This Week in AI... Why Agentic Systems, GPT-5.2, and Open Models Matter More Than Ever

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If it feels like the AI world is moving faster every week, you’re not imagining it.

In just a few days, we’ve seen new open-source foundations launched, major upgrades to large language models, cheaper and faster coding agents, powerful vision-language models, and even sweeping political moves aimed at reshaping how AI is regulated.

Instead of treating these as disconnected announcements, let’s slow down and look at the bigger picture. What’s actually happening here? Why do these updates matter? And what do they tell us about where AI is heading next?

This post breaks it all down — without the hype, and without assuming you already live and breathe AI research papers.

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The Quiet Rise of Agentic AI (And Why Governance Matters)

One of the most important stories this week didn’t come with flashy demos or benchmark charts.

The Agentic AI Foundation (AAIF) was created to provide neutral governance for a growing ecosystem of open-source agent technologies. That might sound bureaucratic, but it’s actually a big deal.

At launch, AAIF is stewarding three critical projects:

• Model Context Protocol (MCP) from Anthropic

• Goose, Block’s agent framework built on MCP

• AGENTS.md, OpenAI’s lightweight standard for describing agent behavior in projects

If you’ve been following AI tooling closely, you’ve probably noticed a shift. We’re moving away from single prompt → single response systems, and toward agents that can:

• Use tools

• Access files and databases

• Call APIs

• Make decisions across multiple steps

• Coordinate with other agents

MCP, in particular, has quietly become a backbone for this movement. With over 10,000 published servers, it’s turning into a kind of “USB-C for AI agents” — a standard way to connect models to tools and data.

What makes AAIF important is not just the tech, but the governance. Instead of one company controlling these standards, the foundation includes contributors from AWS, Google, Microsoft, OpenAI, Anthropic, Cloudflare, Bloomberg, and others.

That signals something important:

Agentic AI isn’t a side experiment anymore — it’s infrastructure.

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GPT-5.2: The AI Office Worker Has Arrived

Now let’s talk about the headline grabber: GPT-5.2.

OpenAI positions GPT-5.2 as a model designed specifically for white-collar knowledge work. Think spreadsheets, presentations, reports, codebases, and analysis — the kind of tasks that dominate modern office jobs.

According to OpenAI’s claims, GPT-5.2:

• Outperforms human professionals on ~71% of tasks across 44 occupations (GDPval benchmark)

• Runs 11× faster than previous models

• Costs less than 1% of earlier generations for similar workloads

Those numbers are bold, but the more interesting part is how the model is being framed.

GPT-5.2 isn’t just “smarter.” It’s packaged as a document-first, workflow-aware system:

• Building structured spreadsheets

• Creating polished presentations

• Writing and refactoring production code

• Handling long documents with fewer errors

Different variants target different needs:

• GPT-5.2 Thinking emphasizes structured reasoning

• GPT-5.2 Pro pushes the limits on science and complex problem-solving

• GPT-5.2 Instant focuses on speed and responsiveness

The takeaway isn’t that AI is replacing all office workers tomorrow. It’s that AI is becoming a reliable first draft for cognitive labor — not just text, but work artifacts.

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Open Models Are Getting Smaller, Cheaper, and Smarter

While big proprietary models grab headlines, some of the most exciting progress is happening in open-source land.

Mistral’s Devstral 2: Serious Coding Power, Openly Licensed

Mistral released Devstral 2, a 123B-parameter coding model, alongside a smaller 24B version called Devstral Small 2.

Here’s why that matters:

• Devstral 2 scores 72.2% on SWE-bench Verified

• It’s much smaller than competitors like DeepSeek V3.2

• Mistral claims it’s up to 7× more cost-efficient than Claude Sonnet

• Both models support massive 256K token contexts

Even more importantly, the models are released under open licenses:

• Modified MIT for Devstral 2

• Apache 2.0 for Devstral Small 2

That means companies can run, fine-tune, and deploy these models without vendor lock-in.

Mistral also launched Mistral Vibe CLI, a tool that lets developers issue natural-language commands across entire codebases — a glimpse into how coding agents will soon feel more like collaborators than autocomplete engines.

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Vision + Language + Tools: A New Kind of Reasoning Model

Another major update came from Zhipu AI, which released GLM-4.6V, a vision-language reasoning model with native tool calling.

This is subtle, but powerful.

Instead of treating images as passive inputs, GLM-4.6V can:

• Accept images as parameters to tools

• Interpret charts, search results, and tool outputs

• Reason across text, visuals, and structured data

In practical terms, that enables workflows like:

• Turning screenshots into functional code

• Analyzing documents that mix text, tables, and images

• Running visual web searches and reasoning over results

With both large (106B) and local (9B) versions available, this kind of multimodal agent isn’t just for big cloud players anymore.

---

Developer Tools Are Becoming Agentic, Too

AI models aren’t the only thing evolving — developer tools are changing alongside them.

Cursor 2.2 introduced a new Debug Mode that feels like an early glimpse of agentic programming environments.

Instead of just pointing out errors, Cursor:

1. Instruments your code with logs

2. Generates hypotheses about what’s wrong

3. Asks you to confirm or reproduce behavior

4. Iteratively applies fixes

It also added a visual web editor, letting developers:

• Click on UI elements

• Inspect props and components

• Describe changes in plain language

• Update code and layout in one integrated view

This blending of code, UI, and agent reasoning hints at a future where “programming” looks much more collaborative — part conversation, part verification.

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The Political Dimension: Centralizing AI Regulation

Not all AI news is technical.

