Monday, July 7, 2025

C_THR82_2505 SAP Certified Associate - SAP SuccessFactors Performance and Goals Exam

 

C_THR82_2505 SAP Certified Associate - SAP SuccessFactors Performance and Goals Exam

THEORETICAL EXAM
C_THR82_2505
80 questions (3 hrs)
65% cut score
Available in EN

Overview
The "SAP Certified Associate - Implementation Consultant - SAP SuccessFactors Performance and Goals" certification exam verifies that the candidate possesses fundamental knowledge and skills in the area of SAP SuccessFactors Performance and Goals. This certificate proves that the candidate can apply the knowledge and skills in projects under the guidance of an experienced consultant. It is recommended as an entry-level qualification to allow consultants to get acquainted with the fundamentals of SAP SuccessFactors Performance and Goals.

This certification is intended for SAP partner consultants implementing the solution. Only registered SAP partner consultants will be provided with provisioning rights once they have been certified. Customers and independent consultants, even if certified, will not be provided with provisioning rights. There are no exceptions to this policy.

NOTE: The next version of this exam should be available between December 8-12, 2025.

Topics covered in this exam
To help you get ready, we recommend following these steps:

Study the relevant material
Schedule and take the theoretical exam to earn your certification

Below is a list of topics that may be covered in the theoretical exam. Please note: this is a guide, not a guarantee—SAP may update exam content at any time.

Goal Management
Exam percentage: 11-20%

Job Architecture and Attributes
Exam percentage: <=10

Form Templates
Exam percentage: 11-20%

Configuration of Performance Management
Exam percentage: <=10

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Sample Question and Answers

QUESTION 1
What can an administrator do when accessing the Delete Continuous Feedback page? Note: There are 2 correct answers to this question.

A. The administrator can delete only feedback given or received by active users.
B. The administrator CANNOT restore feedback once the feedback is deleted.
C. The administrator can only delete feedback given in the last three months.
D. The administrator can access all information, including feedback content from others.

Answer: A B

QUESTION 2
Which actions can you enable and disable in Continuous Performance Management Configuration
(CPM)? Note: There are 3 correct answers to this question.

A. Provide discussion topics
B. Access the Delete Continuous Feedback page
C. Support multiple roles
D. Use Al-assisted writing
E. Prevent feedback deletion by users

Answer: A C E

QUESTION 3
What can you do in the Feedback Received tab in Continuous Feedback? Note: There are 2 correct answers to this question.

A. Filter to only show feedback with a linked achievement.
B. Access the profile card to drill down into employee details.
C. Filter to only show feedback with a linked activity.
D. Decline a feedback request.

Answer: C D

QUESTION 4
A manager is giving feedback to an employee using Generative Al.
Which of the following outputs can be retrieved by the Al-Assisted Writing in this scenario? Note:
There are 2 correct answers to this question.

A. The manager can use Al to change the tone of the writing and make it personable.
B. The manager can use Al to link the feedback given to a specific activity.
C. The manager can use Al to make the feedback actionable.
D. The manager can use Al to add an attachment to the feedback that was given.

Answer: A C

QUESTION 5
Which of the following are valid end user actions in Continuous Performance Management (CPM)?
Note: There are 3 correct answers to this question.

A. Create a new development goal from your activities view.
B. Add attachments to one of your activities.
C. Provide coaching advice to your direct report in the 1:1 meeting.
D. Add your own meeting notes to assist with the 1:1 meeting.
E. Send a channel invitation to your colleague to have regular 1:1 meetings.

Answer: A B D

Wednesday, June 4, 2025

Google Generative AI Leader Exam

 

Generative AI Leader
A Generative AI Leader is a visionary professional with comprehensive knowledge of how generative AI (gen AI) can transform businesses. They have business-level knowledge of Google Cloud's gen AI offerings and understand how Google's AI-first approach can lead organizations toward innovative and responsible AI adoption. They influence gen AI-powered initiatives and identify opportunities across business functions and industries, using Google Cloud's enterprise-ready offerings to accelerate innovation.

This certification is for anyone in any job role, with or without hands-on technical experience.

The Generative AI Leader exam assesses your knowledge in these areas:
Fundamentals of gen AI
Google Cloud's gen AI offerings
Techniques to improve gen AI model output
Business strategies for a successful gen AI solution

About this certification exam
Length: 90 minutes
Content: Exam guide
Registration fee: $99 (plus tax where applicable)
Language: English
Exam format: 50-60 multiple choice questions
Exam delivery method: Online-proctored or onsite-proctored
Validity period: 3 years
Prerequisites: None

Certification renewal: You can renew your certification by taking the renewal exam or the standard exam starting 60 days before your certification expires.

Preparing for your exam

Step 1. Understand what's on the exam
The exam guide contains a list of topics that may be assessed on both the standard exam and the renewal exam. Review the exam guide to determine if your knowledge aligns with the topics on the exam.

Step 2. Expand your knowledge with training
Follow the Generative AI Leader learning path
Review the Generative AI Leader Study Guide

Step 3. Prepare with sample questions
The Generative AI Leader sample questions will familiarize you with the format of exam questions and example content that may be covered on both of the exams.

The sample questions do not represent the range of topics or level of difficulty of questions presented on the exam. Performance on the sample questions should not be used to predict your Generative AI Leader exam result.

There is no limit to the number of times you can complete the sample questions.

