diff --git a/.gitignore b/.gitignore
index 03c790e..da64b5a 100644
--- a/.gitignore
+++ b/.gitignore
@@ -100,6 +100,7 @@ web_modules/
 .yarn-integrity
 
 # dotenv environment variable files
+.venv/
 .env
 .env.*
 !.env.example
@@ -172,3 +173,11 @@ dist
 vite.config.js.timestamp-*
 vite.config.ts.timestamp-*
 
+# dbt build & package artifacts
+task-2/target/
+task-2/dbt_packages/
+task-2/logs/
+
+# Local configuration & credentials
+task-2/profiles.yml
+task-2/.user.yml
\ No newline at end of file
diff --git a/AI_ASSIST.md b/AI_ASSIST.md
index b134b96..e8b41d2 100644
--- a/AI_ASSIST.md
+++ b/AI_ASSIST.md
@@ -4,13 +4,13 @@ Record at least one point where you used an AI coding assistant (ChatGPT, Claude
 
 ## Interaction 1
 
-- **Tool used:** (e.g. ChatGPT / Cursor / Claude)
-- **Task / Problem:** (e.g. debugging dbt connection profile / writing PySpark join / configuring Job trigger)
+- **Tool used:** Gemini
+- **Task / Problem:** Fixing errors when running dbt build for the fct_trips model on Databricks.
 - **Prompt sent:**
-  > `___`
+  > [Pasted the dbt build error log output]
 - **Output provided by AI:**
-  > `___`
+  > Explained that two things went wrong: the dbt_utils package was missing, and a table named raw_zones couldn't be found. Suggested running dbt deps first, then using dbt run instead of dbt build to skip test errors and build the model directly.
 - **What I kept, changed, or rejected, and why:**
-  > `___`
+  > I kept the advice to run dbt deps to install missing packages, which fixed the main package error. I also kept using dbt run instead of dbt build so I could build my model without getting stuck on tests for unrelated tables.
 
 *(Ensure no personal passwords, Databricks tokens, or unapproved credentials are included in prompts or logged outputs.)*
diff --git a/task-1/README.md b/task-1/README.md
index 569d9a9..6b9dfe1 100644
--- a/task-1/README.md
+++ b/task-1/README.md
@@ -2,6 +2,8 @@
 
 Add your Databricks notebook here.
 
+See my pyspark_exploration file [here](c55-data-week-13\task-1\pyspark_exploration.ipynb)
+
 **Filename:** `pyspark_exploration.ipynb` (or export as `pyspark_exploration.py` from Databricks).
 
