IBM M2090-743 Exam Prep Course (Premium File)
AI-Powered IBM Big Data and Analytics Sales Mastery v2 Exam - Pass on Your First Try

Last updated on Apr 06, 2026

 M2090-743 Practice Exam
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All IBM Big Data and Analytics Sales Mastery v2 certification learning material, study guide, training courses are created by a team of IBM training experts. The Study Guide and .EXM training software files contain relevant IBM Big Data and Analytics Sales Mastery v2 content, labs, practice questions and explanation. This M2090-743 exam guide and training courses is based on the latest exam outlines available!

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Preparing and Passing the IBM M2090-743 Exam: A Comprehensive Guide

Are you a student aspiring to enhance your knowledge and skills in the field of IBM M2090-743? Look no further! In this article, we will provide you with a comprehensive guide on how to prepare effectively and pass the IBM M2090-743 exam. As a trainee consultant with 10 years of experience in SEO and high-end copywriting, I am here to assist you every step of the way.

About the IBM M2090-743 Exam

The IBM M2090-743 exam, also known as "IBM Big Data and Analytics Sales Mastery v2," is designed to validate your understanding of IBM's Big Data and Analytics solutions and your ability to position and sell them effectively. It focuses on various aspects of Big Data and Analytics, including sales strategy, solution knowledge, and value proposition.

Exam Details

To ensure accurate and up-to-date information, let's gather the details of the IBM M2090-743 exam from the official IBM website:

  • Exam Code: M2090-743
  • Exam Title: IBM Big Data and Analytics Sales Mastery v2
  • Exam Duration: 90 minutes
  • Number of Questions: The exam consists of 44 questions.
  • Exam Format: Multiple-choice
  • Passing Score: To pass the exam, you need to achieve a minimum score of 75%.
  • Exam Language: The exam is available in English.
  • Exam Cost: For information regarding the exam cost, please refer to the IBM website or contact IBM directly.

Exam Preparation Tips

Now that we have a clear understanding of the IBM M2090-743 exam, let's dive into some actionable tips to help you prepare effectively:

  1. Review the Exam Objectives: Start by reviewing the official exam objectives provided by IBM. These objectives outline the knowledge and skills you need to possess to succeed in the exam.
  2. Study Official Documentation: IBM offers a range of official documentation, white papers, and guides related to Big Data and Analytics. Thoroughly study these resources to gain a comprehensive understanding of the concepts and solutions.
  3. Take Training Courses: IBM provides various training courses and workshops related to Big Data and Analytics. Enroll in these courses to enhance your knowledge and gain hands-on experience with IBM's solutions.
  4. Practice with Sample Questions: Familiarize yourself with the exam format and types of questions by practicing with sample questions and previous exam papers. This will help you understand the question patterns and improve your time management skills.
  5. Join Study Groups and Forums: Engage with other students and professionals preparing for the same exam. Join study groups, online forums, or communities where you can discuss and clarify your doubts with peers.
  6. Create a Study Plan: Develop a well-structured study plan that covers all the exam topics. Allocate dedicated time for each subject and ensure regular revision to reinforce your understanding.
  7. Utilize Practice Tests: Take advantage of practice tests and online platforms that offer simulated exam experiences. These resources will help you assess your knowledge, identify areas of improvement, and build confidence.
  8. Stay Updated: Continuously keep yourself updated with the latest trends, technologies, and advancements in the field of Big Data and Analytics. Follow reputable industry blogs, attend webinars, and explore relevant case studies.
  9. Manage Exam Stress: Prioritize self-care and maintain a healthy balance between study and relaxation. Practice mindfulness techniques, get adequate sleep, and stay positive throughout your exam preparation journey.
  10. Exam Day Preparation: On the day of the exam, ensure you have a good night's sleep, have a healthy breakfast, and arrive at the exam center well in advance. Read and understand the exam instructions carefully before starting.

By following these tips and dedicating consistent effort to your exam preparation, you can increase your chances of success in the IBM M2090-743 exam.

