[kcdc 2025] AI Vulnerabilities in 2025 – Some of the darker sides of AI

Speaker: Andreas Erben

For more see the table of contents


From the news

What drives model behavior?

  • Initial training
  • Fine puning
  • Potential customer fine tuning
  • System prompt – how the model should behave.
  • Data/prompt
  • Filters on data, prompts, output

Other links

My take

After the first example, there was a bunch of content on how LLMs work. I tuned out a little during that section probably because familar. Then it got interesting again.

PASSED! Jeanne’s Experience Taking the Oracle Cloud Infrastructure Generative AI Professional

Today I took the Oracle Cloud Infrastructure 2025 Generative AI Professional certification and passed with a score of 86%. Passing is 68%. This roughly the score I got on the Foundations exam. Which isn’t surprising. I tend to make the same amount of “careless mistakes” on exams in general with this sort of thing. Even in elementary school. I’d know the material, and write down the wrong answer. Or misread a question.

It’s a 90 minute exam with 50 questions. This is 30 minutes and 10 questions more than the foundations exam. It took me 22 minutes for this exam (and about 10 minutes for the foundation). For both each question was pick one of four multiple choice questions. In many questions one or two were clear distractors. Why did this take twice as long? More questions was part of it. And another was this one had you reading code for some of the questions. Not a lot of code; just 2-6 lines or so. But that takes longer than reading words. Which is probably why the Java 21 cert was a problem with time. That was reading A LOT of code.

Why I took this certification

Oracle is doing a race to certification, where you can take a number of free certifications between now and Halloween. Unlike the Vector cert, which I took solely because it was free, this one I took both because it was free and to learn something. (same for foundations) And I did. Some was new to me and some I used to know and forgot. I especially appreciated learning/reviewing vocabulary and concepts.

What I did:

  • Watched videos and did skills checks from the free course. This was interesting. The skills check questions cover a good amount of the exam materials. I watched it on 2x speed. I also skipped most of the lab videos. I skipped the demos and focused on the concepts because I wasn’t interested in the Oracle Cloud specifics. I did this over two days. There is some repetition in the videos. For example, in context and k shot prompting was in modules 2 and 3. Same slides; different instructor.
  • Watched the video about preparing for the exam. It came with 4 practice questions which were similar to the exam.
  • Did practice exam. This was 50 questions. matching the real exam. The first time thru I got a 68%, which is exactly passing. (I didn’t review my notes at all) It was useful for knowing what I needed to remember. I reviewed 15 answers and then took it again getting an 84%. (I didn’t review them all because I accidentally closed the browser tab.

The exam

All the questions were single answer multiple choice. Like the Vector exam, you had to sign up for a slot in advance. Scheduling wasn’t bad though. I had a choice of any time during the 24 hours of Monday. (and a few 10:30pm or later Sunday night but I am a morning person)

Also, like the Vector exam, i took a picture and showed the computer my id. Then started.

I wrote a separate blog post about the exam engine. I had a few differences form that time though:

  • I had to download software to my machine for Proctorio (“Secure Companion App” and not just the browser plugin this time. I was sure to delete it right after.
  • I had to close my Terminal and Slack this time. While I certainly didn’t use them last time, I didn’t close them. (I don’t have alerts on Slack so it didn’t affect e)
  • Two of the questions had a multiline sentence as answer options that was behind the floating menu bar on the right. The first time, I dragged it was fine. The second time, I accidentally clicked the picture of me (my video) instead of the grab bar. This turned off my video. I clicked to turn it back on immediately and the proctor didn’t comment. There was nowhere good to put the bar though where it didn’t cover something on one question so I moved it a few times.

After the exam

You get a score report on the screen right after submitting and an email right away with the same score. It took a little under an hour to get the second email saying my certview was updated. While Oacle products were used as examples a lot, you only had to actually know about them for a few questions.

What I found most interesting

I like that the questions were a mix on this exam. Some were pure definitions. Some were scenarios where you had to identify a term or algorithm from the description. Some were code where you had to answer a question about it. (luckily the Python code was clear because remember I didn’t watch any of the demos or do the lab).

I also found it interesting how the exams are related. Some of the concepts from other exams were on this one.

How to Study

The learning path is sufficient to take the exam if you go through it carefully. And just like the other AI certs, pay lots of attention ot the sample questions and practice exams! There are only so many ways you can ask certain topics.

[javaone 2025] java for ai

Speakers: Paul Sandoz

See the table of contents for more posts


General

  • Not going to add XML to the platform as it changes. Scala did that and thinks wrong choice
  • Goals: developer productivity and program performance
  • Many features meet demands of AI but useful for other things
  • “All web companies grow up to be Java companies”. Want to make all AI companies grow up to be Java companies as well
  • AI on Java is harder than it should be

Panama – Foreign Function and Memory (FFM) API

  • Optimal usage for foreign (off heap) memory
  • Better interoperation with foreign (native) APIS
  • Let’s map to native libraries
  • ex: used for Matrix API using native BLIS. The matrix API is Java (for linear algebra)
  • jextract gives a C like interface in Java. Still have do deal with allocating memory
  • Zero copy memory since all memory is held natively on the heap. JVM is not involved

Panama – Vector API

  • SIMD (single instructions, multiple data) programming for optimal CPU utilization
  • Better number crunching
  • Useful for high performance data parallel algorithms
  • Incubating a long time because need value classes to be released
  • Example: https://github.com/mukel/llama3.java
  • Testcase to improve the runtime compiler.
  • Not as fast as native algorithm but might be good enough one day.
  • Show performance improvement from using Vector AI with a demo. The “before” was so slow it was hard to watch. The “after” was essentially at the speed of reading.

Valhalla – Value classes and objects

  • Optimal utilization of on heap member
  • Enables more kinds of numbers
  • Runtime will optimize
  • More kinds of numbers — ex:Float16, Decimal64, Complex, Interval, etc
  • Incubating

Babylon – Code Reflection

  • Interoperates with foreign programming models – ex onnx
  • Better use of hardware and better number crunching
  • For foreign programming models aka not the one for the Java Language Specifications. Ex: GPU, autoparallelization of for loops
  • New Java code model — symbolic in memory representation of java code using a tree like structure. Like the compiler’s AST but more suited for analysis and transformation

My take

I like that he was able to go into more detail than we got into at the keynote. Good demo. Some of it was too advanced for me (not an exert on the matrix stuff), but I learned a lot. I missed a bunch. I went to google something I didn’t know and fell down a rabbit hole. He also showed Onnx which we saw during the keynote.