This week also saw a major U.S. executive order aimed at creating a single federal AI regulatory framework, overriding state-level laws.

The order:

• Preempts certain state AI regulations

• Establishes an AI Litigation Task Force

• Ties federal funding eligibility to regulatory compliance

• Directs agencies to assess whether AI output constraints violate federal law

Regardless of where you stand politically, this move reflects a growing realization:
AI governance is now a national infrastructure issue, not just a tech policy debate.

As AI systems become embedded in healthcare, finance, education, and government, fragmented regulation becomes harder to sustain.

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The Bigger Pattern: AI Is Becoming a System, Not a Tool

If there’s one thread connecting all these stories, it’s this:

AI is no longer about individual models — it’s about systems.

We’re seeing:

• Standards for agent behavior

• Open governance for shared infrastructure

• Models optimized for workflows, not prompts

• Tools that reason, debug, and collaborate

• Governments stepping in to shape long-term direction

The era of “just prompt it” is fading. What’s replacing it is more complex — and more powerful.

Agents need scaffolding. Models need context. Tools need interoperability. And humans are shifting from direct operators to supervisors, reviewers, and designers of AI-driven processes.

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So What Should You Take Away From This?

If you’re a student, developer, or knowledge worker, here’s the practical takeaway:

• Learn how agentic workflows work — not just prompting

• Pay attention to open standards like MCP

• Don’t ignore smaller, cheaper models — they’re closing the gap fast

• Expect AI tools to increasingly ask for confirmation, not blind trust

• Understand that AI’s future will be shaped as much by policy and governance as by benchmarks

The AI race isn’t just about who builds the biggest model anymore.

It’s about who builds the most usable, reliable, and well-governed systems — and who learns to work with them intelligently.

And that race is just getting started.

GPT-5.2, Gemini, and the AI Race -- Does Any of This Actually Help Consumers?

See All on AI Model Releases

The AI world is ending the year with a familiar cocktail of excitement, rumor, and exhaustion. The biggest talk of December: OpenAI is reportedly rushing to ship GPT-5.2 after Google’s Gemini models lit up the leaderboard. Some insiders even describe the mood at OpenAI as a “code red,” signaling just how aggressively they want to reclaim attention, mindshare, and—let’s be honest—investor confidence.

But amid all the hype cycles and benchmark duels, a more important question rises to the surface:

Are consumers or enterprises actually better off after each new model release? Or are we simply watching a very expensive and very flashy arms race?

Welcome to Mixture of Experts.

---

The Model Release Roller Coaster

A year ago, it seemed like OpenAI could do no wrong—GPT-4 had set new standards, competitors were scrambling, and the narrative looked settled. Fast-forward to today: Google Gemini is suddenly the hot new thing, benchmarks are being rewritten, and OpenAI is seemingly playing catch-up.

The truth? This isn’t new. AI progress moves in cycles, and the industry’s scoreboard changes every quarter. As one expert pointed out: “If this entire saga were a movie, it would be nothing but plot twists.”

And yes—actors might already be fighting for who gets to play Sam Altman and Demis Hassabis in the movie adaptation.

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Does GPT-5.2 Actually Matter?

The short answer: Probably not as much as the hype suggests.

While GPT-5.2 may bring incremental improvements—speed, cost reduction, better performance in IDEs like Cursor—don’t expect a productivity revolution the day after launch.

Several experts agreed:

• Most consumers won’t notice a big difference.

• Most enterprises won’t switch models instantly anyway.

• If it were truly revolutionary, they’d call it GPT-6.

The broader sentiment is fatigue. It seems like every week, there’s a new “state-of-the-art” release, a new benchmark victory, a new performance chart making the rounds on social media. The excitement curve has flattened; now the industry is asking:

Are we optimizing models, or just optimizing marketing?

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Benchmarks Are Broken—But Still Drive Everything

One irony in today’s AI landscape is that everyone agrees benchmarks are flawed, easily gamed, and often disconnected from real-world usage. Yet companies still treat them as existential battlegrounds.

The result:
An endless loop of model releases aimed at climbing leaderboard rankings that may not reflect what users actually need.

Benchmarks motivate corporate behavior more than consumer benefit. And that’s how we get GPT-5.2 rushed to market—not because consumers demanded it, but because Gemini scored higher.

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The Market Is Asking the Wrong Question About Transparency

Another major development this month: Stanford’s latest AI Transparency Index. The most striking insight?

Transparency across the industry has dropped dramatically—from 74% model-provider participation last year to only 30% this year.

But not everyone is retreating. IBM’s Granite team took the top spot with a 95/100 transparency score, driven by major internal investments in dataset lineage, documentation, and policy.

Why the divergence?

Because many companies conflate transparency with open source.
And consumers—enterprises included—aren’t always sure what they’re actually asking for.

The real demand isn’t for “open weights.” It’s for knowability:

• What data trained this model?

• How safe is it?

• How does it behave under stress?

• What were the design choices?

Most consumers don’t have vocabulary for that yet. So they ask for open source instead—even when transparency and openness aren’t the same thing.

As one expert noted:
“People want transparency, but they’re asking the wrong questions.”

---

Amazon Nova: Big Swing or Big Hype?

At AWS re:Invent, Amazon introduced its newest Nova Frontier models, with claims that they’re positioned to compete directly with OpenAI, Google, and Anthropic.

Highlights:

• Nova Forge promises checkpoint-based custom model training for enterprises.

• Nova Act is Amazon’s answer to agentic browser automation, optimized for enterprise apps instead of consumer websites.

