VALID MLA-C01 TEST GUIDE & MLA-C01 VALID TEST DURATION

Valid MLA-C01 Test Guide & MLA-C01 Valid Test Duration

Valid MLA-C01 Test Guide & MLA-C01 Valid Test Duration

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Amazon AWS Certified Machine Learning Engineer - Associate Sample Questions (Q11-Q16):

NEW QUESTION # 11
An ML engineer is evaluating several ML models and must choose one model to use in production. The cost of false negative predictions by the models is much higher than the cost of false positive predictions.
Which metric finding should the ML engineer prioritize the MOST when choosing the model?

  • A. Low recall
  • B. High precision
  • C. Low precision
  • D. High recall

Answer: D

Explanation:
Recall measures the ability of a model to correctly identify all positive cases (true positives) out of all actual positives, minimizing false negatives. Since the cost of false negatives is much higher than falsepositives in this scenario, the ML engineer should prioritize models with high recall to reduce the likelihood of missing positive cases.


NEW QUESTION # 12
A company has a large, unstructured dataset. The dataset includes many duplicate records across several key attributes.
Which solution on AWS will detect duplicates in the dataset with the LEAST code development?

  • A. Use Amazon SageMaker Data Wrangler to pre-process and detect duplicates.
  • B. Use the AWS Glue FindMatches transform to detect duplicates.
  • C. Use Amazon Mechanical Turk jobs to detect duplicates.
  • D. Use Amazon QuickSight ML Insights to build a custom deduplication model.

Answer: B

Explanation:
Scenario:The dataset contains duplicate records that need to be detected with minimal code development.
Why FindMatches in AWS Glue?
* Purpose-Built for Deduplication:The FindMatches transform in AWS Glue is specifically designed to identify duplicate records in structured or semi-structured datasets.
* Machine Learning-Based:It uses ML to identify duplicates based on configurable thresholds and provides flexibility for tuning accuracy.
* Low Code Overhead:Minimal development effort is required as Glue provides an interactive console for configuring and running FindMatches transforms.
Steps to Implement:
* Prepare the Data:Upload the unstructured dataset to an S3 bucket and define a schema if needed.
* Create a Glue Job:
* Use the AWS Glue Studio to create a job and select the FindMatches transform.
* Specify key attributes for deduplication.
* Run and Evaluate:Execute the Glue job, and review the results for duplicates.
* Resolve Duplicates:Export results to an S3 bucket or process them as needed.
References:
* AWS Glue FindMatches Documentation
* FindMatches Transform Example


NEW QUESTION # 13
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 ## 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?

  • A. Use Amazon SageMaker Pipelines to create a pipeline of data processing steps. Automate the pipeline by using Amazon EventBridge.
  • B. Process data at each step by using Amazon SageMaker Data Wrangler. Automate the process by using Data Wrangler jobs.
  • C. Use Amazon SageMaker notebooks for each data processing step. Automate the process by using Amazon EventBridge.
  • D. Process data at each step by using AWS Lambda functions. Automate the process by using AWS Step Functions and Amazon EventBridge.

Answer: A

Explanation:
Amazon SageMaker Pipelines is designed for creating, automating, and managing end-to-end ML workflows, including complex data preprocessing tasks. It supports handling large datasets and can integrate with custom steps, such as NLP transformations. By combining SageMaker Pipelines with Amazon EventBridge, the entire workflow can be triggered and automated efficiently, meeting the requirements for scalability, automation, and processing complexity.


NEW QUESTION # 14
A company is building a deep learning model on Amazon SageMaker. The company uses a large amount of data as the training dataset. The company needs to optimize the model's hyperparameters to minimize the loss function on the validation dataset.
Which hyperparameter tuning strategy will accomplish this goal with the LEAST computation time?

  • A. Grid search
  • B. Bayesian optimization
  • C. Hyperbaric!
  • D. Random search

Answer: C

Explanation:
Hyperband is a hyperparameter tuning strategy designed to minimize computation time by adaptively allocating resources to promising configurations and terminating underperforming ones early. It efficiently balances exploration and exploitation, making it ideal for large datasets and deep learning models where training can be computationally expensive.


NEW QUESTION # 15
A company stores time-series data about user clicks in an Amazon S3 bucket. The raw data consists of millions of rows of user activity every day. ML engineers access the data to develop their ML models.
The ML engineers need to generate daily reports and analyze click trends over the past 3 days by using Amazon Athena. The company must retain the data for 30 days before archiving the data.
Which solution will provide the HIGHEST performance for data retrieval?

  • A. Keep all the time-series data without partitioning in the S3 bucket. Manually move data that is older than 30 days to separate S3 buckets.
  • B. Put each day's time-series data into its own S3 bucket. Use S3 Lifecycle policies to archive S3 buckets that hold data that is older than 30 days to S3 Glacier Flexible Retrieval.
  • C. Create AWS Lambda functions to copy the time-series data into separate S3 buckets. Apply S3 Lifecycle policies to archive data that is older than 30 days to S3 Glacier Flexible Retrieval.
  • D. Organize the time-series data into partitions by date prefix in the S3 bucket. Apply S3 Lifecycle policies to archive partitions that are older than 30 days to S3 Glacier Flexible Retrieval.

Answer: D

Explanation:
Partitioning the time-series data by date prefix in the S3 bucket significantly improves query performance in Amazon Athena by reducing the amount of data that needs to be scanned during queries. This allows the ML engineers to efficiently analyze trends over specific time periods, such as the past 3 days. Applying S3 Lifecycle policies to archive partitions older than 30 days to S3 Glacier FlexibleRetrieval ensures cost- effective data retention and storage management while maintaining high performance for recent data retrieval.


NEW QUESTION # 16
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