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Amazon AWS Certified Machine Learning Engineer - Associate Sample Questions (Q18-Q23):

NEW QUESTION # 18
A company uses Amazon Athena to query a dataset in Amazon S3. The dataset has a target variable that the company wants to predict.
The company needs to use the dataset in a solution to determine if a model can predict the target variable.
Which solution will provide this information with the LEAST development effort?

A. Configure Amazon Macie to analyze the dataset and to create a model. Report the model's achieved performance.
B. Create a new model by using Amazon SageMaker Autopilot. Report the model's achieved performance.
C. Select a model from Amazon Bedrock. Tune the model with the data. Report the model's achieved performance.
D. Implement custom scripts to perform data pre-processing, multiple linear regression, and performance evaluation. Run the scripts on Amazon EC2 instances.

Answer: B

Explanation:
Amazon SageMaker Autopilot automates the process of building, training, and tuning machine learning models. It provides insights into whether the target variable can be effectively predicted by evaluating the model's performance metrics. This solution requires minimal development effort as SageMaker Autopilot handles data preprocessing, algorithm selection, and hyperparameter optimization automatically, making it the most efficient choice for this scenario.

NEW QUESTION # 19
An advertising company uses AWS Lake Formation to manage a data lake. The data lake contains structured data and unstructured data. The company's ML engineers are assigned to specific advertisement campaigns.
The ML engineers must interact with the data through Amazon Athena and by browsing the data directly in an Amazon S3 bucket. The ML engineers must have access to only the resources that are specific to their assigned advertisement campaigns.
Which solution will meet these requirements in the MOST operationally efficient way?

A. Configure S3 bucket policies to restrict access to the S3 bucket based on the ML engineers' campaigns.
B. Use Lake Formation to authorize AWS Glue to access the S3 bucket. Configure Lake Formation tags to map ML engineers to their campaigns.
C. Store users and campaign information in an Amazon DynamoDB table. Configure DynamoDB Streams to invoke an AWS Lambda function to update S3 bucket policies.
D. Configure IAM policies on an AWS Glue Data Catalog to restrict access to Athena based on the ML engineers' campaigns.

Answer: B

Explanation:
AWS Lake Formation provides fine-grained access control and simplifies data governance for data lakes. By configuring Lake Formation tags to map ML engineers to their specific campaigns, you can restrict access to both structured and unstructured data in the data lake. This method is operationally efficient, as it centralizes access control management within Lake Formation and ensures consistency across Amazon Athena and S3 bucket access without requiring manual updates to policies or DynamoDB-based custom logic.

NEW QUESTION # 20
A company wants to improve the sustainability of its ML operations.
Which actions will reduce the energy usage and computational resources that are associated with the company's training jobs? (Choose two.)

A. Use Amazon SageMaker Debugger to stop training jobs when non-converging conditions are detected.
B. Use Amazon SageMaker Ground Truth for data labeling.
C. Use AWS Trainium instances for training.
D. Deploy models by using AWS Lambda functions.
E. Use PyTorch or TensorFlow with the distributed training option.

Answer: A,C

Explanation:
SageMaker Debuggercan identify when a training job is not converging or is stuck in a non-productive state.
By stopping these jobs early, unnecessary energy and computational resources are conserved, improving sustainability.
AWS Trainiuminstances are purpose-built for ML training and are optimized for energy efficiency and cost- effectiveness. They use less energy per training task compared to general-purpose instances, making them a sustainable choice.

NEW QUESTION # 21
An ML engineer needs to use data with Amazon SageMaker Canvas to train an ML model. The data is stored in Amazon S3 and is complex in structure. The ML engineer must use a file format that minimizes processing time for the data.
Which file format will meet these requirements?

A. CSV files compressed with Snappy
B. Apache Parquet files
C. JSON files compressed with gzip
D. JSON objects in JSONL format

Answer: B

Explanation:
Apache Parquet is a columnar storage file format optimized for complex and large datasets. It provides efficient reading and processing by accessing only the required columns, which reduces I/O and speeds up data handling. This makes it ideal for use with Amazon SageMaker Canvas, where minimizing processing time is important for training ML models. Parquet is also compatible with S3 and widely supported in data analytics and ML workflows.

NEW QUESTION # 22
A company regularly receives new training data from the vendor of an ML model. The vendor delivers cleaned and prepared data to the company's Amazon S3 bucket every 3-4 days.
The company has an Amazon SageMaker pipeline to retrain the model. An ML engineer needs to implement a solution to run the pipeline when new data is uploaded to the S3 bucket.
Which solution will meet these requirements with the LEAST operational effort?

A. Create an AWS Lambda function that scans the S3 bucket. Program the Lambda function to initiate the pipeline when new data is uploaded.
B. Create an Amazon EventBridge rule that has an event pattern that matches the S3 upload. Configure the pipeline as the target of the rule.
C. Create an S3 Lifecycle rule to transfer the data to the SageMaker training instance and to initiate training.
D. Use Amazon Managed Workflows for Apache Airflow (Amazon MWAA) to orchestrate the pipeline when new data is uploaded.

Answer: B

Explanation:
UsingAmazon EventBridgewith an event pattern that matches S3 upload events provides an automated, low- effort solution. When new data is uploaded to the S3 bucket, the EventBridge rule triggers the SageMaker pipeline. This approach minimizes operational overhead by eliminating the need for custom scripts or external orchestration tools while seamlessly integrating with the existing S3 and SageMaker setup.

NEW QUESTION # 23
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