• Speech-to-speech frontier models catch up with OpenAI and Google.

Sounds exciting—but there’s a catch.

Most enterprises don’t actually want to train or fine-tune models.

They think they do.
They think they have the data, GPUs, and specialization to justify it.

But the reality is harsh:

• Fine-tuning pipelines are expensive and brittle.

• Enterprise data is often too noisy or inconsistent.

• Tool-use, RAG, and agents outperform fine-tuning for most use cases.

Only the top 1% of organizations will meaningfully benefit from Nova Forge today.
Everyone else should use agents, not custom models.

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The Future: Agents That Can Work for Days

Amazon also teased something ambitious: frontier agents that can run for hours or even days to complete complex tasks.

At first glance, that sounds like science fiction—but the core idea already exists:

• Multi-step tool use

• Long-running workflows

• MapReduce-style information gathering

• Automated context management

• Self-evals and retry loops

The limiting factor isn’t runtime. It’s reliability.

We’re entering a future where you might genuinely say:

“Okay AI, write me a 300-page market analysis on the global semiconductor supply chain,”
and the agent returns the next morning with a comprehensive draft.

But that’s only useful if accuracy scales with runtime—and that’s the new frontier the industry is chasing.

As one expert put it:

“You can run an agent for weeks. That doesn’t mean you’ll like what it produces.”

---

So… Who’s Actually Winning?

Not OpenAI.
Not Google.
Not Amazon.
Not Anthropic.

The real winner is competition itself.

Competition pushes capabilities forward.
But consumers? They’re not seeing daily life transformation with each release.
Enterprises? They’re cautious, slow to adopt, and unwilling to rebuild entire stacks for minor gains.

The AI world is moving fast—but usefulness is moving slower.

Yet this is how all transformative technologies evolve:
Capabilities first, ethics and transparency next, maturity last.

Just like social media’s path from excitement → ubiquity → regulation,
AI will go through the same arc.

And we’re still early.

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Final Thought

We’ll keep seeing rapid-fire releases like GPT-5.2, Gemini Ultra, Nova, and beyond. But model numbers matter less than what we can actually build on top of them.

AI isn’t a model contest anymore.
It’s becoming a systems contest—agents, transparency tooling, deployment pipelines, evaluation frameworks, and safety assurances.

And that’s where the real breakthroughs of 2026 and beyond will come from.

Until then, buckle up. The plot twists aren’t slowing down.

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GPT-5.2 is now live in the OpenAI API

GPT-5.2 is now available in the API   Today we released GPT-5.2 in the API and ChatGPT—our most advanced frontier model yet and our best model for real-world agentic work. GPT-5.2 excels at coding, document & data analysis, and customer support use cases.

Build with GPT 5.2

Here’s why you may want to consider switching your workloads to GPT-5.2: SOTA on long-context understanding: GPT-5.2 beats other models on the OpenAI MRCRv2 long-context eval, and customers like Notion, Box, Databricks, and Hex report strong reasoning performance on complex, ambiguous, data-heavy tasks. Advanced tool-calling capabilities: GPT-5.2 is SOTA on Tool Decathlon and beats other models on τ²-Bench Telecom, both benchmarks for long-horizon tool use. Triple Whale and Zoom say GPT-5.2 enables more reliable agent execution through improved tool calling. Our strongest vision model yet: GPT-5.2 is our strongest vision model yet, cutting chart-reasoning and UI-understanding errors by over 50%. Enhanced spatial reasoning makes it more reliable for complex dashboards, app UIs, and diagram analysis. SOTA on coding: GPT-5.2 leads on SWE-Bench Pro, a benchmark for complex coding tasks. It excels at front-end UI generation and delivers meaningful improvements across debugging, refactoring, and shipping fixes.  GPT-5.2 is now available in the Responses and Chat Completions API. The model adjusts its reasoning based on the complexity of the task and you can control the reasoning effort by setting it to none, low, medium, high, and for the first time, “xhigh” for the most complex tasks. GPT-5.2 is 40% more expensive than GPT-5 and GPT-5.1. It costs $1.75/1M input tokens and $14/1M output tokens, with a 90% discount on cached inputs. The model is available on Priority Processing and Flex Processing plans, and can be used with the Batch API. More details on the API Pricing Page.   We’ve published prompting guidance and updated our Prompt Optimizer tool to help you get the most out of GPT-5.2.

Build with GPT 5.2

—The OpenAI Team

© 2025 OpenAI. All Rights Reserved. 3180 18th St, San Francisco, CA 94110

Data Engineer - Mphasis USA - Nov 18, 2025

See All: Miscellaneous Interviews @ FloCareer

1. RATE CANDIDATE'S SKILLS
   Databricks
   AWS
   PySpark
   Splunk
   

2. Implementing Machine Learning Models using Databricks and AWS
   Your team needs to deploy a machine learning model in production using Databricks and AWS services. Describe your approach to implement and deploy this model.
   Ideal Answer (5 Star)
   To deploy a machine learning model, start by developing and training the model using Databricks' MLlib or another library like TensorFlow or PyTorch. Use Databricks notebooks for collaborative development and experimentation. Leverage AWS SageMaker for model training and hosting if preferred. Store training data in AWS S3, and use Databricks' integration with S3 for seamless data access. Once the model is trained, use MLflow for model management and tracking. Deploy the model as a REST API using AWS Lambda or Databricks REST API for scalable access. Monitor model performance and update the model as needed based on new data or requirements.