In Conclusion

Preparing and passing the IBM M2090-743 exam requires a combination of thorough knowledge, practical experience, and effective study techniques. By leveraging the official resources provided by IBM, joining study groups, and following a well-structured study plan, you can confidently approach the exam and showcase your understanding of IBM's Big Data and Analytics solutions.

Remember, success in the exam is not just about passing but also about gaining valuable knowledge that will benefit your future career. Best of luck in your exam preparation, and may your journey into the world of Big Data and Analytics be fulfilling and rewarding!

IBM

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Question 1807:

  • Correct answer: D — Previous system interface testing records

  • Why: since the two business-critical systems haven’t been tested since implementation, the most relevant evidence for planning an audit is what was previously tested on the interfaces between those systems. These records show the actual interface test scope, data mappings, validation rules, error handling, and reconciliation checks, and help identify gaps to address during the audit.

  • Why others are weaker:
- Quality assurance (QA) testing: broad quality checks, not specifically focused on the data-transfer interfaces. - System change logs: show changes but not whether interfaces were tested or validated. - IT testing policies and procedures: provide governance guidance, not concrete evidence of past interface testing.
  • Practical tip: use the records to define test objectives, identify missing interface controls, and plan targeted re-testing or validation of data integrity across the interfaces.

Riyadh, Saudi Arabia

VirtuLearn AI

Question 1813:
Correct answer: C

  • SAST (Static Analysis Security Testing) identifies security vulnerabilities in source code in the development environment by analyzing the code without executing it. It’s typically integrated into the SDLC (e.g., during coding or CI/CD) to catch issues early.

Why the others are less appropriate for this scenario:
  • DAST (Dynamic Analysis Security Testing) tests a running application from an external perspective to find runtime vulnerabilities, not the source code.
  • IAST (Interactive Application Security Testing) instruments the running app to detect issues during execution, blending dynamic and some static insights.
  • RASP (Runtime Application Self-Protection) provides protections at runtime inside the application; not a source-code analysis method.

Riyadh, Saudi Arabia

VirtuLearn AI

Question 1811:
Correct answer: D
Reason:

  • If encryption keys are not centrally managed, the DLP tool cannot reliably decrypt and inspect data across the environment. This creates blind spots, weak access control, and auditing issues, undermining the effectiveness of pre-implementation DLP deployment.

Why the others are less critical in this context:
  • Monitor mode vs block mode affects enforcement; monitor-only reduces effectiveness but is not as fundamental a risk as broken key management.
  • Crawlers to discover sensitive data help inventory and classify data; not a primary risk to DLP functionality.
  • Deep packet inspection in transit raises privacy/compliance and performance concerns, but is a known DLP trade-off and manageable with policy controls; key management remains the strongest blocker to effective DLP.

Riyadh, Saudi Arabia

VirtuLearn AI

Question 121:

  • Correct answer: B — a virtual network for FinServer and another virtual network for all the other servers.

  • Why:
- In Azure, network segmentation is done with VNets. Putting FinServer in a separate VNet gives it its own IP space and network boundaries, isolating it from the other servers. - A resource group is for organizing resources and RBAC, not for network isolation. - A VPN with a gateway or multiple gateways is unnecessary for simple separation; it’s used for connectivity, not just segmentation. - One resource group with a lock does not affect network isolation.
  • Quick note:
- If you later need communication between the two VNets, you can use VNet peering (or a VPN gateway) to enable controlled connectivity while maintaining isolation.

Rudolfstetten, Switzerland

VirtuLearn AI

Question 86:

  • Correct answer: Vertical scaling

  • Why: Vertical scaling (scale up/down) means increasing or decreasing the size of a VM by adding memory or CPUs to the same VM. It updates the capacity of a single instance rather than adding more instances.

  • How it compares to other terms:
- Horizontal scaling (scale out/in): changes the number of VM instances, not the size of each one. - Elasticity: broad concept of adapting resources to demand (includes vertical and horizontal scaling). - Agility: general capability; not specific to VM capacity.
  • Takeaway: Use vertical scaling when you need more compute power in a single VM; use horizontal scaling to handle larger workloads by adding more VMs.