The sample questions are not timed.
If you close the sample questions while in progress, your work won't be saved and you will have to start from the beginning.
The sample questions are currently available in English only.
Launch sample questions

Step 4. Schedule an exam
Decide whether to take the exam remotely (see online testing requirements) or at a test center (locate a test center near you).
Register to take the standard exam or renewal exam today.
Review exam terms and conditions and data sharing policies.

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Sample Question And Answers

QUESTION 1
A marketing team wants to use a foundation model to create social media and advertising campaigns.
They want to create written articles and images from text.
They lack deep AI expertiseand need a versatile solution.
Which Google foundation model should they use?

A. Gemma
B. Imagen
C. Gemini
D. Veo

Answer: C

QUESTION 2
A financial institution uses generative AI (gen AI) to approve and reject loan applications, but gives no reasons for rejection.
Customers are starting to file complaints. The company needs to implement a solution to reduce the complaints.
What should the company do?

A. Collect a larger and more diverse dataset for the gen AI model.
B. Implement explainable gen AI policies.
C. Fine-tune the gen AI model.
D. Develop fairness assessments for the gen AI model.

Answer: B

QUESTION 3
A software development team wants to use generative AI (gen AI) to code faster so they can launch their software prototype quicker. What should the team do?

A. Use gen AI to refactor and optimize existing code.
B. Use gen AI to suggest code snippets and complete functions.
C. Use gen AI to automatically generate comprehensive documentation for their code.
D. Use gen AI to identify potential bugs and security vulnerabilities in their code.

Answer: B

QUESTION 4
What is the definition of generative AI?

A. A type of artificial intelligence that enables a system to autonomously learn and improve using neural networks and deep learning.4
B. A type of artificial intelligence that can create new content and ideas, including text, images, music, and code.
C. A type of machine learning algorithm inspired by the human brain that is made up of interconnected nodes.
D. A type of predictive model that estimates a relationship by fitting a line to the observed data.

Answer: B

QUESTION 5
A company wants a generative AI platform that provides the infrastructure, tools, and pre-trained models needed to build,
deploy, and manage its generative AI solutions. Which Google Cloud offering should the company use?

A. BigQuery
B. Vertex AI
C. Google Kubernetes Engine (GKE)
D. Google Cloud Storage

Answer: B

Tuesday, January 21, 2025

C_THR70_2411 SAP Certified Associate - SAP SuccessFactors Incentive Management Exam

 

EXAM C_THR70_2411
80 questions (3 hrs)
75% cut score
Available in English

Validate your SAP skills and expertise
The "SAP Certified Associate - SAP SuccessFactors Incentive Management" certification exam confirms your mastery of the fundamental skills in SAP SuccessFactors Incentive Management and demonstrates your ability to effectively implement compensation plans. These skills are valuable for carrying out a range of implementation and customization tasks within a project team, and allow you to play a constructive role in the success of a project.

Topic areas
Please see below the list of topics that may be covered within this SAP Certification and the courses that touches on these topics. Its accuracy does not constitute a legitimate claim. SAP reserves the right to update the exam content (topics, items, weighting) at any time.

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Key Concepts
Exam percentage: ≤10%
Related course code: THR70_2411

Administration and Security
Exam percentage: 11% - 20%
Related course code: THR70_2411

Organization Data
Exam percentage: 11% - 20%
Related course code: THR70_2411

Classification and Compensation Elements
Exam percentage: 11% - 20%
Related course code: THR70_2411

Compensation Plans and Rules
Exam percentage: 11% - 20%
Related course code: THR70_2411

Pipeline and Calculation
Exam percentage: 11% - 20%
Related course code: THR70_2411

Dashboard, Plan Communicator and Disputes
Exam percentage: ≤10%
Related course code: THR70_2411

Embedded Analytics

Exam percentage: ≤10%
Related course code: THR70_2411

The C_THR70_2411 exam, titled SAP Certified Associate - SAP SuccessFactors Incentive Management, is designed to validate your expertise in implementing and managing SAP SuccessFactors Incentive Management solutions. This certification is ideal for application consultants, implementation consultants, and system administrators aiming to demonstrate their proficiency in this domain.

Exam Details:
* Exam Code: C_THR70_2411
* Number of Questions: 80
* Duration: 180 minutes
* Question Format: Multiple-choice and multiple-response questions
* Passing Score: 75%
* Language: English
* Exam Fee: Approximately $200 USD
* Prerequisites: None

Key Topics Covered:
1. Classification and Compensation Elements (11% - 20%)
2. Administration and Security (11% - 20%)
3. Organization Data (11% - 20%)
4. Compensation Plans and Rules (11% - 20%)
5. Pipeline and Calculation (11% - 20%)
6. Dashboard, Plan Communicator, and Disputes (≤10%)
7. Key Concepts (≤10%)
8. Embedded Analytics (≤10%)

Preparation Tips:
* Study Materials: Utilize official SAP training resources and guides to thoroughly understand each topic area.
* Practice Exams: Engage in practice tests to familiarize yourself with the exam format and identify areas needing further study.
* Hands-on Experience: Gaining practical experience with SAP SuccessFactors Incentive Management will enhance your understanding and application of concepts.

Registration Process:
1. SAP Training Account: Create an account on the SAP Training website.
2. Subscription Purchase: Choose and purchase the appropriate certification subscription, such as CER001 for a single exam attempt or CER006 for multiple attempts.
3. Exam Scheduling: Access the SAP Certification Hub to schedule your exam at a convenient time.

Retake Policy:
If you do not pass the exam on your first attempt, you are allowed up to two additional retakes. After three unsuccessful attempts, a waiting period is required before reapplying.