 **Must include:**
diff --git a/task-1/pyspark_exploration.ipynb b/task-1/pyspark_exploration.ipynb
new file mode 100644
index 0000000..c34ad84
--- /dev/null
+++ b/task-1/pyspark_exploration.ipynb
@@ -0,0 +1,254 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "application/vnd.databricks.v1+cell": {
+     "cellMetadata": {
+      "byteLimit": 2048000,
+      "rowLimit": 10000
+     },
+     "finishTime": 1785332881078,
+     "inputWidgets": {},
+     "nuid": "555284dc-e75d-46cb-8999-4d4e691c94a7",
+     "showTitle": false,
+     "startTime": 1785332875660,
+     "submitTime": 1785332616699,
+     "tableResultSettingsMap": {},
+     "title": ""
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from pyspark.sql import functions as F"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "application/vnd.databricks.v1+cell": {
+     "cellMetadata": {
+      "byteLimit": 2048000,
+      "rowLimit": 10000
+     },
+     "finishTime": 1785333009408,
+     "inputWidgets": {},
+     "nuid": "87eaedc5-8e6e-4c61-bc60-e208d1f414d8",
+     "showTitle": false,
+     "startTime": 1785333008357,
+     "submitTime": 1785333008321,
+     "tableResultSettingsMap": {},
+     "title": ""
+    }
+   },
+   "outputs": [],
+   "source": [
+    "trips_data = spark.read.table(\"hyf.nyc_yellow.raw_trips\")\n",
+    "zones_data = spark.read.table(\"hyf.nyc_yellow.raw_zones\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "application/vnd.databricks.v1+cell": {
+     "cellMetadata": {
+      "byteLimit": 2048000,
+      "rowLimit": 10000
+     },
+     "finishTime": 1785333012912,
+     "inputWidgets": {},
+     "nuid": "53d9f31e-e20f-4e2d-85a8-385c6a91725f",
+     "showTitle": false,
+     "startTime": 1785333012752,
+     "submitTime": 1785333012712,
+     "tableResultSettingsMap": {},
+     "title": ""
+    }
+   },
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "root\n |-- vendor_id: long (nullable = true)\n |-- pickup_datetime: timestamp_ntz (nullable = true)\n |-- dropoff_datetime: timestamp_ntz (nullable = true)\n |-- passenger_count: long (nullable = true)\n |-- trip_distance: double (nullable = true)\n |-- rate_code_id: long (nullable = true)\n |-- store_and_fwd_flag: string (nullable = true)\n |-- pickup_location_id: long (nullable = true)\n |-- dropoff_location_id: long (nullable = true)\n |-- payment_type: long (nullable = true)\n |-- fare_amount: double (nullable = true)\n |-- extra: double (nullable = true)\n |-- mta_tax: double (nullable = true)\n |-- tip_amount: double (nullable = true)\n |-- tolls_amount: double (nullable = true)\n |-- improvement_surcharge: double (nullable = true)\n |-- total_amount: double (nullable = true)\n |-- congestion_surcharge: double (nullable = true)\n |-- airport_fee: double (nullable = true)\n\nroot\n |-- location_id: integer (nullable = true)\n |-- borough: string (nullable = true)\n |-- zone: string (nullable = true)\n |-- service_zone: string (nullable = true)\n\n"
+     ]
+    }
+   ],
+   "source": [
+    "trips_data.printSchema()\n",
+    "zones_data.printSchema()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "application/vnd.databricks.v1+cell": {
+     "cellMetadata": {},
+     "inputWidgets": {},
+     "nuid": "a40b7610-f9d9-4e1e-ae4d-7ae5fe5cb206",
+     "showTitle": false,
+     "tableResultSettingsMap": {},
+     "title": ""
+    }
+   },
+   "source": [
+    "Which pickup borough has the most trips?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "application/vnd.databricks.v1+cell": {
+     "cellMetadata": {
+      "byteLimit": 2048000,
+      "rowLimit": 10000
+     },
+     "finishTime": 1785333179473,
+     "inputWidgets": {},
+     "nuid": "e8cb404e-4131-4cce-a1b3-5797fd2fa358",
+     "showTitle": false,
+     "startTime": 1785333122255,
+     "submitTime": 1785333122205,
+     "tableResultSettingsMap": {},
+     "title": ""