3. Building a Splunk Dashboard for Business Metrics
   Your team requires a Splunk dashboard that displays real-time business metrics for executive stakeholders. These metrics include sales figures, customer acquisition rates, and system uptime. How would you design this dashboard to ensure usability and clarity?
   Ideal Answer (5 Star)
   To design a Splunk dashboard for executive stakeholders, I would start by identifying the key metrics and KPIs that need to be displayed. I would use panels to segregate different categories of metrics, such as sales, customer acquisition, and system uptime. For usability, I would design the dashboard with a clean layout using visualizations like line charts for trends, single value panels for KPIs, and heatmaps for real-time data. I would incorporate dynamic filters to allow stakeholders to drill down into specific time periods or regions. Additionally, I would ensure the dashboard is responsive and accessible on various devices by using Splunk's Simple XML and CSS for custom styling.

4. Handling Data Skew in PySpark
   You are working with a PySpark job that frequently fails due to data skew during a join operation. Explain how you would handle data skew to ensure successful execution.
   Ideal Answer (5 Star)
   To handle data skew in PySpark, I would start by identifying skewed keys using `groupBy('key').count().orderBy('count', ascending=False).show()`. For skew mitigation, I would consider techniques such as salting, where I add a random suffix to keys to distribute data more evenly across partitions. This involves modifying the join key as df.withColumn('salted_key', concat(col('key'), lit('_'), rand())). Using skewed key handling functions like skewedJoin() can also help. If the skew is due to a small number of distinct keys, broadcasting a small dataset with broadcast(df) can also improve performance.

5. Implementing a Data Governance Framework
   Your organization is implementing a data governance framework on Databricks to ensure compliance and data security. Describe the key components you would include in this framework and how you would implement them.
   Ideal Answer (5 Star)
   To implement a data governance framework on Databricks, I would include:
   
   Data Cataloging: Use Databricks' Unity Catalog to maintain an inventory of datasets, their metadata, and lineage.
   
   Access Controls: Implement role-based access controls (RBAC) to manage data access permissions.
   
   Data Encryption: Enable encryption at rest and in transit to secure data.
   
   Compliance Monitoring: Use logging and monitoring tools like Splunk to track access and changes to data for compliance auditing.
   
   Data Quality and Stewardship: Assign data stewards for critical datasets and implement data quality checks.
   
   Training and Awareness: Conduct regular training sessions for employees on data governance policies and best practices.

6. Building a Real-time Analytics Dashboard using PySpark and AWS
   Your team needs to build a real-time analytics dashboard that processes streaming data from AWS Kinesis and displays insights using PySpark on Databricks. What is your approach to design such a system?
   Ideal Answer (5 Star)
   For building a real-time analytics dashboard, start by ingesting data using AWS Kinesis Data Streams to handle high-throughput real-time data. Use AWS Glue to transform raw data and AWS Lambda to trigger additional processing if needed. In Databricks, use PySpark's structured streaming capabilities to process the streamed data. Design the PySpark job to read directly from Kinesis, apply necessary transformations, and write processed data to an optimized storage solution like Delta Lake for real-time queries. Implement visualization tools like AWS QuickSight or integrate with BI tools to create the dashboard. Ensure the system is fault-tolerant by setting up appropriate checkpoints and error handling in Spark.
   

7. Disaster Recovery Planning for Data Engineering Solutions
   Your company needs a robust disaster recovery plan for its data engineering solutions built on AWS and Databricks. Outline your strategy for implementing disaster recovery.
   Ideal Answer (5 Star)
   For disaster recovery, start by setting up AWS S3 cross-region replication to ensure data redundancy. Use AWS Backup to automate and manage backups of AWS resources. Implement database snapshots and backups for RDS and Redshift. In Databricks, regularly export critical configurations and notebooks. Use Databricks' REST API to automate the export and import of notebooks and clusters for recovery purposes. Test the disaster recovery plan regularly by simulating failures and ensuring that RTO (Recovery Time Objective) and RPO (Recovery Point Objective) are met. Document the recovery procedures and ensure all team members are trained on the recovery protocols.

8. Handling Large-scale Data Migrations
   You are tasked with migrating large datasets from on-premises Hadoop clusters to AWS S3 and processing them in Databricks. Describe the approach you would take to ensure a smooth and efficient migration.
   Ideal Answer (5 Star)
   To handle large-scale data migrations from Hadoop to AWS S3 for processing in Databricks, I would:
   
   Data Transfer: Use AWS Direct Connect or AWS Snowball for efficient data transfer from on-premises to AWS S3.
   
   Data Format: Convert data to an optimized format like Parquet or ORC to reduce storage and increase processing efficiency.
   
   Security: Ensure data is encrypted during transfer and at rest in S3 using AWS KMS.
   
   Incremental Migration: Implement incremental data transfer to minimize downtime and validate data integrity.
   
   Validation: Use checksums and data validation techniques to ensure data consistency post-migration.
   
   Processing: Set up Databricks clusters to process the migrated data using PySpark and leverage Delta Lake for efficient data handling.
   

9. Implementing Incremental Data Processing
   You are tasked with creating a PySpark job that processes only the new data added to a large dataset each day to optimize resource usage. Outline your approach for implementing incremental data processing.
   Ideal Answer (5 Star)
   For incremental data processing in PySpark, I would use watermarking and windowing concepts. By leveraging structured streaming, I would set a watermark to handle late data and define a window for processing. For example: 
   df = df.withWatermark('timestamp', '1 day').groupBy(window('timestamp', '1 day')).agg(sum('value'))
   . Additionally, maintaining a 'last_processed' timestamp in persistent storage allows the job to query only new data each run, using filters like df.filter(df['event_time'] > last_processed_time). This ensures efficient and accurate incremental data processing.