Maintaining Certification:
Stay informed about any updates or changes to the certification by regularly checking SAP's official communications. Engaging in continuous learning and professional development will help maintain the validity and relevance of your certification.

Achieving the C_THR70_2411 certification demonstrates your capability to effectively implement and manage SAP SuccessFactors Incentive Management solutions, thereby enhancing your professional credibility and career prospects.


Sample Question and Answers

QUESTION 1
Which of the following are characteristics of Credit Types? Note: There are 2 correct answers to this question.

A. They are used to identify credits by product or sale type.
B. They are a required field on the credit output.
C. They are used in credits to define Territories.
D. They are an optional field within the system.

Answer: A, B

QUESTION 2
You want to design a plan that credits a transaction to a position based on specific criteria such as
postal codes, customer or product criteria. Which of the following would you use in a credit rule?

A. Generic attributes
B. Territories
C. Formulas
D. Classification rules

Answer: D

QUESTION 3
If a Processing Unit is enabled, which of the following applies? Note: There are 2 correct answers to this question.

A. You can finalize a group of positions in a single pipeline run.
B. You must run Compensate and Pay before Classify.
C. You can post an entire period for multiple Processing Units in a single pipeline run.
D. You CANNOT finalize an entire period for multiple Processing Units in a single pipeline run.

Answer: A, C

QUESTION 4
How are released periods used in dashboard configuration? Note: There are 3 correct answers to this question.

A. The administrator can release periods based on calendars.
B. Payees can view dashboards for released periods only.
C. Both administrators and payees can release periods.
D. Payees can view results prior to pipeline completion.
E. The administrator can release periods based on Processing Units.

Answer: A, B, E

QUESTION 5
Which of the following are best practices when working with Variables? Note: There are 2 correct answers to this question.

A. Always use a Variable in a rule instead of a compensation element.
B. Avoid using Variables because they increase processing time.
C. Always leave the Variable default assignment field empty.
D. Set a default assignment for all Variables.

Answer: A, D

Tuesday, December 10, 2024

C_S4CPB_2408 SAP Certified Associate - Implementation Consultant - SAP S/4HANA Cloud Public Edition Exam

 

EXAM C_S4CPB_2408
60 questions (120m)
67% cut score
Available in English, Chinese, Japanese

Validate your SAP skills and expertise
This certification verifies that you possess the core skills required to explain and execute core implementation project tasks to deploy, adopt, and extend SAP S/4HANA Cloud Public Edition. This certification is designed for implementation project members to prove their overall understanding and in-depth skills to participate in their role as members of a SAP S/4HANA Cloud Public Edition implementation project.

Please note: To prepare for this certification, it is necessary to take the Learning Journey Exploring SAP Cloud ERP in addition to the Learning Journey displayed under "How to Prepare."
Stay certified and stay ahead

Continuous learning and keeping your skills up to date is a priority and SAP Certification makes it easy for you to maintain your SAP skills and valid credentials.

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The standard validity of your certification is 12 months.
Every time you successfully complete an assessment, the validity period is extended by 12 more months.
You’ll receive personalized communication to ensure that you don’t miss your certification expiry date.

Topic areas
Please see below the list of topics that may be covered within this SAP Certification and the courses that touches on these topics. Its accuracy does not constitute a legitimate claim. SAP reserves the right to update the exam content (topics, items, weighting) at any time.

Data Migration and Business Process Testing
Exam percentage: 11% - 20%
Related course code: S4C01_30

Introduction to Cloud Computing and SAP Cloud ERP Deployment Options
Exam percentage: 11% - 20%
Related course code: S4C01_30, S4CP01_30

Implementing with a Cloud Mindset, Building the Team, and Conducting Fit-to-Standard Workshops
Exam percentage: 11% - 20%
Related course code: S4C01_30, S4CP01_30

System Landscapes and Identity Access Management

Exam percentage: 11% - 20%
Related course code: S4C01_30

Configuration and the SAP Fiori Launchpad
Exam percentage: 11% - 20%
Related course code: S4C01_30

Extensibility and Integration
Exam percentage: 11% - 20%
Related course code: S4C01_30


Sample Question and Answers

QUESTION 1
Which of the following activities are completed in the Realize phase of the SAP Activate
Methodology? Note: There are 2 correct answers to this question.

A. Demonstrate where to find business process documentation
B. Gather perceived change impact feedback
C. Set up manual test cases in SAP Cloud ALM
D. Enter configuration values in SAP Central Business Configuration

Answer: C D

QUESTION 2
When using the Local SAP SHANA Database Schema migration approach, what is the maximum file size? Note: There are 2 correct answers to this question.

A. 160 MB per ZIP file
B. 160 MB per file
C. 100 MB per ZIP file
D. 100 MB per file

Answer: C D

QUESTION 3
Which technologies should you use to integrate SAP SHANA Cloud Public Edition with another SAP
public cloud solution? Note: There are 2 correct answers to this question

A. SAP Integration Suite
B. Predelivered APIs
C. SAP Process Orchestration
D. SAP Cloud Connector

Answer: A B

QUESTION 4
In which SAP Activate methodology phase do consultants configure business processes based on the
information gathered in the Fit-to-Standard workshops?

A. Realize
B. Explore
C. Deploy
D. Prepare

Answer: A

QUESTION 5
After integration requirements have been finalized, what is used to analyze, design, and document the integration strategy?

