MLS-C01 Exploratory Data Analysis Practice Question
A data engineer ingests streaming data into Amazon Kinesis Data Streams. The data science team needs to analyze the data using Amazon SageMaker notebooks. What is the most efficient way to provide access to the stream data for ad-hoc exploration?
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Use the Kinesis connector for Spark to read data directly from the stream into a Spark DataFrame in the notebook.
The Kinesis connector for Spark enables direct reading of streaming data into a Spark DataFrame in the SageMaker notebook, allowing real-time ad-hoc analysis with minimal latency. Option A is incorrect because writing to DynamoDB via Lambda adds unnecessary transformation steps and latency, and DynamoDB is not optimized for large-scale streaming data exploration. Option B is incorrect because using Kinesis Firehose to deliver data to S3 and then querying with Athena introduces significant latency (data is written in batches) and is not suitable for real-time exploration. Option C is incorrect because the Kinesis Agent is designed for sending data from sources to Kinesis, not for consuming or reading data; it cannot be used to read stream data into a notebook.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Create an AWS Lambda function to transform and write data to DynamoDB, then query DynamoDB from the notebook.
Why it's wrong here
Unnecessary transformation and overhead.
- ✗
Configure a Kinesis Firehose delivery stream to deliver data to an S3 bucket, then query the data from the notebook using Athena.
Why it's wrong here
Adds latency and complexity; not real-time.
- ✗
Install the Kinesis Agent on the SageMaker notebook instance and configure it to write data to a local file.
Why it's wrong here
Kinesis Agent is for sending data to streams, not consuming.
- ✓
Use the Kinesis connector for Spark to read data directly from the stream into a Spark DataFrame in the notebook.
Why this is correct
Direct, real-time access for ad-hoc exploration.
Quick reference
AWS S3 Storage Class Comparison
| Storage Class | Min Duration | Retrieval | Use Case |
|---|---|---|---|
| S3 Standard | None | Immediate | Frequently accessed data |
| S3 Standard-IA | 30 days | Immediate | Infrequent access, rapid retrieval |
| S3 One Zone-IA | 30 days | Immediate | Non-critical infrequent data |
| S3 Intelligent-Tiering | None | Immediate–hours | Unknown or changing access patterns |
| S3 Glacier Instant | 90 days | Milliseconds | Archive with instant retrieval |
| S3 Glacier Flexible | 90 days | Minutes–hours | Archive, flexible retrieval |
| S3 Glacier Deep Archive | 180 days | Hours | Long-term compliance archive |
Go deeper
Related to this question
About these practice questions
Courseiva writes every MLS-C01 question from scratch — 1,672 in total, each with an explanation and a wrong-answer breakdown. None are copied from real exams or dumps. Learn why practice questions differ from exam dumps →
JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This MLS-C01 practice question is part of Courseiva's free Amazon Web Services certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the MLS-C01 exam.