10. Splunk Data Model Acceleration
    You have been asked to accelerate a Splunk data model to improve the performance of Pivot reports. However, you need to ensure that the acceleration does not impact the system's overall performance. How would you approach this task?
    Ideal Answer (5 Star)
    To accelerate a Splunk data model, I would start by evaluating the data model's complexity and the frequency of the Pivot reports that rely on it. I would enable data model acceleration selectively, focusing on the most queried datasets. By setting an appropriate acceleration period that balances freshness with performance, I can minimize resource usage. Monitoring resource utilization and adjusting the acceleration settings as needed would help prevent impacts on overall system performance. Additionally, I would use Splunk's monitoring console to ensure the acceleration process is efficient and to identify any potential performance bottlenecks.
    

11. Using Splunk for Log Correlation and Analysis
    You are tasked with correlating logs from multiple sources (e.g., application logs, database logs, and server logs) to troubleshoot a complex issue impacting application performance. Describe how you would leverage Splunk to perform this task effectively.
    Ideal Answer (5 Star)
    To correlate logs from multiple sources in Splunk, I would first ensure all logs are ingested and indexed properly with consistent timestamps across all sources. I would use field extractions to ensure that common identifiers, such as transaction IDs, are correctly parsed. By utilizing Splunk's 'join' command, I can correlate events from different sources based on these identifiers. Additionally, I would leverage the 'transaction' command to group related events into a single transaction. This helps in visualizing the entire lifecycle of a request across different systems, enabling effective troubleshooting. Lastly, I would create dashboards to visualize patterns and identify anomalies across the correlated logs.

12. PySpark Window Functions
    Write a PySpark code snippet using window functions to calculate a running total of a `sales` column, partitioned by `region` and ordered by `date`. Assume you have a DataFrame with columns `date`, `region`, and `sales`.
    Ideal Answer (5 Star)
    from pyspark.sql import SparkSession
    from pyspark.sql.window import Window
    from pyspark.sql.functions import col, sum
    
    spark = SparkSession.builder.appName("Window Functions").getOrCreate()
    
    # Sample data
    data = [("2023-10-01", "North", 100), ("2023-10-02", "North", 200),
            ("2023-10-01", "South", 150), ("2023-10-02", "South", 250)]
    columns = ["date", "region", "sales"]
    df = spark.createDataFrame(data, columns)
    
    # Define window specification
    window_spec = Window.partitionBy("region").orderBy("date").rowsBetween(Window.unboundedPreceding, Window.currentRow)
    
    # Calculate running total
    running_total_df = df.withColumn("running_total", sum(col("sales")).over(window_spec))
    
    # Show the result
    running_total_df.show()
    

13. Optimizing PySpark Data Pipeline
    You have a PySpark data pipeline that processes large datasets on a nightly basis. Recently, the processing time has increased significantly, impacting downstream applications. Describe how you would identify and resolve the bottlenecks in the pipeline.
    Ideal Answer (5 Star)
    To identify and resolve bottlenecks in a PySpark data pipeline, I would start by utilizing Spark's built-in UI to monitor jobs and stages to pinpoint slow tasks. Common areas to check include data skew, improper shuffling, and inefficient transformations. I would ensure that data is partitioned efficiently, possibly using `repartition` or `coalesce`. Additionally, I would leverage caching strategically to avoid recomputation of the same data. Code example: 
    df = df.repartition(10, 'key_column')
    . I would also review the logical plan using `df.explain()`, and optimize joins using broadcast joins with broadcast(df) where applicable.
    

14. Implementing Data Quality Checks
    You are responsible for ensuring data quality in a Databricks pipeline that processes data from multiple sources. Describe the approach and tools you would use to implement data quality checks.
    Ideal Answer (5 Star)
    To ensure data quality in a Databricks pipeline, I would implement the following approach:
    
    Data Validation: Use PySpark to implement validation checks such as schema validation, null checks, and value range checks.
    
    Delta Lake: Utilize Delta Lake's schema enforcement feature to prevent schema mismatches.
    
    Data Profiling: Use tools like Great Expectations integrated with Databricks to profile data and set expectations for quality checks.
    
    Automated Testing: Implement automated tests for data validation as part of the CI/CD pipeline.
    
    Monitoring and Alerts: Integrate with Splunk to monitor data quality metrics and set up alerts for anomalies.

Data Engineer - Mphasis USA - Nov 19, 2025

See All: Miscellaneous Interviews @ FloCareer

1. Optimizing Spark Jobs in Databricks
   You have a Spark job running in Databricks that processes terabytes of data daily. Recently, the processing time has increased significantly. You need to optimize the job to ensure it runs efficiently. Describe the steps and techniques you would use to diagnose and optimize the job performance.
   Ideal Answer (5 Star)
   To optimize the Spark job in Databricks, I would first use the Spark UI to analyze the job's execution plan and identify any bottlenecks. Key steps include:
   
   Data Skewness: Check for data skewness and repartition the data to ensure even distribution.
   
   Shuffle Partitions: Adjust the number of shuffle partitions based on the job's scale and cluster size.
   
   Cache and Persist: Use caching or persisting for intermediate datasets that are reused multiple times.
   
   Optimize Joins: Ensure that joins use the appropriate join strategy, such as broadcast joins for smaller datasets.
   
   Resource Allocation: Adjust the executor memory and cores based on workload requirements.
   