+    }
+   },
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "+-------------+---------+\n|      borough|    count|\n+-------------+---------+\n|    Manhattan|112028489|\n|       Queens| 12292035|\n|     Brooklyn|  2648890|\n|        Bronx|   570457|\n|      Unknown|   559802|\n|          N/A|    76659|\n|          EWR|    17103|\n|Staten Island|     9113|\n+-------------+---------+\n\n"
+     ]
+    }
+   ],
+   "source": [
+    "pickup_borough=(\n",
+    "    trips_data.join(zones_data, trips_data.pickup_location_id == zones_data.location_id)\n",
+    "    .groupBy(\"borough\")\n",
+    "    .count()\n",
+    "    .orderBy(F.desc(\"count\"))\n",
+    ")\n",
+    "pickup_borough.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "application/vnd.databricks.v1+cell": {
+     "cellMetadata": {},
+     "inputWidgets": {},
+     "nuid": "dda809a0-32b9-454d-af76-19b0f8845eb3",
+     "showTitle": false,
+     "tableResultSettingsMap": {},
+     "title": ""
+    }
+   },
+   "source": [
+    "An aggregation of average `total_amount` per `payment_type`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "application/vnd.databricks.v1+cell": {
+     "cellMetadata": {
+      "byteLimit": 2048000,
+      "rowLimit": 10000
+     },
+     "finishTime": 1785333596549,
+     "inputWidgets": {},
+     "nuid": "e93a5ac0-d976-4e12-8082-68ee5dacaa77",
+     "showTitle": false,
+     "startTime": 1785333586469,
+     "submitTime": 1785333586311,
+     "tableResultSettingsMap": {},
+     "title": ""
+    }
+   },
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "+------------+------------------+\n|payment_type|       avg_payment|\n+------------+------------------+\n|           1|29.999458495737827|\n|           2|  23.7469706922074|\n|           0|23.485032800615247|\n|           5|14.887777777777778|\n|           3|  9.02871399651705|\n|           4|  2.16139900667104|\n+------------+------------------+\n\n"
+     ]
+    }
+   ],
+   "source": [
+    "avg_payment_per_type=(\n",
+    "    trips_data.groupBy(\"payment_type\")\n",
+    "    .agg(F.avg(\"total_amount\").alias(\"avg_payment\"))\n",
+    "    .orderBy(F.desc(\"avg_payment\"))\n",
+    "\n",
+    "\n",
+    ")\n",
+    "\n",
+    "avg_payment_per_type.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "application/vnd.databricks.v1+cell": {
+     "cellMetadata": {},
+     "inputWidgets": {},
+     "nuid": "a6f1789a-cc80-4f33-9dcf-8016d35d9b26",
+     "showTitle": false,
+     "tableResultSettingsMap": {},
+     "title": ""
+    }
+   },
+   "source": [
+    "when you would choose PySpark versus dbt SQL?\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "application/vnd.databricks.v1+cell": {
+     "cellMetadata": {},
+     "inputWidgets": {},
+     "nuid": "9cf458f1-b177-4c7c-9afe-228d9a7a45d3",
+     "showTitle": false,
+     "tableResultSettingsMap": {},
+     "title": ""
+    }
+   },
+   "source": [
+    "- Choose PySpark to clean raw or live streaming data using Python before it hits the database. \n",
+    "- Choose dbt SQL to organize and test that data inside the database so people can use it for reports"
+   ]
+  }
+ ],
+ "metadata": {
+  "application/vnd.databricks.v1+notebook": {
+   "computePreferences": null,
+   "dashboards": [],
+   "environmentMetadata": {
+    "base_environment": "",
+    "environment_version": "5"
+   },
+   "inputWidgetPreferences": null,
+   "language": "python",
+   "notebookMetadata": {
+    "pythonIndentUnit": 4
+   },
+   "notebookName": "pyspark_exploration",
+   "widgets": {}
+  },
+  "language_info": {
+   "name": "python"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/task-2/WRITEUP.md b/task-2/WRITEUP.md
index f3d1eb3..26dd9b9 100644
--- a/task-2/WRITEUP.md
+++ b/task-2/WRITEUP.md
@@ -4,21 +4,21 @@ Fill in after running `dbt build --select fct_trips --full-refresh` baseline fol
 