A. SAP Business Accelerator Hub
B. SAP Cloud ALM Requirements app
C. Integration Solution Advisory Methodology
D. Integration and API List

Answer: C

QUESTION 6
How can you define the relationship between business roles and business catalogs?

A. A business role is a collection of one or more business catalogs.
B. A business catalog is a collection of one or more business roles.
C. A business catalog restricts access to one or more business roles.
D. A business role restricts access to one or more business catalogs.

Answer: A

Wednesday, September 4, 2024

C_S4CS_2408 SAP Certified Associate - Implementation Consultant - SAP S/4HANA Cloud Public Edition, Sales

 

Delivery Methods: SAP Certification
Level: Associate
Exam: 80 questions
Sample Questions: View more
Cut Score: 71%
Duration: 180 mins
Languages: English

Description
This certification verifies that you possess the fundamental knowledge required to explain and execute core implementation project tasks to deploy, adopt and extend SAP S/4HANA Cloud Public Edition as well as core knowledge in Sales. It proves that the candidate has an overall understanding and technical skills to participate as a member of a SAP S/4HANA Cloud Public Edition implementation project team with a focus on Sales in a mentored role.

Topic Areas
Please see below the list of topics that may be covered within this certification and the courses that cover them. Its accuracy does not constitute a legitimate claim; SAP reserves the right to update the exam content (topics, items, weighting) at any time.

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Topic Areas
Please see below the list of topics that may be covered within this certification and the courses that cover them. Its accuracy does not constitute a legitimate claim; SAP reserves the right to update the exam content (topics, items, weighting) at any time.

Solution Processes Implementation for Core Sales 31% - 40%
Describe the execution of core Sales Solution Processes and the use and configuration of related business functions in SAP S/4HANA Cloud Public Edition
S4C60e (SAP Learning Hub only)
S4C61e (SAP Learning Hub only)
S4C62e (SAP Learning Hub only)
S4C63e (SAP Learning Hub only)
----- OR -----
S4C60 Learning Journey
S4C61 Learning Journey
S4C62 Learning Journey
S4C63 Learning Journey

Introduction to Cloud Computing and SAP Cloud ERP Deployment Options <= 10%
Describe cloud computing, SAP's enteprise portfolio, and Cloud ERP deployment options and enablement packages.
S4C01e (SAP Learning Hub only)
----- OR -----
Implement S/4HANA Public Cloud

Implementing with a Cloud Mindset, Building the Team, and Conducting Fit-to-Standard Workshops <= 10%
Implement with a Cloud Mindset, build the implementation team, and conduct Fit-to-Standard Workshops.
S4C01e (SAP Learning Hub only)
----- OR -----
Implement S/4HANA Public Cloud

Configuration and the SAP Fiori Launchpad <= 10%
Configure business processes with SAP Central Business Configuration and work with the SAP Fiori Launchpad capabilities.
S4C01e (SAP Learning Hub only)
----- OR -----
Implement S/4HANA Public Cloud

Extensibility and Integration <= 10%
Customizing applications and processes with extensibility tools and setting up integrations.
S4C01e (SAP Learning Hub only)
----- OR -----
Implement S/4HANA Public Cloud

Data Migration and Business Process Testing <= 10%
Migrate data from legacy systems and test configured business processes with manual and automated tests.
S4C01e (SAP Learning Hub only)
----- OR -----
Implement S/4HANA Public Cloud

System Landscapes and Identity Access Management <= 10%
Perform implementation and configuration tasks for sourcing and procurement soultion processes.
S4C01e (SAP Learning Hub only)
----- OR -----
Implement S/4HANA Public Cloud

Organizational Units and System Data for Sales <= 10%
Explain Organizational Units and the basic best practices for system data used in SAP S/4HANA Cloud Public Edition, specifically addressing Sales.
S46000 (SAP S/4HANA 2023)
----- OR -----
Applying SAP S/4HANA Sales

Solution Processes Implementation for Analytics <= 10%
Describe the use of main analytical solution processes in SAP S/4HANA Cloud Public Edition.
S4C01e (SAP Learning Hub only)
----- OR -----
Implementing Sales Automation

General Information

Exam Preparation

All SAP consultant certifications are available as Cloud Certifications in the Certification Hub and can be booked with product code CER006. With CER006 – SAP Certification in the Cloud, you can take up to six exams attempts of your choice in one year – from wherever and whenever it suits you! Test dates can be chosen and booked individually.

Each specific certification comes with its own set of preparation tactics. We define them as "Topic Areas" and they can be found on each exam description. You can find the number of questions, the duration of the exam, what areas you will be tested on, and recommended course work and content you can reference.

Certification exams might contain unscored items that are being tested for upcoming releases of the exam. These unscored items are randomly distributed across the certification topics and are not counted towards the final score. The total number of items of an examination as advertised in the Training Shop is never exceeded when unscored items are used.

Please be aware that the professional- level certification also requires several years of practical on-the-job experience and addresses real-life scenarios.

For more information refer to our SAP Certification FAQs.

Safeguarding the Value of Certification

SAP Education has worked hard together with the Certification & Enablement Influence Council to enhance the value of certification and improve the exams. An increasing number of customers and partners are now looking towards certification as a reliable benchmark to safeguard their investments. Unfortunately, the increased demand for certification has brought with it a growing number of people who to try and attain SAP certification through unfair means. This ongoing issue has prompted SAP Education to place a new focus on test security. Our Certification Test Security Guidelines will help you as test taker to understand the testing experience.

