All Certifications posts

Havent received project expert badge

prathikkinim — Wed, 02 Sep 2026 14:46:17 GMT

Hi Team,

I wanted to let you know that I have completed the Databricks Data Engineer Professional certification recently.

I have received the Delivery Expert badge after completing the requirements few days back. However, it has now been more than 48 hours since I completed the exam, and I have not yet received the Project Expert badge.

Could you please check the status of the Project Expert badge and let me know if any additional action is required from my side?

Thank you for your help.

Best regards,
Prathik

Re: Certification Not Issued

Psangi — Wed, 02 Sep 2026 12:09:18 GMT

@cert-ops

Hello Databricks Community,

I am reaching out to seek assistance or visibility regarding two support cases (00998141 and 01000010) that have been open for over ten days now following the successful completion of my certification exam.

As a passionate advocate and daily user of Databricks, I have always deeply admired the platform's technological capabilities. However, experiencing this extended silence from the support team has been quite discouraging. A timely acknowledgment and sense of ownership regarding certification credentials are vital—not just for professional tracking, but also for properly aligning credentials with corporate partner requirements.

Given the standard of excellence expected from a leader in the Data and AI space, I had anticipated a more responsive support experience. Could anyone on the moderation or community team kindly help escalate these cases so my badge can finally be issued and linked?

Thank you in advance for your guidance and support.

Re: Ticket Escalation: Request for Certificate & Webassessor Name Correction (Case #00980951)

cert-ops — Wed, 02 Sep 2026 11:07:07 GMT

Hello @Salam7 ,

Thank you for filing a ticket with our support team, Support team will respond shortly. Please note that we cannot provide support via community.

Thanks & Regards,
@cert-ops

Re: Webassesor last name change

cert-ops — Wed, 02 Sep 2026 11:05:18 GMT

Hello @Ds_1_0 ,

Thank you for filing a ticket with our support team, Support team will respond shortly. Please note that we cannot provide support via community.

Thanks & Regards,
@cert-ops

Re: Unable to change password on Webassesor

niteshm — Tue, 01 Sep 2026 23:45:29 GMT

Is this issue resolved?

Webassesor last name change

Ds_1_0 — Tue, 01 Sep 2026 17:00:12 GMT

Hi, anyone knows how to contact databricks support? Or how long it takes to get any response from them?

I need to change my last name in webassesor, can't do this my self.

Already had to move my exam 4 times becasue support is not responsive. I've tried both... an email and the form..

Re: Certification Not Issued

Psangi — Tue, 01 Sep 2026 14:09:10 GMT

I have already raised two support cases after checking my spam folder and I'm only here because of no response from Databricks support. Below are the two support tickets raised for the same.

I understand that your team may not provide support through community but what should a person do in spite of following up since last 10 days with tickets not even being acknowledged. I almost spent 3-5 hours on this single matter and you can imaging how frustrating it would be for an individual.

(00998141 and 01000010)

Re: Certification Not Issued

Psangi — Tue, 01 Sep 2026 13:55:19 GMT

Thanks for at least responding here, I'm still waiting to hear back from support and its been more than 10 days that I cleared certification and still waiting for the badge to be issued.

Re: Unable to change password on Webassesor

cert-ops — Tue, 01 Sep 2026 12:21:22 GMT

Hello @alg ,

Please file a ticket with our support team so they can review the case and determine next steps. Please note that we cannot provide support via community.

Thanks & Regards,
@cert-ops

Re: URGENT – Training Request #00987902 – Exam Language Confirmation/Change

cert-ops — Tue, 01 Sep 2026 12:19:11 GMT

Hello @Argelia,

Thank you for filing a ticket with our support team, Support team will respond shortly. Please note that we cannot provide support via community.

