Pass the Amazon Web Services AWS Certified Associate MLA-C01 Questions and answers with CertsForce

Amazon Comprehend is a fully managed natural language processing (NLP) service that includes a built-in sentiment analysis feature. It can quickly and efficiently analyze text data to determine whether the sentiment is positive, negative, neutral, or mixed. Using Amazon Comprehend requires minimal setup and provides accurate results without the need to train and deploy custom models, making it the fastest and most efficient solution for this task.

The key requirement is semantic search over text documents that already reside in Amazon S3. AWS provides Amazon Kendra, a fully managed service specifically designed for semantic and natural language search across unstructured text.

Amazon Kendra natively supports S3 connectors, which can ingest documents directly from an S3 bucket, automatically process the text, generate embeddings, and index the content for semantic retrieval. This removes the need for the company to manage embedding generation, vector storage, or similarity search infrastructure. Queries can be expressed in natural language, making Kendra well suited for RAG-style applications.

Option A and B require building and maintaining a custom embedding pipeline and do not provide a true vector similarity search engine using SQL. SageMaker Feature Store is not intended to function as a vector database for semantic search.

Option D is incorrect because Amazon Textract is an OCR service for extracting text from scanned documents and images; it does not support semantic search.

Therefore, ingesting documents using the Amazon Kendra S3 connector and querying Kendra is the correct and AWS-recommended solution.

SageMaker script mode allows ML engineers to bring existing training scripts written for frameworks such as PyTorch and TensorFlow directly into SageMaker with minimal changes. AWS documentation explicitly states that script mode is designed to support migration of existing ML workloads.

With script mode, the user provides a custom training script, and SageMaker handles infrastructure provisioning, distributed training, logging, and model artifact storage. This makes script mode ideal for companies that want to reuse established codebases without rewriting them.

Built-in algorithms require adopting AWS-provided implementations. SageMaker Canvas is a no-code tool, and JumpStart provides pretrained models and templates but does not focus on custom script reuse.

Therefore, Option D is the correct and AWS-recommended choice.

This problem describes severe class imbalance in an image classification task, where the minority class has poor predictive performance. In such cases, accuracy is a misleading metric, because a model can achieve high accuracy by predicting only the majority class. AWS ML best practices recommend using F1 score, which balances precision and recall and is more appropriate for imbalanced classification problems.

To improve performance on the minority image class, image augmentation is the preferred approach. Augmentation techniques—such as rotation, cropping, flipping, and brightness adjustment—create realistic new training examples while preserving semantic meaning. AWS documentation recommends augmentation for computer vision workloads to improve generalization without collecting new data.

SMOTE (Options C and D) is designed for tabular data, not image data, and generating synthetic pixel-level images using SMOTE is not appropriate or supported in typical computer vision pipelines.

Option A is incorrect because optimizing for accuracy does not address minority-class performance. Option D is incorrect because SMOTE is unsuitable for images.

Therefore, optimizing for F1 score and using image augmentation on the minority class is the correct solution.

AWS strongly recommends converting large CSV datasets into columnar formats such as Apache Parquet to improve query performance. Parquet reduces I/O by reading only the required columns and applies compression, which significantly speeds up analytics workloads.

AWS Glue ETL jobs provide a fully managed, serverless way to perform this conversion with minimal operational overhead. Once converted, the Parquet files can be queried efficiently by services such as Amazon Athena, Redshift Spectrum, and SageMaker processing jobs.

Splitting CSV files does not address inefficient storage format. Dropping columns risks data loss. Amazon EMR introduces infrastructure management overhead and is unnecessary for a straightforward format conversion.

AWS documentation clearly identifies CSV-to-Parquet conversion using Glue ETL as a best practice for scalable analytics.

Therefore, Option C is the correct answer.

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.

Amazon Macie is a fully managed data security and privacy service that uses machine learning to discover and classify sensitive data in Amazon S3. It is purpose-built to identify sensitive data with minimal operational overhead. After identifying the sensitive data, you can use AWS Lambda functions to automate the process of removing or redacting the sensitive data, ensuring efficiency and integration with the hybrid cloud environment. This solution requires the least development effort and aligns with the requirement to handle sensitive data effectively.

For near real-time predictions, AWS documentation recommends Amazon SageMaker real-time inference endpoints. These endpoints support automatic scaling based on metrics such as ConcurrentInvocationsPerInstance, ensuring low latency and high availability during traffic spikes.

For long-running historical analysis, SageMaker Processing jobs are the appropriate solution. Processing jobs are designed for batch workloads, can run for hours, and integrate cleanly with SageMaker pipelines. Scheduling them with Amazon EventBridge provides a fully managed, scalable, and serverless solution.

AWS Lambda is unsuitable for multi-hour workloads. EC2 Auto Scaling adds unnecessary infrastructure management overhead.

Therefore, Options A and C together meet all requirements and align with AWS best practices.

AWS provides Amazon SageMaker Pipelines as the native CI/CD solution for ML. Pipelines can automatically trigger retraining when new data arrives in Amazon S3, handle large datasets (such as 10 GB files), and orchestrate preprocessing, training, evaluation, and deployment steps.

For governance and auditing, Amazon SageMaker Model Registry integrates directly with Pipelines to manage model versions, approval status, and metadata such as training metrics. This combination eliminates the need for custom tracking logic and ensures traceability across the ML lifecycle.

Option A lacks native ML lineage and version governance. Option C is unsuitable for large data and complex workflows. Option D is manual and not scalable.

Therefore, using SageMaker Pipelines with the Model Registry is the correct and AWS-recommended solution.

The correct answer is A. Create Amazon Managed Streaming for Apache Kafka (Amazon MSK) Serverless clusters to process the data.

Amazon MSK Serverless allows organizations to run Apache Kafka workloads without managing or provisioning the underlying infrastructure. Unlike MSK Provisioned clusters (Option B), serverless clusters automatically scale to accommodate variable workloads and handle operational tasks such as patching, scaling, and monitoring. This meets the company’s requirement to avoid managing or scaling infrastructure while providing near real-time streaming analytics.

Option C, using Amazon Kinesis Data Streams with Application Auto Scaling, provides a managed streaming solution but is not based on open-source technology. While Kinesis can handle near real-time data, the question specifically emphasizes the company’s desire for open-source technology, which points toward Kafka.

Option D, self-hosting Apache Flink on EC2, requires manual management of servers, scaling, patching, and container orchestration. This approach contradicts the requirement of not managing or scaling infrastructure and introduces operational complexity.

MSK Serverless provides seamless integration with open-source Kafka APIs, enabling applications to produce and consume streaming data in real time while AWS handles scaling and availability. It is ideal for analytics pipelines where data ingestion is continuous and variable, and infrastructure overhead must be minimized.

By choosing MSK Serverless, the company can implement near real-time updates for its analytics platform using open-source Kafka without the operational burden of cluster management, fully aligning with AWS best practices for deployment and orchestration of ML workflows in a managed, scalable, and serverless manner.