Security Guidelines
 


Sample Question and Answers
 

QUESTION 1
You work on a Sell from Stock (BD9) process in SAP SHANA Cloud Public Edition.
What must be created to confirm a customer's intention to buy the products?

A. Sales quotation
B. Outbound delivery
C. Sales inquiry
D. Sales order

Answer: D

QUESTION 2
You need to manage a customer down payment. Which action do you perform during sales order entry?

A. Enter an appropriate item in the billing plan of the sales order.
B. Create a sales order with a dedicated order type.
C. Enter a specific condition in the pricing procedure of the sales order.
D. Mark the down payment checkbox at item level.

Answer: A

QUESTION 3
You are working on a Sales Order Processing with Collective Billing (BKZ) process in SAP SHANA Cloud Public Edition.
Which of the following split criteria always prevent the combination of multiple sales orders into a single outbound delivery? Note: There are 2 correct answers to this question.

A. Plant
B. Shipping point
C. Payment term
D. Ship-to party

Answer: B D

QUESTION 4
Which of the following documents can be used as a reference to create debit memo requests?
Note: There are 2 correct answers to this question.

A. Billing document
B. Delivery document
C. Quantity contract
D. Sales order

Answer: A D

QUESTION 5
Which information must you enter manually in the invoice correction process?

A. Billing block
B. Order reason
C. Return reason
D. Billing plan

Answer: B

Tuesday, August 27, 2024

1Z0-1122-24 Oracle Cloud Infrastructure 2024 AI Foundations Associate Exam

 

Earn associated certifications
Passing this exam is required to earn these certifications. Select each certification title below to view full requirements.
Oracle Cloud Infrastructure 2024 Certified AI Foundations Associate

Format: Multiple Choice
Duration: 60 Minutes
Exam Price: Free
Number of Questions: 40
Passing Score: 65%
Validation: This Exam has been validated against Oracle Cloud Infrastructure 2024

Policy: Cloud Recertification

Prepare to pass exam: 1Z0-1122-24
The Oracle Cloud Infrastructure (OCI) AI Foundations certification is designed to introduce learners to the fundamental concepts of artificial intelligence (AI) and machine learning (ML), with a specific focus on the practical application of these technologies within the Oracle Cloud Infrastructure. This course is ideal for beginners and provides an accessible entry point for those looking to enhance their understanding of AI and ML without the requirement of prior extensive technical experience.

By participating in this course, you will gain a comprehensive overview of the AI landscape, including an understanding of basic AI and ML concepts, deep learning fundamentals, and the role of generative AI and large language models in modern computing. The course is structured to ensure a step-by-step learning process, guiding you from the basic principles to more complex topics in AI, making learning both effective and engaging.

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Take recommended training
Complete one of the courses below to prepare for your exam (optional):

Become An OCI AI Foundations Associate (2024)

Additional Preparation and Information
A combination of Oracle training and hands-on experience (attained via labs and/or field experience), in the learning subscription, provides the best preparation for passing the exam.

Review exam topics
Objectives % of Exam
Intro to AI Foundations 10%
Intro to ML Foundations 15%
Intro to DL Foundations 15%
Intro to Generative AI & LLMs 15%
Get started with OCI AI Portfolio 15%
OCI Generative AI and Oracle 23ai 10%
Intro to OCI AI Services* 20%

Intro to AI Foundations
Discuss AI Basics
Discuss AI Applications & Types of Data
Explain AI vs ML vs DL

Intro to ML Foundations
Explain Machine Learning Basics
Discuss Supervised Learning Fundamentals (Regression & Classification)
Discuss Unsupervised Learning Fundamentals
Discuss Reinforcement Learning Fundamentals

Intro to DL Foundations
Discuss Deep Learning Fundamentals
Explain Convolutional Models (CNN)
Explain Sequence Models (RNN & LSTM)

Intro to Generative AI & LLMs
Discuss Generative AI Overview
Discuss Large Language Models Fundamentals
Explain Transformers Fundamentals
Explain Prompt Engineering & Instruction Tuning
Explain LLM Fine Tuning

Get started with OCI AI Portfolio
Discuss OCI AI Services Overview
Discuss OCI ML Services Overview
Discuss OCI AI Infrastructure Overview
Explain Responsible AI

OCI Generative AI and Oracle 23ai
Describe OCI Generative AI Services
Discuss Autonomous Database Select AI
Discuss Oracle Vector Search

Intro to OCI AI Services*
Explore OCI AI Services & related APIs (Language, Vision, Document Understanding, Speech)


Sample Question and Answers

QUESTION 1
What is the key feature of Recurrent Neural Networks (RNNs)?

A. They process data in parallel.
B. They are primarily used for image recognition tasks.
C. They have a feedback loop that allows information to persist across different time steps.
D. They do not have an internal state.

Answer: C

Explanation:
Recurrent Neural Networks (RNNs) are a class of neural networks where connections between nodes
can form cycles. This cycle creates a feedback loop that allows the network to maintain an internal
state or memory, which persists across different time steps. This is the key feature of RNNs that
distinguishes them from other neural networks, such as feedforward neural networks that process
inputs in one direction only and do not have internal states.
RNNs are particularly useful for tasks where context or sequential information is important, such as
in language modeling, time-series prediction, and speech recognition. The ability to retain
information from previous inputs enables RNNs to make more informed predictions based on the
entire sequence of data, not just the current input.
In contrast:
Option A (They process data in parallel) is incorrect because RNNs typically process data sequentially, not in parallel.
Option B (They are primarily used for image recognition tasks) is incorrect because image recognition
is more commonly associated with Convolutional Neural Networks (CNNs), not RNNs.
Option D (They do not have an internal state) is incorrect because having an internal state is a
defining characteristic of RNNs.
This feedback loop is fundamental to the operation of RNNs and allows them to handle sequences of
data effectively by "remembering" past inputs to influence future outputs. This memory capability is
what makes RNNs powerful for applications that involve sequential or time-dependent data .