Thanks & Regards,
@cert-ops

Re: Certification Not Issued

cert-ops — Tue, 01 Sep 2026 12:16:51 GMT

Hello @Psangi,

Thank you for filing a ticket with our support team, support team will respond shortly. It can take up to 48 hours for badges/ certificates to be issued. Also please check your spam just in case it went there. Please note that we cannot provide support via community.

Thanks & Regards,
@cert-ops

Re: Project Expert badge not received

cert-ops — Tue, 01 Sep 2026 12:13:42 GMT

Hello @SubikshaT,

It can take up to 48 hours for badges/ certificates to be issued. Also please check your spam just in case it went there. If you do not receive your badge in 48 hours, please file a ticket with our support team. Please also provide them with your email address so that they can look up your account (it is not advised that you post your email address here in community).

Thanks & Regards,
@cert-ops

Re: Ticket #01001225 - Problema de Permissão na validação do Webcam

cert-ops — Tue, 01 Sep 2026 12:11:39 GMT

Olá @Jgoncalvesm1 ,

Obrigado por abrir um ticket com nossa equipe de suporte. A equipe de suporte responderá em breve.

Observe que não podemos fornecer suporte ou tratar de suspensões de exames por meio da comunidade.

Obrigado e atenciosamente,
@cert-ops

Project Expert badge not received

SubikshaT — Tue, 01 Sep 2026 12:07:54 GMT

Hi,
I have completed all required Delivery Expert badges and hold the required Associate/ Professional Certification. However, I have not yet received the corresponding Project Expert badge. Could you please assist with badge issuance?

Re: New free resource: an AI Prep Guide for any Databricks certification

Cert-Team — Mon, 31 Aug 2026 16:48:38 GMT

@T_Anwar glad to know it helped!!

Re: Difference in exam content between the PT-BR and English versions - Databricks Certified Data En

Advika — Mon, 31 Aug 2026 13:35:01 GMT

Hello @GustavoRaniery,

Thanks for flagging this! Please raise a ticket with the Databricks Support Team. They’ll be able to look into the differences you’ve noticed and help clarify your concern.

Re: Insights Requested: Databricks Certified Data Engineer Associate Exam Experience & PatternCo

Advika — Mon, 31 Aug 2026 13:17:10 GMT

Hello @saisaranv,

You can check out the Exam Guide, which includes the topics covered in the exam, recommended training courses, and sample questions to help you prepare.

You can also use the AI Prep Guide, a free, exam-agnostic resource that shows you how to effectively use any AI chatbot to prepare for Databricks certification exams.

These resources should help you as you get closer to your exam. Best of luck with your preparation!

Databricks ML Professional Certification: What Changed Between My First and Second

AngelShrestha — Mon, 31 Aug 2026 04:28:46 GMT

Everyone writes these after passing on the first try. That's part of why they're not that useful. This is thesecond-attempt version.

Please keep this in mind as you read: this is my story, not a formula. What tripped me up might be easy for you, and what I'm about to call obvious might be the exact thing you're stuck on right now. That's fine. That's just where we each are.

I sat the Databricks Certified Machine Learning Professional exam, didn't pass, went back, rebuilt how I studied from the ground up, and passed on the second attempt.

About the Exam, Briefly

The Databricks Certified Machine Learning Professional exam tests the full arc of production ML on Databricks — building distributed pipelines, running the MLOps lifecycle, deploying and monitoring models at scale. It's 59 scored multiple-choice questions in 120 minutes, roughly split across three domains:

Domain Weight Focus Area

Model Development	~44%	SparkML pipelines, HPO, MLflow, Feature Store
MLOps	~44%	Lifecycle management, validation, environments, retraining, drift
Model Deployment	~12%	Deployment strategies, custom model serving

(Weights shift between exam versions — always check the current official exam guide before you plan your study hours around any table you find online, including this one. https://www.databricks.com/learn/certification/machine-learning-professional )

Don't let that smaller Model Deployment percentage fool you into deprioritizing it. It's tempting to assume it's "just serving a model" and give it less time than it deserves. That assumption is exactly what got me the first time.

