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Free AWS Certified Machine Learning Engineer - Associate Practice Questions

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Exam style questions across every MLA-C01 domain

Last Update 18 hours ago
Total Questions : 241

Start with our free MLA-C01 practice questions, carefully crafted to mirror the domains, phrasing, and difficulty of the real AWS Certified Associate exam. Each MLA-C01 exam question comes with a detailed rationale that explains not just which answer is correct but why the others fall short. That's how concepts stick. Use the free set to benchmark yourself: identify your Amazon Web Services weak domains, see where you're losing marks, and build a focused study plan in minutes.

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Question # 1

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?

Options:

A.  

Use Amazon Rekognition to analyze sentiments of the chat conversations.

B.  

Train a Naive Bayes classifier to analyze sentiments of the chat conversations.

C.  

Use Amazon Comprehend to analyze sentiments of the chat conversations.

D.  

Use random forests to classify sentiments of the chat conversations.

Discussion 0
Question # 2

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?

Options:

A.  

Use zero buffering in the Firehose stream and tune the PutRecordBatch batch size.

B.  

Replace Firehose with AWS DataSync and enhanced fan-out consumers.

C.  

Increase the Firehose buffer interval to 120 seconds.

D.  

Replace Firehose with Amazon SQS.

Discussion 0
Question # 3

A company collects customer data daily and stores it as compressed files in an Amazon S3 bucket partitioned by date. Each month, analysts process the data, check data quality, and upload results to Amazon QuickSight dashboards.

An ML engineer needs to automatically check data quality before the data is sent to QuickSight, with the LEAST operational overhead.

Which solution will meet these requirements?

Options:

A.  

Run an AWS Glue crawler monthly and use AWS Glue Data Quality rules to check data quality.

B.  

Run an AWS Glue crawler and create a custom AWS Glue job with PySpark to evaluate data quality.

C.  

Use AWS Lambda with Python scripts triggered by S3 uploads to evaluate data quality.

D.  

Send S3 events to Amazon SQS and use Amazon CloudWatch Insights to evaluate data quality.

Discussion 0
Question # 4

An ML engineer is collecting data to train a classification ML model by using Amazon SageMaker AI. The target column can have two possible values: Class A or Class

B.  

The ML engineer wants to ensure that the number of samples for both Class A and Class B are balanced, without losing any existing training data. The ML engineer must test the balance of the training data.

Which solution will meet this requirement?

Options:

A.  

Use SageMaker Clarify to check for class imbalance (CI). If the value is equal to 0, then use random undersampling in SageMaker Data Wrangler to balance the classes.

B.  

Use SageMaker Clarify to check for class imbalance (CI). If the value is greater than 0, then use synthetic minority oversampling technique (SMOTE) in SageMaker Data Wrangler to balance the classes.

C.  

Use SageMaker JumpStart to generate a class imbalance (CI) report. If the value is greater than 0, then use random undersampling in SageMaker Studio to balance the classes.

D.  

Use SageMaker JumpStart to generate a class imbalance (CI) report. If the value is equal to 0, then use synthetic minority oversampling technique (SMOTE) in SageMaker Studio to balance the classes.

Discussion 0
Question # 5

A company has an ML model that needs to run one time each night to predict stock values. The model input is 3 MB of data that is collected during the current day. The model produces the predictions for the next day. The prediction process takes less than 1 minute to finish running.

How should the company deploy the model on Amazon SageMaker to meet these requirements?

Options:

A.  

Use a multi-model serverless endpoint. Enable caching.

B.  

Use an asynchronous inference endpoint. Set the InitialInstanceCount parameter to 0.

C.  

Use a real-time endpoint. Configure an auto scaling policy to scale the model to 0 when the model is not in use.

D.  

Use a serverless inference endpoint. Set the MaxConcurrency parameter to 1.

Discussion 0
Question # 6

A company uses Amazon SageMaker Studio to develop an ML model. The company has a single SageMaker Studio domain. An ML engineer needs to implement a solution that provides an automated alert when SageMaker AI compute costs reach a specific threshold.

Which solution will meet these requirements?

Options:

A.  

Add resource tagging by editing the SageMaker AI user profile in the SageMaker AI domain. Configure AWS Cost Explorer to send an alert when the threshold is reached.

B.  

Add resource tagging by editing the SageMaker AI user profile in the SageMaker AI domain. Configure AWS Budgets to send an alert when the threshold is reached.

C.  

Add resource tagging by editing each user ' s IAM profile. Configure AWS Cost Explorer to send an alert when the threshold is reached.

D.  

Add resource tagging by editing each user ' s IAM profile. Configure AWS Budgets to send an alert when the threshold is reached.

Discussion 0
Question # 7

An ML engineer normalized training data by using min-max normalization in AWS Glue DataBrew. The ML engineer must normalize production inference data in the same way before passing the data to the model.

Which solution will meet this requirement?

Options:

A.  

Apply statistics from a well-known dataset to normalize the production samples.

B.  

Keep the min-max normalization statistics from the training set and use them to normalize the production samples.

C.  

Calculate new min-max statistics from a batch of production samples and use them to normalize all production samples.

D.  

Calculate new min-max statistics from each production sample and use them to normalize all production samples.

Discussion 0
Question # 8

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.

The training dataset includes categorical data and numerical data. The ML engineer must prepare the training dataset to maximize the accuracy of the model.

Which action will meet this requirement with the LEAST operational overhead?

Options:

A.  

Use AWS Glue to transform the categorical data into numerical data.

B.  

Use AWS Glue to transform the numerical data into categorical data.

C.  

Use Amazon SageMaker Data Wrangler to transform the categorical data into numerical data.

D.  

Use Amazon SageMaker Data Wrangler to transform the numerical data into categorical data.

Discussion 0
Question # 9

A company has developed a computer vision model. The company needs to deploy the model into production on Amazon SageMaker AI. The company has not hosted a model on SageMaker AI previously.

An ML engineer needs to implement a solution to track model versions. The solution also must provide recommendations about which Amazon EC2 instance types to use to host the model.

Which solution will meet these requirements?

Options:

A.  

Register the model in Amazon Elastic Container Registry (Amazon ECR). Use AWS Compute Optimizer for recommendations about instance types.

B.  

Register the model in the SageMaker Model Registry. Use SageMaker Autopilot for recommendations about instance types.

C.  

Register the model in the SageMaker Model Registry. Use SageMaker Inference Recommender for recommendations about instance types.

D.  

Register the model in Amazon Elastic Container Registry (Amazon ECR). Use SageMaker Experiments for recommendations about instance types.

Discussion 0
Question # 10

A logistics company has installed in-vehicle cameras for basic monitoring of its drivers. The company wants to improve driver safety by identifying distractions that could lead to accidents.

Which solution will meet this requirement with the LEAST operational effort?

Options:

A.  

Use Amazon Rekognition eye gaze direction detection to monitor driver behavior and identify distractions.

B.  

Use Amazon SageMaker AI to customize an AI model to monitor driver behavior and identify distractions.

C.  

Integrate a third-party driver monitoring system with Amazon Rekognition to monitor driver behavior and identify distractions.

D.  

Use Amazon Comprehend to analyze text-based driver feedback and identify distractions.

Discussion 0

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