RELIABLE MLA-C01 EXAM REGISTRATION & MLA-C01 BRAIN DUMPS

Reliable MLA-C01 Exam Registration & MLA-C01 Brain Dumps

Reliable MLA-C01 Exam Registration & MLA-C01 Brain Dumps

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Tags: Reliable MLA-C01 Exam Registration, MLA-C01 Brain Dumps, MLA-C01 Latest Study Guide, Real MLA-C01 Braindumps, MLA-C01 Regualer Update

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There is no doubt that advanced technologies are playing an important role in boosting the growth of Amazon companies. This is the reason why the employees have now started upgrading their skillset with the AWS Certified Machine Learning Engineer - Associate (MLA-C01) certification exam because they want to work with those latest applications and save their jobs. They attempt the MLA-C01 exam to validate their skills and try to get their dream job.

Amazon AWS Certified Machine Learning Engineer - Associate Sample Questions (Q59-Q64):

NEW QUESTION # 59
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 on an On-Demand Instance. Run the core nodes and task nodes on Spot Instances.
  • B. Run the primary node, core nodes, and task nodes on Spot Instances.
  • C. Run the primary node, core nodes, and task nodes on On-Demand Instances.
  • D. Run the primary node and core nodes on On-Demand Instances. Run the task nodes on Spot Instances.

Answer: D

Explanation:
For Amazon EMR, the primary node and core nodes handle the critical functions of the cluster, including data storage (HDFS) and processing. Running them on On-Demand Instances ensures high availability and prevents data loss, as Spot Instances can be interrupted. The task nodes, which handle additionalprocessing but do not store data, can use Spot Instances to reduce costs without compromising the cluster's resilience or data integrity. This configuration balances cost-effectiveness and reliability.


NEW QUESTION # 60
A company is planning to use Amazon Redshift ML in its primary AWS account. The source data is in an Amazon S3 bucket in a secondary account.
An ML engineer needs to set up an ML pipeline in the primary account to access the S3 bucket in the secondary account. The solution must not require public IPv4 addresses.
Which solution will meet these requirements?

  • A. Provision a Redshift cluster and Amazon SageMaker Studio in a VPC in the primary account. Create an AWS Site-to-Site VPN connection with two encrypted IPsec tunnels between the accounts. Set up interface VPC endpoints for Amazon S3.
  • B. Provision a Redshift cluster and Amazon SageMaker Studio in a VPC with no public access enabled in the primary account. Create a VPC peering connection between the accounts. Update the VPC route tables to remove the route to 0.0.0.0/0.
  • C. Provision a Redshift cluster and Amazon SageMaker Studio in a VPC with no public access enabled in the primary account. Create an AWS Direct Connect connection and a transit gateway. Associate the VPCs from both accounts with the transit gateway. Update the VPC route tables to remove the route to
    0.0.0.0/0.
  • D. Provision a Redshift cluster and Amazon SageMaker Studio in a VPC in the primary account. Create an S3 gateway endpoint. Update the S3 bucket policy to allow IAM principals from the primary account.Set up interface VPC endpoints for SageMaker and Amazon Redshift.

Answer: D

Explanation:
S3 Gateway Endpoint: Allows private access to S3 from within a VPC without requiring a public IPv4 address, ensuring that data transfer between the primary and secondary accounts is secure and private.
Bucket Policy Update: The S3 bucket policy in the secondary account must explicitly allow access from the primary account's IAM principals to provide the necessary permissions.
Interface VPC Endpoints: Required for private communication between the VPC and Amazon SageMaker and Amazon Redshift services, ensuring the solution operates without public internet access.
This configuration meets the requirement to avoid public IPv4 addresses and allows secure and private communication between the accounts.


NEW QUESTION # 61
A company has a binary classification model in production. An ML engineer needs to develop a new version of the model.
The new model version must maximize correct predictions of positive labels and negative labels. The ML engineer must use a metric to recalibrate the model to meet these requirements.
Which metric should the ML engineer use for the model recalibration?

  • A. Recall
  • B. Accuracy
  • C. Precision
  • D. Specificity

Answer: B

Explanation:
Accuracy measures the proportion of correctly predicted labels (both positive and negative) out of the total predictions. It is the appropriate metric when the goal is to maximize the correct predictions of both positive and negative labels. However, it assumes that the classes are balanced; if the classes are imbalanced, other metrics like precision, recall, or specificity may be more relevant depending on the specific needs.


NEW QUESTION # 62
A company has deployed an ML model that detects fraudulent credit card transactions in real time in a banking application. The model uses Amazon SageMaker Asynchronous Inference. Consumers are reporting delays in receiving the inference results.
An ML engineer needs to implement a solution to improve the inference performance. The solution also must provide a notification when a deviation in model quality occurs.
Which solution will meet these requirements?

  • A. Keep using SageMaker Asynchronous Inference for inference. Use SageMaker Inference Recommender for notifications about model quality.
  • B. Use SageMaker Serverless Inference for inference. Use SageMaker Inference Recommender for notifications about model quality.
  • C. Use SageMaker real-time inference for inference. Use SageMaker Model Monitor for notifications about model quality.
  • D. Use SageMaker batch transform for inference. Use SageMaker Model Monitor for notifications about model quality.

Answer: C

Explanation:
SageMaker real-time inference is designed for low-latency, real-time use cases, such as detecting fraudulent transactions in banking applications. It eliminates the delays associated with SageMaker Asynchronous Inference, improving inference performance.
SageMaker Model Monitor provides tools to monitor deployed models for deviations in data quality, model performance, and other metrics. It can be configured to send notifications when a deviation in model quality is detected, ensuring the system remains reliable.


NEW QUESTION # 63
A company needs to give its ML engineers appropriate access to training data. The ML engineers must access training data from only their own business group. The ML engineers must not be allowed to access training data from other business groups.
The company uses a single AWS account and stores all the training data in Amazon S3 buckets. All ML model training occurs in Amazon SageMaker.
Which solution will provide the ML engineers with the appropriate access?

  • A. Configure S3 Object Lock settings for each user.
  • B. Enable S3 bucket versioning.
  • C. Add cross-origin resource sharing (CORS) policies to the S3 buckets.
  • D. Create IAM policies. Attach the policies to IAM users or IAM roles.

Answer: D

Explanation:
By creating IAM policies with specific permissions, you can restrict access to Amazon S3 buckets or objects based on the user's business group. These policies can be attached to IAM users or IAM roles associated with the ML engineers, ensuring that each engineer can only access training data belonging to their group. This approach is secure, scalable, and aligns with AWS best practices for access control.


NEW QUESTION # 64
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