Why Knowing Spark Isn't Optional Here

If you're coming into this from a Data Engineering or analytics background, you've probably done most of your work in SQL, and honestly for a lot of that work, SQL is enough. It's readable, it's powerful, it'll get you through most Delta Live Tables and Medallion Architecture work without ever opening a notebook full of PySpark.

That stops being true the moment you're doing ML on Databricks.

ML workflows run on distributed data. Vectorizing features, training at scale, tuning hyperparameters across a cluster, all of it runs through pyspark.ml, and the Professional exam expects you to be fluent reading it, not just aware it exists. You'll be shown pipeline code and asked what's wrong with it far more often than you'll be asked to write anything from scratch. So get comfortable with:

Estimator vs. Transformer mechanics, and why a fitted Pipeline gives you back a PipelineModel that predicts with .transform(), never .predict()
VectorAssembler, StringIndexer, and where OneHotEncoder belongs (and where it doesn't)
CrossValidator and TrainValidationSplit for tuning
MLflow's logging primitives; log_param(), log_metric(), nested runs
Unity Catalog's role in model tracking and deployment

None of these are checkbox topics. They're the actual production skills the exam is a proxy for.

What I Actually Changed the Second Time Around

I stopped re-reading and started diagnosing. The first time, I studied the way most people default to: read the docs, watch the course, take a practice test, hope it sticks. But this exam doesn't test recall: it tests whether you can read a scenario paragraph, catch the one constraint that changes the answer, and work out why a described pipeline is broken. Re-reading material doesn't build that muscle. Working through every concepts until you can explain your reasoning out loud does.

I worked a real scenarios deep, not fast. I went through all the objectives in the dump one at a time, and for each one asked not just "is it one or other" but "what would this scenario have to say for every other option to be the correct one instead.
Here's roughly what that looked like, pulled from my own notes:

A Data Scientist is running k-fold cross-validation for hyperparameter tuning, then retraining the best config on the full dataset. They want per-config metrics, a link between the final model and its search process, and easy comparison in the MLflow UI.

The wrong answers here weren't lazy distractors: each one is correct for a different design goal.
Nesting a parent run per CV fold looks reasonable right up until you realize it scatters one config's results across multiple parents, wrecking the exact UI comparison the question asked for.
The answer is a parent run for the experiment, one child run per hyperparameter configuration, and a separate run for final model evaluation.
Knowing why the other three fail a specific requirement is the actual skill being tested; not knowing that nested runs exist.

I built decision tables, not a notes doc. I stopped writing things like "MinMaxScaler exists" and started writing things like:

StringIndexer → OneHotEncoder → VectorAssembler is the safe general chain — but tree-based models (Random Forest, GBT) can skip OneHotEncoder entirely, since Spark trees read categorical metadata natively off StringIndexer. Linear or logistic models can't skip it without implying false ordinality.
.persist() is in-memory/disk caching for the current session. .write().save() is durable model serialization. Confusing the two is a classic distractor, and I fell for it more than once in practice.
Batch-to-streaming scoring only requires one change: spark.read → spark.readStream. Table name, schema, and prediction UDF all stay identical, because Delta's batch/streaming design is unified. If an answer option implies you need to rewrite the pipeline to go streaming, that's your tell it's wrong.
In a custom PyFunc model, expensive initialization belongs in load_context(), which runs once — not in predict(), which runs on every single request. This exact anti-pattern shows up more than once, dressed up as a "why is latency high" scenario.

Every one of those reads like trivia sitting on its own. On the actual exam, they show up wrapped in a paragraph describing symptoms, and you have to work backward to the cause.

I hunted down the traps by name. This exam moves faster than most third-party study material does. Two worth flagging specifically:

Hyperopt is deprecated. If a question is testing current best practice for hyperparameter tuning, the expected answer is Optuna or Ray Tune; even though a lot of circulating material still centers Hyperopt.
Workspace Model Registry stages and webhooks are out. Unity Catalog's alias-based promotion pattern is current. If an option leans on stage="Production", be suspicious of it.
https://www.databricks.com/sites/default/files/2025-10/databricks-certified-machine-learning-profess...