QUESTION 2

What role do Transformers perform in Large Language Models (LLMs)?

A. Limit the ability of LLMs to handle large datasets by imposing strict memory constraints
B. Manually engineer features in the data before training the model
C. Provide a mechanism to process sequential data in parallel and capture long-range dependencies
D. Image recognition tasks in LLMs

Answer: C

Explanation:
Transformers play a critical role in Large Language Models (LLMs), like GPT-4, by providing an
efficient and effective mechanism to process sequential data in parallel while capturing long-range
dependencies. This capability is essential for understanding and generating coherent and
contextually appropriate text over extended sequences of input.
Sequential Data Processing in Parallel:
Traditional models, like Recurrent Neural Networks (RNNs), process sequences of data one step at a
time, which can be slow and difficult to scale. In contrast, Transformers allow for the parallel
processing of sequences, significantly speeding up the computation and making it feasible to train on large datasets.
This parallelism is achieved through the self-attention mechanism, which enables the model to
consider all parts of the input data simultaneously, rather than sequentially. Each token (word,
punctuation, etc.) in the sequence is compared with every other token, allowing the model to weigh
the importance of each part of the input relative to every other part.
Capturing Long-Range Dependencies:
Transformers excel at capturing long-range dependencies within data, which is crucial for
understanding context in natural language processing tasks. For example, in a long sentence or
paragraph, the meaning of a word can depend on other words that are far apart in the sequence. The
self-attention mechanism in Transformers allows the model to capture these dependencies
effectively by focusing on relevant parts of the text regardless of their position in the sequence.
This ability to capture long-range dependencies enhances the model's understanding of context,
leading to more coherent and accurate text generation.
Applications in LLMs:
In the context of GPT-4 and similar models, the Transformer architecture allows these models to
generate text that is not only contextually appropriate but also maintains coherence across long
passages, which is a significant improvement over earlier models. This is why the Transformer is the
foundational architecture behind the success of GPT models.
Reference:
Transformers are a foundational architecture in LLMs, particularly because they enable parallel
processing and capture long-range dependencies, which are essential for effective language
understanding and generation .

QUESTION 3
Which is NOT a category of pretrained foundational models available in the OCI Generative AI service?

A. Embedding models
B. Translation models
C. Chat models
D. Generation models

Answer: B

Explanation:
The OCI Generative AI service offers various categories of pretrained foundational models, including
Embedding models, Chat models, and Generation models. These models are designed to perform a
wide range of tasks, such as generating text, answering questions, and providing contextual
embeddings. However, Translation models, which are typically used for converting text from one
language to another, are not a category available in the OCI Generative AI service's current offerings.
The focus of the OCI Generative AI service is more aligned with tasks related to text generation, chat
interactions, and embedding generation rather than direct language translation .

QUESTION 4
What does "fine-tuning" refer to in the context of OCI Generative AI service?

A. Encrypting the data for security reasons
B. Adjusting the model parameters to improve accuracy
C. Upgrading the hardware of the AI clusters
D. Doubling the neural network layers

Answer: B

Explanation:
Fine-tuning in the context of the OCI Generative AI service refers to the process of adjusting the
parameters of a pretrained model to better fit a specific task or dataset. This process involves further
training the model on a smaller, task-specific dataset, allowing the model to refine its understanding
and improve its performance on that specific task. Fine-tuning is essential for customizing the
general capabilities of a pretrained model to meet the particular needs of a given application,
resulting in more accurate and relevant outputs. It is distinct from other processes like encrypting
data, upgrading hardware, or simply increasing the complexity of the model architecture .

QUESTION 5

What is the primary benefit of using Oracle Cloud Infrastructure Supercluster for AI workloads?

A. It delivers exceptional performance and scalability for complex AI tasks.
B. It is ideal for tasks such as text-to-speech conversion.
C. It offers seamless integration with social media platforms.
D. It provides a cost-effective solution for simple AI tasks.

Answer: A

Explanation:
Oracle Cloud Infrastructure Supercluster is designed to deliver exceptional performance and
scalability for complex AI tasks. The primary benefit of this infrastructure is its ability to handle
demanding AI workloads, offering high-performance computing (HPC) capabilities that are crucial for
training large-scale AI models and processing massive datasets. The architecture of the Supercluster
ensures low-latency networking, efficient resource allocation, and high-throughput processing,
making it ideal for AI tasks that require significant computational power, such as deep learning, data
analytics, and large-scale simulations .

QUESTION 6

Which AI Ethics principle leads to the Responsible AI requirement of transparency?

A. Explicability
B. Prevention of harm
C. Respect for human autonomy
D. Fairness

Answer: A



Monday, July 1, 2024

Google Professional Machine Learning Engineer Exam update

 

Length: Two hours
Registration fee: $ (plus tax where applicable)
Language: English
Exam format: 50-60 multiple choice and multiple select questions

Exam delivery method:
a. Take the online-proctored exam from a remote location, review the online testing requirements.
b. Take the onsite-proctored exam at a testing center, locate a test center near you

Prerequisites: None
Recommended experience: 3+ years of industry experience including 1 or more years designing and managing solutions using Google Cloud.

