CodePipeline detects code changes and starts to build automatically.
CodePipeline monitors the Git repository (for example, AWS CodeCommit, GitHub, or Bitbucket) and automatically triggers the pipeline when changes are detected.
Step 3:
The company builds and deploys ML models and applications to staging servers for testing.
The pipeline runs build, training, and test stages (often using AWS CodeBuild and SageMaker) and deploys artifacts to a staging or test environment for validation.
Step 4:
Human approval is provided after testing is successful.
A manual approval action is a best practice for ML workflows to ensure governance, compliance, and quality checks before production deployment.
Step 5:
CodePipeline deploys ML models and applications to production.
After approval, the pipeline automatically deploys the validated model or application to the production environment.

The primary requirement in this scenario is achieving sub-second latency for a real-time analytics dashboard powered by Amazon OpenSearch Service. The current architecture uses Amazon Data Firehose, which buffers incoming records based on time or size before delivering them to the destination. A buffer interval of
60 seconds introduces unavoidable latency, making it unsuitable for near-real-time or sub-second use cases.
According to AWS documentation, reducing or eliminating buffering in Firehose is the correct approach when low-latency ingestion is required. Setting the Firehose buffer interval to zero seconds forces Firehose to deliver records as soon as they are received. Additionally, tuning the PutRecordBatch batch size allows efficient ingestion while minimizing delivery delay. This configuration is explicitly recommended for latency- sensitive analytics pipelines.
Option B is incorrect because AWS DataSync is designed for batch-oriented data transfers between storage systems, not real-time streaming. Enhanced fan-out consumers are a feature of Amazon Kinesis Data Streams, not DataSync, making this option invalid.
Option C directly contradicts the requirement. Increasing the buffer interval from 60 seconds to 120 seconds would further increase latency and degrade real-time performance.
Option D is also incorrect because Amazon SQS is a message queueing service, not a streaming ingestion service optimized for indexing data into OpenSearch with minimal latency. Using SQS would add additional processing layers and would not inherently provide sub-second ingestion into OpenSearch.
Therefore, using zero buffering in the Firehose stream and tuning the PutRecordBatch batch size is the only change that aligns with AWS best practices for achieving sub-second latency in real-time analytics pipelines.