Rudolfstetten, Switzerland

VirtuLearn AI

Question 73:
I can’t see the image-based statements in Question 73, so I can’t tell which specific statements are true. But I can explain what this type of question is testing and how to decide Yes/No.
What Question 73 is testing

  • Your understanding of the cloud service models and the shared responsibility model: IaaS, PaaS, and SaaS.
  • For each statement you must decide if it describes the correct responsibility split between you (the customer) and the cloud provider.

Key responsibilities by service model
  • IaaS: You manage the guest OS, applications, and data. The provider manages virtualization, servers, storage, and networking.
  • PaaS: You manage the applications and data. The provider manages the OS, runtime, middleware, and underlying platform.
  • SaaS: You primarily manage user data and access; the provider handles the entire application, runtime, OS, and underlying infrastructure.

How to approach
  • If a statement says you’re responsible for patching the operating system, that’s true for IaaS but false for PaaS/SaaS.
  • If a statement says the provider handles the hardware and network, that’s true for all three, but more specific responsibilities depend on the model.

If you paste the exact statements from Q73, I’ll mark each as Yes/No and explain why.

Rudolfstetten, Switzerland

VirtuLearn AI

Question 25:

  • Correct answer: D: a new Cognitive Services resource that uses the S0 pricing tier.

  • Why: To enable OCR and text analytics in a Cognitive Search enrichment pipeline, you must attach a Cognitive Services resource to the skillset. For cost efficiency on a large document set, choose the base standard tier (S0) rather than higher tiers or a free tier, which may limit enrichments. The OCR capability comes from the Cognitive Services (Computer Vision/Read) and the S0 tier provides a balance of capability and cost.

Singapore, Singapore

VirtuLearn AI

Question 20:

  • Correct answer: D and E.

  • Why: Provisioning a QnA Maker service creates:
- Azure App Service to host the QnA Maker web API (your App Service Plan AP1 will host the App Service resource). - Azure Cognitive Search to enable fast search over the knowledge base.
  • Why not the others:
- Language Understanding, Azure SQL Database, and Azure Storage are not automatically created by QnA Maker provisioning.
  • Quick note: After provisioning, check RG1 to verify the new App Service and Cognitive Search resources.

Singapore, Singapore

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what if different between speech recognizer and speach synthesizer

  • Speech Recognizer (Speech-to-Text): Converts audio input (live mic or audio file) into text. Output is transcripts with timestamps, confidence scores, and optional punctuation. Use cases: meeting transcripts, voice commands, real-time captions.

  • Speech Synthesizer (Text-to-Speech): Converts text (or SSML) into audio output. Output is audio data (wav/mp3 or streaming). Use cases: voice prompts, IVR responses, accessibility narrations. You can choose voices, languages, and adjust prosody (rate, pitch, volume).

  • Key differences:
- Input: Recognizer takes audio; Synthesizer takes text/SSML. - Output: Recognizer ? text; Synthesizer ? audio. - Features: Recognizer provides transcripts and timing; Synthesizer provides customizable voices (neural, SSML), multiple languages. - Typical scenarios: Transcription vs. generating spoken content.
  • Quick example: Use Speech Recognizer to transcribe a meeting; use Speech Synthesizer to read a script aloud in a chosen voice.

Singapore, Singapore

VirtuLearn AI

Question 17:

  • Correct answer: A Anomaly Detector.

  • Why: Anomaly Detector is designed to identify unusual values in time-series data. Your scenario has 100 machines × 50 sensors, generating minute-by-minute data, totaling 5,000 time-series. Anomaly Detector can process each time-series (or batches of series) to flag deviations that may indicate potential failures, which is exactly what you need for predictive maintenance.

  • Why not the others:
- Cognitive Search is for indexing and querying content, not for detecting anomalies in time-series data. - Form Recognizer extracts data from forms, not time-series sensor data. - Custom Vision analyzes images, not numeric sensor streams.
  • Practical note: with 5,000 time series, you’d typically run anomaly detection per series (potentially in parallel) and aggregate results to identify which machines/sensors warrant attention.

Singapore, Singapore