Certification Renewal / Recertification: Candidates must recertify in order to maintain their certification status. Unless explicitly stated in the detailed exam descriptions, all Google Cloud certifications are valid for two years from the date of certification. Recertification is accomplished by retaking the exam during the recertification eligibility time period and achieving a passing score. You may attempt recertification starting 60 days prior to your certification expiration date.

Exam overview

Step 1: Get real world experience

Before attempting the Machine Learning Engineer exam, it's recommended that you have 3+ years of hands-on experience with Google Cloud products and solutions. Ready to start building? Explore the Google Cloud Free Tier for free usage (up to monthly limits) of select products.

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Step 2: Understand what's on the exam
The exam guide contains a complete list of topics that may be included on the exam. Review the exam guide to determine if your skills align with the topics on the exam.

See current exam guide

Step 3: Review the sample questions
Familiarize yourself with the format of questions and example content that may be covered on the Machine Learning Engineer exam.

Review sample questions

Step 4: Round out your skills with training

Prepare for the exam by following the Machine Learning Engineer learning path. Explore online training, in-person classes, hands-on labs, and other resources from Google Cloud.

Start preparing

Prepare for the exam with Googlers and certified experts. Get valuable exam tips and tricks, as well as insights from industry experts.

Explore Google Cloud documentation for in-depth discussions on the concepts and critical components of Google Cloud.

Learn about designing, training, building, deploying, and operationalizing secure ML applications on Google Cloud using the Official Google Cloud Certified Professional Machine Learning Engineer Study Guide. This guide uses real-world scenarios to demonstrate how to use the Vertex AI platform and technologies such as TensorFlow, Kubeflow, and AutoML, as well as best practices on when to choose a pretrained or a custom model.

Step 5: Schedule an exam

Register and select the option to take the exam remotely or at a nearby testing center.

Review exam terms and conditions and data sharing policies.
A Professional Machine Learning Engineer builds, evaluates, productionizes, and optimizes ML models by using Google Cloud technologies and knowledge of proven models and techniques. The ML Engineer handles large, complex datasets and creates repeatable, reusable code. The ML Engineer considers responsible AI and fairness throughout the ML model development process, and collaborates closely with other job roles to ensure long-term success of ML-based applications. The ML Engineer has strong programming skills and experience with data platforms and distributed data processing tools. The ML Engineer is proficient in the areas of model architecture, data and ML pipeline creation, and metrics interpretation. The ML Engineer is familiar with foundational concepts of MLOps, application development, infrastructure management, data engineering, and data governance. The ML Engineer makes ML accessible and enables teams across the organization. By training, retraining, deploying, scheduling, monitoring, and improving models, the ML Engineer designs and creates scalable, performant solutions.

* Note: The exam does not directly assess coding skill. If you have a minimum proficiency in Python and Cloud SQL, you should be able to interpret any questions with code snippets.

The Professional Machine Learning Engineer exam assesses your ability to:
Architect low-code ML solutions
Collaborate within and across teams to manage data and models
Scale prototypes into ML models
Serve and scale models
Automate and orchestrate ML pipelines
Monitor ML solutions

Sample Question and Answers

QUESTION 1
As the lead ML Engineer for your company, you are responsible for building ML models to digitize
scanned customer forms. You have developed a TensorFlow model that converts the scanned images
into text and stores them in Cloud Storage. You need to use your ML model on the aggregated data
collected at the end of each day with minimal manual intervention. What should you do?

A. Use the batch prediction functionality of Al Platform
B. Create a serving pipeline in Compute Engine for prediction
C. Use Cloud Functions for prediction each time a new data point is ingested
D. Deploy the model on Al Platform and create a version of it for online inference.

Answer: A

Explanation:
Batch prediction is the process of using an ML model to make predictions on a large set of data
points. Batch prediction is suitable for scenarios where the predictions are not time-sensitive and can
be done in batches, such as digitizing scanned customer forms at the end of each day. Batch
prediction can also handle large volumes of data and scale up or down the resources as needed. AI
Platform provides a batch prediction service that allows users to submit a job with their TensorFlow
model and input data stored in Cloud Storage, and receive the output predictions in Cloud Storage as
well. This service requires minimal manual intervention and can be automated with Cloud Scheduler
or Cloud Functions. Therefore, using the batch prediction functionality of AI Platform is the best
option for this use case.
Reference:
Batch prediction overview
Using batch prediction

QUESTION 2
You work for a global footwear retailer and need to predict when an item will be out of stock based
on historical inventory data. Customer behavior is highly dynamic since footwear demand is influenced by many different
factors. You want to serve models that are trained on all available data, but track your performance
on specific subsets of data before pushing to production. What is the most streamlined and reliable
way to perform this validation?
A. Use the TFX ModelValidator tools to specify performance metrics for production readiness
B. Use k-fold cross-validation as a validation strategy to ensure that your model is ready forproduction.
C. Use the last relevant week of data as a validation set to ensure that your model is performingaccurately on current data
D. Use the entire dataset and treat the area under the receiver operating characteristics curve (AUC ROC) as the main metric.

