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Amazon AWS Certified Machine Learning Engineer - Associate Sample Questions (Q165-Q170):

NEW QUESTION # 165
A company has an ML model in Amazon SageMaker AI. An ML engineer needs to implement a monitoring solution to automatically detect changes in the input data distribution of model features.
Which solution will meet this requirement with the LEAST operational overhead?

A. Configure SageMaker Model Monitor. Establish a data quality baseline. Ensure that the emit_metrics option is enabled in the baseline constraints file. Configure an Amazon CloudWatch alarm to notify the company about changes in specific metrics that are related to data quality.
B. Configure SageMaker Model Monitor. Establish a model quality baseline. Ensure that the comparison_method option is set to Robust in the baseline constraints file. Configure an Amazon CloudWatch alarm to notify the company about changes in model quality metrics.
C. Use Amazon CloudWatch to directly observe the SageMaker AI endpoint ' s performance metrics.Manually analyze the CloudWatch logs for indicators of data drift or shifts in feature distribution.
D. Use SageMaker Debugger with custom rules to track shifts in feature distributions. Configure Amazon CloudWatch alarms to notify the company when the rules detect significant changes.

Answer: A

Explanation:
Option A is correct because the requirement is to detect changes in the input data distribution of model features, which is a data quality / data drift monitoring problem. AWS documentation states that Amazon SageMaker Model Monitor uses rules to detect data drift and alerts you when it happens. The documented workflow is to enable data capture, create a baseline from training data, and then run monitoring jobs that compare incoming inference data against that baseline. That directly matches the need to automatically detect changes in feature distributions.
AWS also documents that Model Monitor can emit metrics to Amazon CloudWatch, and those metrics can be used with CloudWatch alarms to notify teams when data quality drifts beyond acceptable thresholds. That makes Option A the lowest-operational-overhead solution because it uses SageMaker's built-in monitoring capability plus managed alerting, rather than requiring custom drift logic. The inclusion of emit_metrics and CloudWatch alarming is consistent with the SageMaker monitoring pattern for automated notification.
The other options are weaker. Option B is for model quality monitoring, which focuses on prediction performance against ground truth, not shifts in the input feature distribution. Option C uses SageMaker Debugger, which is aimed at training-time debugging and custom rule analysis rather than managed production data drift monitoring. Option D relies on manual log analysis and endpoint performance metrics, which does not directly solve feature-distribution drift detection and adds more operational effort. Therefore, the best AWS-documented answer is A.

NEW QUESTION # 166
An ML engineer needs to choose the most appropriate data format for various data uses. Different teams will access the data for analytics, ML, and reporting purposes.
Select the correct data format from the following list to meet the requirements for each use case. Select each data format one time. (Select FOUR.)

Answer:

Explanation:

The best answers are Parquet, JSON, CSV, and ORC in that order.
Parquet is the strongest choice for complex analytical queries over large structured datasets because it is a columnar format. Columnar storage allows query engines such as Amazon Athena, AWS Glue, and Spark to read only the columns required by the query instead of scanning full rows. AWS documentation states that Apache Parquet and ORC are columnar storage formats optimized for fast retrieval in analytical applications, and that column-level compression can reduce storage space and I/O during query processing. This directly matches the need to filter, aggregate, reduce query response time, and lower storage/query cost.
JSON is correct for semi-structured real-time logs because JSON supports flexible and nested data structures.
AWS Glue documentation describes JSON as a format for data structures with consistent shape but flexible contents and notes that it is not row-based or column-based. That makes it appropriate for application logs, event records, and evolving schemas used later for analytics or ML ingestion.
CSV is correct for small spreadsheet exports and occasional human-readable analysis. AWS Glue describes CSV as a minimal, row-based data format. CSV is widely supported by spreadsheet tools and is easy for humans to inspect, but it is not ideal for large-scale analytical performance because it lacks efficient column pruning and rich schema support.
ORC is correct for the Apache Hive read-heavy big data pipeline. ORC is a performance-oriented, column- based format, and it is strongly associated with Hive-based analytics workloads. It provides high compression and efficient reads, making it well suited for structured data in read-heavy big data pipelines.

NEW QUESTION # 167
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. Create a new model by using Amazon SageMaker Autopilot. Report the model's achieved performance.
B. Implement custom scripts to perform data pre-processing, multiple linear regression, and performance evaluation. Run the scripts on Amazon EC2 instances.
C. Configure Amazon Macie to analyze the dataset and to create a model. Report the model's achieved performance.
D. Select a model from Amazon Bedrock. Tune the model with the data. Report the model's achieved performance.

Answer: A

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 # 168
A company has used Amazon SageMaker to deploy a predictive ML model in production. The company is using SageMaker Model Monitor on the model. After a model update, an ML engineer notices data quality issues in the Model Monitor checks.
What should the ML engineer do to mitigate the data quality issues that Model Monitor has identified?

A. Include additional data in the existing training set for the model. Retrain and redeploy the model.
B. Initiate a manual Model Monitor job that uses the most recent production data.
C. Adjust the model's parameters and hyperparameters.
D. Create a new baseline from the latest dataset. Update Model Monitor to use the new baseline for evaluations.

Answer: D

NEW QUESTION # 169
A company has significantly increased the amount of data that is stored as .csv files in an Amazon S3 bucket.
Data transformation scripts and queries are now taking much longer than they used to take.
An ML engineer must implement a solution to optimize the data for query performance.
Which solution will meet this requirement with the LEAST operational overhead?

A. Configure an AWS Glue extract, transform, and load (ETL) job to convert the .csv files to Apache Parquet format.
B. Configure an AWS Glue job to drop columns that have string type values and to save the results to the S3 bucket.
C. Configure an Amazon EMR cluster to process the data that is in the S3 bucket.
D. Configure an AWS Lambda function to split the .csv files into smaller objects in the S3 bucket.

Answer: A

Explanation:
AWS documentation strongly recommends using columnar storage formats to optimize analytical query performance on large datasets stored in Amazon S3. Apache Parquet is a columnar, compressed, and splittable file format that significantly improves query speed and reduces I/O compared to row-based formats such as CSV.
By using AWS Glue to convert CSV files into Parquet format, the company can achieve faster query execution with minimal operational overhead. Glue is fully managed, serverless, and integrates natively with S3, Amazon Athena, and Amazon Redshift Spectrum.
Option A does not improve query efficiency; splitting files still leaves the data in an inefficient row-based format. Option B may reduce data size but does not address the fundamental inefficiency of CSV for analytics. Option D introduces significant operational overhead because Amazon EMR requires cluster provisioning, scaling, and maintenance.
Therefore, converting CSV files to Apache Parquet using AWS Glue ETL is the most efficient and low- maintenance solution.

NEW QUESTION # 170
......

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