How hard is the AWS Certified Data Analytics Specialty Exam?

2024-01-17 08:09:34 SPOTOCLUB AWS 336
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The AWS Certified Data Analytics Specialty Exam Overview

The AWS Certified Data Analytics Specialty Exam serves as a robust validation of an individual's proficiency in leveraging AWS services to design and implement substantial big data solutions. Acknowledged for its complexity, the exam demands a profound grasp of the AWS ecosystem, big data technologies, and key data analytics principles.

Comprising 65 multiple-choice and multiple-response questions, candidates are allotted 3 hours to showcase their understanding of data collection, processing, storage, and analysis. The exam evaluates knowledge across various AWS services, including Amazon S3, Amazon EMR, Amazon Redshift, Amazon Kinesis, and Amazon QuickSight.

Scoring a minimum of 750 out of 1000 is imperative to pass, yet the exam's difficulty can vary based on an individual's experience and familiarity with AWS services and data analytics concepts.

Insights into AWS Certified Data Analytics Specialty

The AWS Certified Data Analytics Specialty certification aids organizations in identifying and advancing professionals with crucial competencies for executing cloud-based activities. Holding this certification signifies a comprehensive understanding of utilizing AWS data lakes and analytics services to extract insights from data. The exam also assesses candidates on designing, building, implementing, and safeguarding data analytics solutions within the AWS environment, including their proficiency with other AWS data analytics services.

Prerequisite Knowledge

Designed for individuals experienced in constructing, designing, securing, and managing analytics applications through AWS services, the AWS Certified Data Analytics - Specialty program recommends candidates to possess:

  • Five or more years of experience with data analytics tools.
  • A minimum of two years of hands-on experience using AWS.
  • Experience in developing, designing, securing, and maintaining analytics systems with AWS services.

With these prerequisites in mind, let's delve into the core aspects of the AWS Certified Data Analytics Specialty Exam.

Understanding the Difficulty Level

Successfully navigating the AWS Data Analytics Specialty certification demands an in-depth understanding of data analytics technologies and solutions. Beyond basic data analytics knowledge, candidates are expected to know which AWS tools or services are appropriate for specific issues. While fundamental data analytics questions are limited, the exam extends into a more comprehensive exploration of the subject matter. Therefore, a solid grasp of fundamental data analytics knowledge is essential before attempting the exam.

Candidates should be familiar with various data types, data storage methods, OLTP and OLAP systems, batch and stream processing, ACID and BASE compliance, and AWS services and pipelines aligned with these principles. The exam scrutinizes these areas, necessitating a thorough understanding for success.

Exam Format

An understanding of the exam format is crucial for effective preparation. The AWS Certified Data Analytics Specialty Exam includes:

  • 65 Multiple Choice and Multi-Response Questions.
  • A 180-minute duration for completion.
  • Available in English, Korean, Japanese, and Simplified Chinese.
  • Costing approximately $300 USD.
  • Passing scores between 75% and 80% are required for certification.

Next, let's explore the structure of the exam, providing insight into its domains and associated topics.

Exam Structure

The AWS Certified Data Analytics - Specialty Exam encompasses the following domains, each addressing specific fields and related issues:

Domain 1: Collection

1.1 Determine the operational characteristics of the collection system.

  • Evaluate data loss tolerance in failure scenarios.
  • Assess costs associated with data acquisition, transfer, and provisioning.
  • Identify failure scenarios and take remediation actions.
  • Determine data persistence at various points of data capture.
  • Identify the latency characteristics of the collection system.

1.2 Select a collection system that handles frequency, volume, and source of data.

  • Characterize volume and flow characteristics of incoming data.
  • Match flow characteristics to potential solutions.
  • Assess tradeoffs between various ingestion services.
  • Explain throughput capability of different data collection types.
  • Choose a collection solution satisfying connectivity constraints.

1.3 Select a collection system addressing key data properties.

  • Capture data changes at the source.
  • Discuss data structure, format, compression, and encryption requirements.
  • Distinguish the impact of out-of-order delivery and duplicate delivery of data.
  • Describe data transformation and filtering during the collection process.

Domain 2: Storage and Data Management

2.1 Determine the operational characteristics of the storage solution for analytics.

  • Determine appropriate storage service(s) based on cost vs. performance.
  • Understand durability, reliability, and latency characteristics of the storage solution.
  • Determine requirements for strong vs. eventual consistency.
  • Determine storage solution to address data freshness requirements.

2.2 Determine data access and retrieval patterns.

  • Determine storage solution based on update patterns.
  • Determine storage solution based on access patterns.
  • Determine storage solution for long-term vs. transient storage.
  • Determine storage solution for structured vs. semi-structured data.
  • Determine storage solution to address query latency requirements.

2.3 Select appropriate data layout, schema, structure, and format.

  • Determine mechanisms to address schema evolution requirements.
  • Select storage format based on task requirements.
  • Select compression/encoding strategies for chosen storage format.
  • Select data sorting and distribution strategies for efficient data access.
  • Explain cost and performance implications of different data distributions, layouts, and formats.
  • Implement data formatting and partitioning schemes for data-optimized analysis.