I drilled the confusion clusters until they stopped being confusing. A short list of what kept costing me points until I gave each one dedicated reps:

SparkTrials is safe for tuning single-node models in parallel. It is not safe to wrap around a SparkML model; that nests distributed computation inside distributed computation.
Log loss vs. F1: reach for log loss when the probability itself drives an automated downstream action, not just when you want an accuracy-adjacent number.
Consecutive drift vs. baseline drift: are you comparing the current window to the one right before it, or to the original reference distribution? The exam expects you to know which comparison a given monitoring setup is actually running.
mlflow.autolog() is training-only. It does not extend to production inference, no matter how the question tries to imply otherwise.
workload_size concurrency ceilings on Model Serving endpoints showed up in more than one variant: worth memorizing the actual limits rather than reasoning about them from scratch under time pressure.

Set Up an Actual Workspace If You Can

One of the better decisions I made was treating this less like an exam to pass and more like a small system to build. If you have access, spin up a real Databricks environment; even a single metastore with dev, test, and prod workspaces is enough to make Feature Store, Unity Catalog, and deployment questions click in a way that reading about them never quite does.

Pro tip: use small compute for this and set your cluster to auto-terminate after 30 minutes of inactivity. Your wallet will thank you.

Exam Day, in Short

Diagnose before you look at the options; for any question describing broken or slow behavior, work out which component is actually failing before you read A through D. The options are written to reward pattern-matching on keywords if you let them.

The buried constraint is usually the whole question. Two options can look equally correct until a throughput number, a latency SLA, or a "long term" versus "one-time" qualifier sitting mid-paragraph tells you which one actually survives.

What I'd Tell Myself Before Attempt One

Don't just aim to pass: aim to understand why the wrong answers are wrong. Stop collecting new practice questions before you've squeezed everything out of the ones you already have.
Somewhere around question dump, going deep instead of fast stopped feeling like a study technique and started feeling like the only way I actually knew anything.

The certification is a nice outcome. The reason it's worth this much effort is that the underlying skills; reading Spark pipelines, reasoning about MLflow's run hierarchy, knowing when a deployment pattern actually fits are the same ones I'll use on the next real system I build, badge or no badge.

Because in the end, it’s not just about the certification.
It’s about the learning journey.

#MachineLearningProfessional
#Certification
#Learning

Ticket #01001225 - Problema de Permissão na validação do Webcam

Jgoncalvesm1 — Sun, 30 Aug 2026 22:20:25 GMT

Olá Equipe de Treinamento da Databricks,

Estou escrevendo para fornecer detalhes adicionais referentes ao meu ticket nº 01001225.

Infelizmente, não consegui concluir meu exame devido a um problema técnico: um erro na permissão de validação da webcam. Por causa dessa falha no sistema, minha sessão de exame foi suspensa/cancelada.

Como se tratou de um problema técnico fora do meu controle, solicito gentilmente sua ajuda para remarcar meu exame ou emitir um novo voucher para que eu possa refazer a prova.

Agradeço seu apoio e aguardo sua atualização.

Atenciosamente,
[Jefferson Goncalves]

Certification Not Issued

Psangi — Sun, 30 Aug 2026 15:51:48 GMT

Hi Everyone,

I know this may not be the right place to raise a support-related issue, but I’m reaching out to see if anyone from the community can offer guidance or additional assistance.

I passed my Databricks Data Engineer Associate certification seven days ago; however, I have not yet received my certification badge. I have already raised two support cases with Databricks Support, but unfortunately I have not received an acknowledgement or response.

Has anyone experienced a similar delay, or can advise on the appropriate next step to get this resolved?

Thank you in advance for any help.

Regards,
Paramesh