   Code Optimization: Review and refactor the Spark code to optimize transformations and actions, and use DataFrame/Dataset API for better optimization.
   
   Use Delta Lake: If applicable, use Delta Lake for ACID transactions and faster reads/writes.
   

2. Handling Large-scale Data Migrations
   You are tasked with migrating large datasets from on-premises Hadoop clusters to AWS S3 and processing them in Databricks. Describe the approach you would take to ensure a smooth and efficient migration.
   Ideal Answer (5 Star)
   To handle large-scale data migrations from Hadoop to AWS S3 for processing in Databricks, I would:
   
   Data Transfer: Use AWS Direct Connect or AWS Snowball for efficient data transfer from on-premises to AWS S3.
   
   Data Format: Convert data to an optimized format like Parquet or ORC to reduce storage and increase processing efficiency.
   
   Security: Ensure data is encrypted during transfer and at rest in S3 using AWS KMS.
   
   Incremental Migration: Implement incremental data transfer to minimize downtime and validate data integrity.
   
   Validation: Use checksums and data validation techniques to ensure data consistency post-migration.
   
   Processing: Set up Databricks clusters to process the migrated data using PySpark and leverage Delta Lake for efficient data handling.
   

3. Implementing Data Quality Checks
   You are responsible for ensuring data quality in a Databricks pipeline that processes data from multiple sources. Describe the approach and tools you would use to implement data quality checks.
   Ideal Answer (5 Star)
   To ensure data quality in a Databricks pipeline, I would implement the following approach:
   
   Data Validation: Use PySpark to implement validation checks such as schema validation, null checks, and value range checks.
   
   Delta Lake: Utilize Delta Lake's schema enforcement feature to prevent schema mismatches.
   
   Data Profiling: Use tools like Great Expectations integrated with Databricks to profile data and set expectations for quality checks.
   
   Automated Testing: Implement automated tests for data validation as part of the CI/CD pipeline.
   
   Monitoring and Alerts: Integrate with Splunk to monitor data quality metrics and set up alerts for anomalies.
   

4. Alerting on Anomalies in Data Streams
   As a Data Engineer, you are responsible for setting up alerts in Splunk to detect anomalies in real-time data streams from IoT devices. How would you configure these alerts to minimize false positives while ensuring timely detection of true anomalies?
   Ideal Answer (5 Star)
   To configure alerts on IoT device data streams in Splunk, I would first establish a baseline of normal operating parameters using historical data analysis. This involves identifying key metrics and their usual ranges. I would then set up real-time searches with conditionals that trigger alerts when metrics fall outside these ranges. To minimize false positives, I would incorporate thresholds that account for expected variations and implement a machine learning model, such as a clustering algorithm, to dynamically adjust the thresholds. Additionally, I would set up multi-condition alerts that trigger only when multiple indicators of an anomaly are present.
   

5. Handling Large Joins Efficiently
   You need to perform a join between two large datasets in PySpark. Explain how you would approach this to ensure optimal performance.
   Ideal Answer (5 Star)
   To handle large joins efficiently in PySpark, I would start by checking if one of the datasets is small enough to fit in memory and use a broadcast join with broadcast(small_df). If both are large, I would ensure they are partitioned on the join key using df.repartition('join_key'). Additionally, optimizing join strategies through spark.conf.set('spark.sql.autoBroadcastJoinThreshold', -1) and leveraging sort-merge joins can be beneficial. Using df.explain() to review the physical plan helps in understanding and improving join strategies.
   

6. Disaster Recovery Planning for Data Engineering Solutions
   Your company needs a robust disaster recovery plan for its data engineering solutions built on AWS and Databricks. Outline your strategy for implementing disaster recovery.
   Ideal Answer (5 Star)
   For disaster recovery, start by setting up AWS S3 cross-region replication to ensure data redundancy. Use AWS Backup to automate and manage backups of AWS resources. Implement database snapshots and backups for RDS and Redshift. In Databricks, regularly export critical configurations and notebooks. Use Databricks' REST API to automate the export and import of notebooks and clusters for recovery purposes. Test the disaster recovery plan regularly by simulating failures and ensuring that RTO (Recovery Time Objective) and RPO (Recovery Point Objective) are met. Document the recovery procedures and ensure all team members are trained on the recovery protocols.
   

7. PySpark Window Functions
   Write a PySpark code snippet using window functions to calculate a running total of a `sales` column, partitioned by `region` and ordered by `date`. Assume you have a DataFrame with columns `date`, `region`, and `sales`.
   Ideal Answer (5 Star)
   from pyspark.sql import SparkSession
   from pyspark.sql.window import Window
   from pyspark.sql.functions import col, sum
   
   spark = SparkSession.builder.appName("Window Functions").getOrCreate()
   
   # Sample data
   data = [("2023-10-01", "North", 100), ("2023-10-02", "North", 200),
           ("2023-10-01", "South", 150), ("2023-10-02", "South", 250)]
   columns = ["date", "region", "sales"]
   df = spark.createDataFrame(data, columns)
   
   # Define window specification
   window_spec = Window.partitionBy("region").orderBy("date").rowsBetween(Window.unboundedPreceding, Window.currentRow)
   
   # Calculate running total
   running_total_df = df.withColumn("running_total", sum(col("sales")).over(window_spec))
   
   # Show the result
   running_total_df.show()
   

8. Integrating Splunk for Monitoring AWS and Databricks Infrastructure
   Your company wants to leverage Splunk to monitor AWS and Databricks infrastructure. Describe how you would set up and configure Splunk for this purpose.
   Ideal Answer (5 Star)
   To integrate Splunk for monitoring, first deploy the Splunk Universal Forwarder on AWS EC2 instances to collect logs and metrics. Configure log forwarding from AWS CloudWatch to Splunk using AWS Lambda and Kinesis Firehose. Set up Splunk apps for AWS and Databricks to provide dashboards and analytics for infrastructure monitoring. Use Splunk's Machine Learning Toolkit to analyze trends and anomalies in real-time. Ensure proper access controls and encryption are set up for data sent to Splunk. Regularly update dashboards and alerts to reflect infrastructure changes and track key performance indicators (KPIs).