2.4 Define data lifecycle based on usage patterns and business requirements.

  • Determine strategy to address data lifecycle requirements.
  • Apply lifecycle and data retention policies to different storage solutions.

2.5 Determine the appropriate system for cataloging data and managing metadata.

  • Evaluate mechanisms for discovery of new and updated data sources.
  • Evaluate mechanisms for creating and updating data catalogs and metadata.
  • for searching and retrieving data catalogs and metadata.
    • Explain mechanisms for tagging and classifying data.

    Domain 3: Processing

    3.1 Determine appropriate data processing solution requirements.

    • Understand data preparation and usage requirements.
    • Understand different types of data sources and targets.
    • Evaluate performance and orchestration needs.
    • Evaluate appropriate services for cost, scalability, and availability.

    3.2 Design a solution for transforming and preparing data for analysis.

    • Apply appropriate ETL/ELT techniques for batch and real-time workloads.
    • Implement failover, scaling, and replication mechanisms.
    • Implement techniques to address concurrency needs.
    • Implement techniques to improve cost-optimization efficiencies.
    • Apply orchestration workflows.
    • Aggregate and enrich data for downstream consumption.

    3.3 Automate and operationalize data processing solutions.

    • Implement automated techniques for repeatable workflows.
    • Apply methods to identify and recover from processing failures.
    • Deploy logging and monitoring solutions to enable auditing and traceability.

    Domain 4: Analysis and Visualization

    4.1 Determine the operational characteristics of the analysis and visualization solution.

    • Determine costs associated with analysis and visualization.
    • Determine scalability associated with analysis.
    • Determine failover recovery and fault tolerance within the RPO/RTO.
    • Determine the availability characteristics of an analysis tool.
    • Evaluate dynamic, interactive, and static presentations of data.
    • Translate performance requirements to an appropriate visualization approach.

    4.2 Select the appropriate data analysis solution for a given scenario.

    • Evaluate and compare analysis solutions.
    • Select the right type of analysis based on the customer use case.

    4.3 Select the appropriate data visualization solution for a given scenario.

    • Evaluate output capabilities for a given analysis solution.
    • Choose the appropriate method for data delivery.
    • Choose and define the appropriate data refresh schedule.
    • Choose appropriate tools for different data freshness requirements.
    • Understand the capabilities of visualization tools for interactive use cases.
    • Implement the appropriate data access mechanism.
    • Implement an integrated solution from multiple heterogeneous data sources.

    Domain 5: Security

    5.1 Select appropriate authentication and authorization mechanisms.

    • Implement appropriate authentication methods.
    • Implement appropriate authorization methods.
    • Implement appropriate access control mechanisms.

    5.2 Apply data protection and encryption techniques.

    • Determine data encryption and masking needs.
    • Apply different encryption approaches.
    • Implement at-rest and in-transit encryption mechanisms.
    • Implement data obfuscation and masking techniques.
    • Apply basic principles of key rotation and secrets management.

    5.3 Apply data governance and compliance controls.

    • Determine data governance and compliance requirements.
    • Understand and configure access and audit logging across data analytics services.
    • Implement appropriate controls to meet compliance requirements.

    Resources for Exam Preparation

    AWS Exam Guide

    The Exam Guide for the Exploring AWS learning route is specifically tailored for individuals working in data analytics roles. It is a valuable resource for those aiming to secure an AWS Certified Data Analytics Specialty position. This guide serves not only as a comprehensive learning tool for experienced professionals but also for beginners looking to build expertise in designing, creating, securing, and managing analytics solutions.

    SPOTO Online Tutorials

    The spoto Online Tutorial for AWS Certified Data Analytics Specialty (DAS-C01) offers an in-depth understanding of the exam domains, specifics, and policies. These tutorials, crafted by experts in the field, enhance overall preparation and knowledge acquisition.

    Online Course: AWS Certified Data Analytics

    The Online Course for AWS Certified Data Analytics provides an interactive learning experience, guided by industry experts. This course is designed to equip candidates with a strong foundation in exam topics and ideas, ensuring effective preparation and confidence for the certification exam.

    Practice Tests

    Practice exams play a crucial role in identifying areas of weakness and improvement. With a variety of practice exams available online, candidates can choose from a range of options. Testprep Training, in particular, offers highly beneficial practice exams to aid in comprehensive preparation for the AWS Certified Data Analytics Specialty Exam.

    Is the Exam Worth the Investment?

    AWS Data Analytics holds significant value for data-focused IT professionals. It is especially worthwhile for those actively involved in data models or tasked with implementing AWS services related to big data. This certification serves as a valuable credential for advanced professionals seeking expertise in organizing data analysis solutions and navigating diverse data analytic processes.

    For individuals with experience in data analysis, AWS Data Analytics is a powerful resource for career development and skill demonstration. While challenging, achieving this certification signifies a comprehensive understanding of AWS tools, services, and the intricacies of data analysis.