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| Certification Vendor: | Amazon Web Services (AWS) |
|---|---|
| Exam Name: | AWS Certified Machine Learning Engineer - Associate |
| Exam Number: | MLA-C01 |
| Exam Duration: | 170 minutes |
| Exam Format: | Multiple response, Multiple choice |
| Passing Score: | 1000-2000 scaled score (passing score approximately 720) |
| Related Certifications: | AWS Certified Solutions Architect - Associate AWS Certified Data Engineer - Associate AWS Certified Developer - Associate |
| Exam Price: | 150 USD |
| Available Languages: | English, Japanese, Traditional Chinese, Korean, Simplified Chinese |
| Real Exam Qty: | 85 |
| Certificate Validity Period: | 3 years |
| Sample Questions: | Amazon MLA-C01 Sample Questions |
| Exam Way: | Online proctored (Pearson VUE) or in-person testing center |
| Pre Condition: | Recommended: 2+ years of ML engineering experience, familiarity with AWS ML services |
| Official Syllabus URL: | https://aws.amazon.com/certification/certified-machine-learning-engineer-associate/ |
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NEW QUESTION # 99
A company ingests sales transaction data using Amazon Data Firehose into Amazon OpenSearch Service. The Firehose buffer interval is set to 60 seconds.
The company needs sub-second latency for a real-time OpenSearch dashboard.
Which architectural change will meet this requirement?
Answer: A
Explanation:
Amazon Data Firehose supports near real-time delivery by configuring the buffer interval and buffer size.
AWS documentation states that setting the buffer interval to the minimum (as low as 1 second) enables low- latency ingestion.
By using zero or minimal buffering and tuning PutRecordBatch, data is delivered to OpenSearch almost immediately, enabling sub-second dashboard updates.
DataSync and SQS are not designed for real-time streaming analytics. Increasing the buffer interval worsens latency.
AWS explicitly recommends Firehose with minimal buffering for real-time OpenSearch ingestion.
Therefore, Option A is the correct and AWS-verified solution.
NEW QUESTION # 100
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?
Answer: C
Explanation:
The key requirements are real-time processing, high throughput, and minimal operational overhead. Amazon Kinesis Data Streams is designed for ingesting thousands of events per second with low latency.
For anomaly detection on streaming data, Amazon Managed Service for Apache Flink provides a built-in Random Cut Forest (RCF) function. RCF is an unsupervised anomaly detection algorithm that works well on numerical streaming data and does not require labeled training data.
This fully managed combination eliminates the need to deploy or maintain SageMaker endpoints, EC2 instances, or custom ML pipelines. Options B and C introduce unnecessary infrastructure and model management overhead. Option D is batch-oriented and unsuitable for real-time anomaly detection.
Therefore, using Kinesis Data Streams with Flink's built-in Random Cut Forest is the most scalable and low- overhead solution.
NEW QUESTION # 101
A company has a team of data scientists who use Amazon SageMaker notebook instances to test ML models.
When the data scientists need new permissions, the company attaches the permissions to each individual role that was created during the creation of the SageMaker notebook instance.
The company needs to centralize management of the team ' s permissions.
Which solution will meet this requirement?
Answer: C
Explanation:
Managing permissions for multiple Amazon SageMaker notebook instances can become complex when handled individually. To centralize and streamline permission management, AWS recommends creating a single IAM role with the necessary permissions and attaching this role to each notebook instance used by the data science team.
Steps to Implement the Solution:
Create a Single IAM Role with Necessary Permissions:
Define an IAM role that encompasses all permissions required by the data scientists for their tasks. This includes permissions for SageMaker operations and any other AWS services they interact with.
AWS provides managed policies like AmazonSageMakerFullAccess that can be attached to the role to grant comprehensive SageMaker permissions.(IAM Policies for SageMaker) Attach the IAM Role to Each Notebook Instance:
When creating or updating a SageMaker notebook instance, specify the IAM role created in the previous step.
This ensures that all notebook instances operate under a consistent set of permissions.
In the SageMaker console, during the notebook instance setup, you can choose an existing IAM role to associate with the instance.(Creating SageMaker Workspaces) Benefits of This Approach:
Centralized Permission Management:By using a single IAM role, you simplify the process of updating permissions. Changes to the role ' s policies automatically propagate to all associated notebook instances, ensuring consistent access control.
Adherence to Best Practices:AWS recommends using IAM roles to manage permissions for applications running on services like SageMaker. This approach avoids the need to manage individual user permissions separately.(IAM Best Practices for SageMaker) Alternative Options and Their Drawbacks:
Option B: Creating a single IAM group and adding data scientists to it does not directly associate the group with notebook instances. IAM groups are used to manage user permissions, not to assign roles to AWS resources like notebook instances.
Option C: Using a single IAM user with the AdministratorAccess policy is not recommended due to security risks associated with granting broad permissions and the challenges in managing shared user credentials.
Option D: Associating an IAM group with a role and then with notebook instances is not a valid approach, as IAM groups cannot be directly associated with AWS resources.
Conclusion: Option A is the most effective solution to centralize and manage permissions for SageMaker notebook instances, aligning with AWS best practices for IAM role management.
References:
AWS Documentation: IAM Policies for SageMaker
AWS Documentation: Creating SageMaker Workspaces
AWS Documentation: IAM Best Practices for SageMaker
NEW QUESTION # 102
Case study
An ML engineer is developing a fraud detection model on AWS. The training dataset includes transaction logs, customer profiles, and tables from an on-premises MySQL database. The transaction logs and customer profiles are stored in Amazon S3.
The dataset has a class imbalance that affects the learning of the model's algorithm. Additionally, many of the features have interdependencies. The algorithm is not capturing all the desired underlying patterns in the data.
Before the ML engineer trains the model, the ML engineer must resolve the issue of the imbalanced data.
Which solution will meet this requirement with the LEAST operational effort?
Answer: A
Explanation:
Problem Description:
* The training dataset has a class imbalance, meaning one class (e.g., fraudulent transactions) has fewer samples compared to the majority class (e.g., non-fraudulent transactions). This imbalance affects the model's ability to learn patterns from the minority class.
Why SageMaker Data Wrangler?
* SageMaker Data Wrangler provides a built-in operation called "Balance Data," which includes oversampling and undersampling techniques to address class imbalances.
* Oversampling the minority class replicates samples of the minority class, ensuring the algorithm receives balanced inputs without significant additional operational overhead.
Steps to Implement:
* Import the dataset into SageMaker Data Wrangler.
* Apply the "Balance Data" operation and configure it to oversample the minority class.
* Export the balanced dataset for training.
Advantages:
* Ease of Use: Minimal configuration is required.
* Integrated Workflow: Works seamlessly with the SageMaker ecosystem for preprocessing and model training.
* Time Efficiency: Reduces manual effort compared to external tools or scripts.
NEW QUESTION # 103
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?
Answer: C
NEW QUESTION # 104
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