The Cisco CCNA network associate certification validates the ability to install, configure, operate, and troubleshoot medium-size routed and switched networks, more at http://www.certkingdom.com
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
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.
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.
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
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.
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.
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.
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.
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.
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
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.
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 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
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.
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