9. Handling Data Ingestion Spikes in Splunk
   Your organization experiences occasional spikes in data ingestion due to seasonal events. These spikes sometimes lead to delayed indexing and processing in Splunk. How would you manage these spikes to maintain performance and data availability?
   Ideal Answer (5 Star)
   To handle data ingestion spikes in Splunk, I would first ensure that the indexing and search head clusters are appropriately scaled to accommodate peak loads. Implementing load balancing across indexers can help distribute the load more evenly. I'd configure indexer acknowledgment to ensure data persistence and prevent data loss during spikes. Using data retention policies, I can manage storage effectively without impacting performance. Additionally, I would consider implementing a queueing system to manage data bursts and prioritize critical data streams. Monitoring and alerting on queue lengths can also help in preemptively addressing potential bottlenecks.

10. Partitioning Strategies in PySpark
    You have a large dataset that you need to store in a distributed file system, and you want to optimize it for future queries. Explain your approach to partitioning the data using PySpark.
    Ideal Answer (5 Star)
    To optimize a large dataset for future queries using partitioning in PySpark, I would partition the data based on frequently queried columns, using df.write.partitionBy('column_name').parquet('path/to/save'). This technique reduces data scan during query execution. Choosing the right partition column typically involves domain knowledge and query patterns analysis. Additionally, ensuring that partition keys have a balanced distribution of data helps avoid partition skew. The data can also be bucketed with bucketBy(numBuckets, 'column_name') if needed for more efficient joins.

11. Handling Data Security in AWS and Databricks
    Your organization is dealing with sensitive data, and you need to ensure its security across AWS services and Databricks. What are the best practices you would implement to secure data?
    Ideal Answer (5 Star)
    To secure sensitive data, implement encryption at rest and in transit using AWS Key Management Service (KMS) for S3 and other AWS services. Use AWS Identity and Access Management (IAM) to enforce strict access controls, implementing least privilege principles. Enable logging and monitoring with AWS CloudTrail and CloudWatch to track access and modifications to data. In Databricks, use table access controls and secure cluster configurations to restrict data access. Regularly audit permissions and access logs to ensure compliance with security policies. Implement network security best practices like VPCs, security groups, and endpoint policies.

12. Data Cleaning and Transformation
    You are provided with a dataset that contains several missing values and inconsistent data formats. Describe how you would clean and transform this dataset using PySpark.
    Ideal Answer (5 Star)
    To clean and transform a dataset with missing values and inconsistent formats in PySpark, I would first identify null values using df.select([count(when(col(c).isNull(), c)).alias(c) for c in df.columns]). For missing data, I might use df.fillna() for imputation or df.dropna() to remove rows. For inconsistent formats, such as dates, I would use to_date(df['date_column'], 'MM-dd-yyyy') to standardize. Additionally, using regexp_replace() can help clean strings. Finally, I would apply transformations like withColumn() to derive new columns or selectExpr() for SQL-like transformations.

13. Optimizing Splunk Search Performance
    Your team has been experiencing slow search performance in Splunk, especially during peak hours. You are tasked with optimizing the search queries to improve performance without reducing data granularity or the volume of data being processed. What steps would you take to achieve this?
    Ideal Answer (5 Star)
    To optimize Splunk search performance, I would first review the existing search queries for inefficiencies. I would ensure that they are using search time modifiers like 'earliest' and 'latest' to limit the time range being queried. I would also evaluate the use of 'where' versus 'search' commands, as 'search' is generally more efficient. Additionally, I would implement summary indexing for frequently accessed datasets to reduce the need for full data scans. Evaluating and potentially increasing hardware resources during peak hours could also be considered. Finally, I would use Splunk's job inspector to identify slow search components and optimize them accordingly.

14. RATE CANDIDATE'S SKILLS
    Databricks
    AWS
    PySpark
    Splunk
    

Python FASTAPI - Impetus - 4 yoe - Nov 19, 2025

See All: Miscellaneous Interviews @ FloCareer

1. Data Validation with Pydantic
   How does FastAPI leverage Pydantic for data validation, and why is it beneficial?
   
   FastAPI uses Pydantic models for data validation and serialization. Pydantic allows defining data schemas with Python type annotations, and it performs runtime data validation and parsing. For example, you can define a Pydantic model as follows:
   from pydantic import BaseModel
   
   class Item(BaseModel):
       name: str
       price: float
       is_offer: bool = None
   When a request is made, FastAPI automatically validates the request data against this model. This ensures that only correctly structured data reaches your application logic, reducing errors and improving reliability.
           

2. Implementing Workflow Rules in FastAPI
   How would you implement workflow rules in a FastAPI application using a rules engine or library?
   Ideal Answer (5 Star)
   Implementing workflow rules in FastAPI can be done using libraries like Temporal or Azure Logic Apps. Temporal provides a framework for defining complex workflows in code with features like retries and timers. In FastAPI, you can define a workflow as a series of tasks executed in a specific order:
   from temporalio import workflow
   
   @workflow.defn(name='my_workflow')
   class MyWorkflow:
       @workflow.run
       async def run(self, param: str) -> str:
           result = await some_async_task(param)
           return result
   Using a rules engine allows for separation of business logic from application code, improving maintainability and scalability.
           