Answer: A

Explanation:
TFX ModelValidator is a tool that allows you to compare new models against a baseline model and
evaluate their performance on different metrics and data slices1. You can use this tool to validate
your models before deploying them to production and ensure that they meet your expectations and requirements.
k-fold cross-validation is a technique that splits the data into k subsets and trains the model on k-1
subsets while testing it on the remaining subset. This is repeated k times and the average
performance is reported2. This technique is useful for estimating the generalization error of a model,
but it does not account for the dynamic nature of customer behavior or the potential changes in data distribution over time.
Using the last relevant week of data as a validation set is a simple way to check the models
performance on recent data, but it may not be representative of the entire data or capture the longterm
trends and patterns. It also does not allow you to compare the model with a baseline or evaluate it on different data slices.
Using the entire dataset and treating the AUC ROC as the main metric is not a good practice because
it does not leave any data for validation or testing. It also assumes that the AUC ROC is the only
metric that matters, which may not be true for your business problem. You may want to consider
other metrics such as precision, recall, or revenue.

QUESTION 3
You work on a growing team of more than 50 data scientists who all use Al Platform. You are
designing a strategy to organize your jobs, models, and versions in a clean and scalable way. Which strategy should you choose?

A. Set up restrictive I AM permissions on the Al Platform notebooks so that only a single user or group can access a given instance.
B. Separate each data scientist's work into a different project to ensure that the jobs, models, and versions created by each data scientist are accessible only to that user.
C. Use labels to organize resources into descriptive categories. Apply a label to each created resource so that users can filter the results by label when viewing or monitoring the resources
D. Set up a BigQuery sink for Cloud Logging logs that is appropriately filtered to capture information about Al Platform resource usage In BigQuery create a SQL view that maps users to the resources they are using.

Answer: C

Explanation:
Labels are key-value pairs that can be attached to any AI Platform resource, such as jobs, models,
versions, or endpoints1. Labels can help you organize your resources into descriptive categories, such
as project, team, environment, or purpose. You can use labels to filter the results when you list or
monitor your resources, or to group them for billing or quota purposes2. Using labels is a simple and
scalable way to manage your AI Platform resources without creating unnecessary complexity or overhead.
Therefore, using labels to organize resources is the best strategy for this use case.
Reference:
Using labels
Filtering and grouping by labels

QUESTION 4
During batch training of a neural network, you notice that there is an oscillation in the loss. How should you adjust your model to ensure that it converges?

A. Increase the size of the training batch
B. Decrease the size of the training batch
C. Increase the learning rate hyperparameter
D. Decrease the learning rate hyperparameter

Answer: D

Explanation:
Oscillation in the loss during batch training of a neural network means that the model is
overshooting the optimal point of the loss function and bouncing back and forth. This can prevent
the model from converging to the minimum loss value. One of the main reasons for this
phenomenon is that the learning rate hyperparameter, which controls the size of the steps that the
model takes along the gradient, is too high. Therefore, decreasing the learning rate hyperparameter
can help the model take smaller and more precise steps and avoid oscillation. This is a common
technique to improve the stability and performance of neural network training12.
Reference:
Interpreting Loss Curves
Is learning rate the only reason for training loss oscillation after few epochs?

QUESTION 5
You are building a linear model with over 100 input features, all with values between -1 and 1.
You suspect that many features are non-informative. You want to remove the non-informative features
from your model while keeping the informative ones in their original form. Which technique should you use?

A. Use Principal Component Analysis to eliminate the least informative features.
B. Use L1 regularization to reduce the coefficients of uninformative features to 0.
C. After building your model, use Shapley values to determine which features are the most informative.
D. Use an iterative dropout technique to identify which features do not degrade the model when removed.

Answer: B

Explanation:
L1 regularization, also known as Lasso regularization, adds the sum of the absolute values of the
models coefficients to the loss function1. It encourages sparsity in the model by shrinking some
coefficients to precisely zero2. This way, L1 regularization can perform feature selection and remove
the non-informative features from the model while keeping the informative ones in their original
form. Therefore, using L1 regularization is the best technique for this use case.
Reference:
Regularization in Machine Learning - GeeksforGeeks
Regularization in Machine Learning (with Code Examples) - Dataquest
L1 And L2 Regularization Explained & Practical How To Examples
L1 and L2 as Regularization for a Linear Model

QUESTION 6
Your team has been tasked with creating an ML solution in Google Cloud to classify support requests
for one of your platforms. You analyzed the requirements and decided to use TensorFlow to build the
classifier so that you have full control of the model's code, serving, and deployment. You will use
Kubeflow pipelines for the ML platform. To save time, you want to build on existing resources and
use managed services instead of building a completely new model. How should you build the classifier?

A. Use the Natural Language API to classify support requests
B. Use AutoML Natural Language to build the support requests classifier
C. Use an established text classification model on Al Platform to perform transfer learning
D. Use an established text classification model on Al Platform as-is to classify support requests

Answer: C

Explanation:
Transfer learning is a technique that leverages the knowledge and weights of a pre-trained model
and adapts them to a new task or domain1. Transfer learning can save time and resources by
avoiding training a model from scratch, and can also improve the performance and generalization of
the model by using a larger and more diverse dataset2. AI Platform provides several established text
classification models that can be used for transfer learning, such as BERT, ALBERT, or XLNet3. These
models are based on state-of-the-art natural language processing techniques and can handle various
text classification tasks, such as sentiment analysis, topic classification, or spam detection4. By using
one of these models on AI Platform, you can customize the models code, serving, and deployment,
and use Kubeflow pipelines for the ML platform. Therefore, using an established text classification
model on AI Platform to perform transfer learning is the best option for this use case.
Reference:
Transfer Learning - Machine Learnings Next Frontier