3. Understanding Python Decorators
   Explain how decorators work in Python. Provide an example of a custom decorator that logs the execution time of a function.
   Ideal Answer (5 Star)
   Python decorators are a powerful tool that allows you to modify the behavior of a function or class method. A decorator is a function that takes another function as an argument and extends or alters its behavior. Decorators are often used for logging, enforcing access control and authentication, instrumentation, and caching.
   
   Here is an example of a custom decorator that logs the execution time of a function:
   
   ```python
   import time
   
   def execution_time_logger(func):
       def wrapper(*args, **kwargs):
           start_time = time.time()
           result = func(*args, **kwargs)
           end_time = time.time()
           print(f"Execution time of {func.__name__}: {end_time - start_time} seconds")
           return result
       return wrapper
   
   @execution_time_logger
   def sample_function():
       time.sleep(2)
       return "Function executed"
   
   sample_function()
   ```
   
   In this example, `execution_time_logger` is a decorator that prints the time taken by `sample_function` to execute. The `wrapper` function inside the decorator captures the start and end times and calculates the execution time, printing the result.
           

4. Designing REST APIs with FastAPI
   How would you design a REST API using FastAPI for a simple online bookstore application? Describe the main components and endpoints you would create.
   Ideal Answer (5 Star)
   To design a REST API for an online bookstore using FastAPI, you would start by identifying the main resources: books, authors, and orders. Each resource would have its own endpoint. For example, '/books' would handle book-related operations. You would use FastAPI's path operation decorators to define routes like GET '/books' to retrieve all books, POST '/books' to add a new book, GET '/books/{id}' to get a specific book, PUT '/books/{id}' to update a book, and DELETE '/books/{id}' to remove a book. FastAPI's automatic data validation using Pydantic models ensures that the request and response bodies are correctly formatted.
           

5. Problem Solving in Software Development
   Discuss a challenging problem you encountered in a Python FastAPI project and how you resolved it. What was your thought process and which tools or techniques did you use?
   Ideal Answer (5 Star)
   In a recent FastAPI project, I encountered a performance bottleneck due to synchronous database queries in a high-traffic endpoint. The solution involved refactoring the code to use asynchronous database operations with `asyncpg` and `SQLAlchemy`. My thought process was to first identify the problematic areas using profiling tools like cProfile and Py-Spy to pinpoint the slowest parts of the application. Once identified, I researched best practices for async programming in Python and implemented changes. I also set up load testing with tools like Locust to verify the improvements. This approach not only resolved the performance issue but also increased the overall responsiveness of the application.
           

6. 
   Testing REST APIs
   Explain how you would test a REST API developed with FastAPI. What tools and strategies would you use?
   Ideal Answer (5 Star)
   Testing a REST API developed with FastAPI involves writing unit and integration tests to ensure the correctness and reliability of the API. You can use the 'pytest' framework for writing tests and 'httpx' for making HTTP requests in the test environment. FastAPI's 'TestClient' allows you to simulate requests to your API endpoints without running a live server. You should test various scenarios, including valid and invalid inputs, to ensure that your API behaves as expected. Mocking external services and databases can help isolate the API logic during testing.
           

7. Asynchronous Background Tasks
   In FastAPI, implement an endpoint that triggers a background task to send an email. Assume the email sending is a mock function that prints to the console.
   Ideal Answer (5 Star)
   from fastapi import FastAPI, BackgroundTasks
   
   app = FastAPI()
   
   def send_email(email: str, message: str):
       print(f"Sending email to {email}: {message}")
   
   @app.post("/send-email/")
   async def send_email_endpoint(email: str, message: str, background_tasks: BackgroundTasks):
       background_tasks.add_task(send_email, email, message)
       return {"message": "Email sending in progress"}
   

8. File Upload Endpoint
   Create an endpoint in FastAPI that allows users to upload files. The files should be saved to a directory on the server.
   Ideal Answer (5 Star)
   from fastapi import FastAPI, File, UploadFile
   import os
   
   app = FastAPI()
   UPLOAD_DIRECTORY = './uploads/'
   os.makedirs(UPLOAD_DIRECTORY, exist_ok=True)
   
   @app.post("/uploadfile/")
   async def create_upload_file(file: UploadFile = File(...)):
       file_location = os.path.join(UPLOAD_DIRECTORY, file.filename)
       with open(file_location, "wb") as f:
           f.write(await file.read())
       return {"info": f"file '{file.filename}' saved at '{UPLOAD_DIRECTORY}'"}
   

9. Asynchronous Programming in FastAPI
   Discuss how FastAPI supports asynchronous programming and the advantages it provides.
   Ideal Answer (5 Star)
   FastAPI supports asynchronous programming natively, utilizing Python's async and await keywords. This allows the server to handle multiple requests simultaneously, making it highly efficient and capable of supporting a large number of concurrent users. Asynchronous programming is especially beneficial for I/O-bound operations, such as database queries and external API calls. For instance, an async function in FastAPI might look like this:
   @app.get('/async-endpoint')
   async def async_endpoint():
       await some_async_io_operation()
       return {"message": "Async operation complete"}
   This non-blocking nature can significantly improve the throughput of web applications.
   

10. RATE CANDIDATE'S SKILLS
    Python
    FAST API
    REST API