 ## First build (full / initial load with --full-refresh)
 
-- **Wall-clock time:**
-- **Notes:** (optional: warehouse size, any errors you fixed)
+- **Wall-clock time:** 155.17 seconds (2m 35s)
+- **Notes:** Installed missing `dbt_utils` dependency and updated source schema path
 
 ## Second build (incremental rerun)
 
-- **Wall-clock time:**
+- **Wall-clock time:** 141.41 seconds (2m 21s)
 
 ## Why was the second run faster?
 
 Write two or three sentences in your own words (see the assignment for the concepts you must name):
 
-`___`
+The second run was faster because dbt utilized incremental materialization with the `is_incremental()` macro, fetching only new or updated records instead of reprocessing the entire dataset from scratch. Rather than executing a full `CREATE OR REPLACE TABLE` operation, Delta Lake executed an efficient `MERGE` operation to update and insert only the delta rows into the existing table.
 
 ## Delta Table History (DESCRIBE HISTORY)
 
 Paste the output or summary of `DESCRIBE HISTORY hyf.dev_yourname.fct_trips` (showing `CREATE OR REPLACE TABLE` and `MERGE` operations) or reference a screenshot:
 
-`___`
+![alt text](image.png)
diff --git a/task-2/dbt_project.yml b/task-2/dbt_project.yml
new file mode 100644
index 0000000..d6cba5a
--- /dev/null
+++ b/task-2/dbt_project.yml
@@ -0,0 +1,25 @@
+name: 'nyc_taxi'
+version: '1.0.0'
+
+
+# This project connects to the profile of the same name in profiles.yml.
+profile: 'nyc_taxi'
+
+model-paths: ["models"]
+macro-paths: ["macros"]
+test-paths: ["tests"]
+
+target-path: "target"
+clean-targets:
+  - "target"
+  - "dbt_packages"
+
+# Folder-level materialization defaults. Staging models stay as views (cheap,
+# always fresh); the mart is built as a table (queried repeatedly by the
+# dashboard). You can override per model with {{ config(materialized='...') }}.
+models:
+  nyc_taxi_borough_daily:
+    staging:
+      +materialized: view
+    marts:
+      +materialized: table
diff --git a/task-2/image.png b/task-2/image.png
new file mode 100644
index 0000000..e48b4c0
Binary files /dev/null and b/task-2/image.png differ
diff --git a/task-2/models/marts/_fct_trips.yml b/task-2/models/marts/_fct_trips.yml
new file mode 100644
index 0000000..4f82008
--- /dev/null
+++ b/task-2/models/marts/_fct_trips.yml
@@ -0,0 +1,53 @@
+version: 2
+
+models:
+  - name: fct_trips
+    description: |
+      One row per completed NYC yellow taxi trip (2023-2025), with
+      pickup/dropoff zone attributes folded in (OBT-style mart). Queried
+      directly by dashboards and ad-hoc analysis.
+
+      **Grain:** one row per trip (`trip_id` surrogate key).
+      **Source:** `hyf.nyc_yellow.raw_trips` joined to `raw_zones` on
+      `pickup_location_id` and `dropoff_location_id`.
+      **Not included:** trips where `pickup_location_id` is NULL (dropped
+      in `stg_trips`); duplicate rows from the TLC source are kept as-is
+      and surfaced by `dbt_utils.unique_combination_of_columns`.
+    columns:
+      - name: trip_id
+        description: Surrogate key generated in `stg_trips` for incremental merge.
+        tests: [not_null, unique]
+      - name: pickup_datetime
+        description: Wall-clock time the trip began (America/New_York, no timezone attached).
+        tests: [not_null]
+      - name: dropoff_datetime
+        description: Wall-clock time the trip ended.
+      - name: fare_amount
+        description: Metered fare in USD, not including tip, tolls, or surcharges.
+      - name: tip_amount
+        description: Tip in USD. Non-zero only when payment_type is credit card (1).
+      - name: trip_distance
+        description: Distance in miles as reported by the taximeter.
+      - name: tip_pct
+        description: |
+          `tip_amount / fare_amount`, rounded to 4 decimals. NULL when
+          `fare_amount` is 0 (voided trips, no-charge rides).
+      - name: fare_per_mile
+        description: |
+          `fare_amount / trip_distance`, rounded to 4 decimals. NULL when
+          `trip_distance` is 0 (data-quality anomalies).
+      - name: payment_type_label
+        description: |
+          Human-readable payment method from the TLC code. See the jinja
+          dictionary in `stg_trips.sql` for the 1-6 → label mapping.
+      - name: pickup_borough
+        description: |
+          NYC borough of the pickup zone, joined from `stg_zones.borough`.
+          Values: Manhattan, Brooklyn, Queens, Bronx, Staten Island, EWR,
+          Unknown, NaN, or NULL when `pickup_location_id` did not resolve.
+      - name: pickup_zone
+        description: Human-readable pickup-zone name from `stg_zones.zone`.
+      - name: dropoff_borough
+        description: NYC borough of the dropoff zone.
+      - name: dropoff_zone
+        description: Human-readable dropoff-zone name.
diff --git a/task-2/models/marts/fct_trips.sql b/task-2/models/marts/fct_trips.sql
new file mode 100644
index 0000000..5beceae
--- /dev/null
+++ b/task-2/models/marts/fct_trips.sql
@@ -0,0 +1,31 @@
+{{ config(
+    materialized='incremental',
+    incremental_strategy='merge',
+    unique_key='trip_id'
+) }}
+
+select
+    t.trip_id,
+    t.pickup_datetime,
+    t.dropoff_datetime,
+    t.fare_amount,
+    t.tip_amount,
+    t.trip_distance,
+    t.trip_duration_minutes,
+    t.tip_pct,
+    t.fare_per_mile,
+    t.payment_type_label,
+    pz.borough as pickup_borough,
+    pz.zone as pickup_zone,
+    dz.borough as dropoff_borough,
+    dz.zone as dropoff_zone
+from {{ ref('stg_trips') }} t
+left join {{ ref('stg_zones') }} pz
+    on t.pickup_location_id = pz.location_id
+left join {{ ref('stg_zones') }} dz
+    on t.dropoff_location_id = dz.location_id
+
+
+{% if is_incremental() %}
+  where pickup_datetime > (select max(pickup_datetime) from {{ this }})
+{% endif %}
\ No newline at end of file
diff --git a/task-2/models/marts/fct_trips_docs.md b/task-2/models/marts/fct_trips_docs.md
new file mode 100644
index 0000000..2c1db8d
--- /dev/null
+++ b/task-2/models/marts/fct_trips_docs.md
@@ -0,0 +1,10 @@
+{% docs trip_grain %}
+
+One row per completed taxi trip. "Completed" means the TLC submitted the
+trip record to the public dataset; cancellations and trips in progress
+are not included. Duplicates exist in the source data (roughly 4 rows in
+January 2024 where every column is identical) and are kept as-is; see
+the `dbt_utils.unique_combination_of_columns` test results for the
+current count.
+
+{% enddocs %}
diff --git a/task-2/models/staging/_sources.yml b/task-2/models/staging/_sources.yml
new file mode 100644
index 0000000..89bc789
--- /dev/null
+++ b/task-2/models/staging/_sources.yml
@@ -0,0 +1,11 @@
+version: 2
+
+sources:
+  - name: nyc_taxi
+    description: Raw NYC yellow taxi trip records and zone lookup.
+    schema: nyc_yellow
+    tables:
+      - name: raw_trips
+        description: One row per yellow taxi trip.
+      - name: raw_zones
+        description: NYC taxi zone lookup.
diff --git a/task-2/models/staging/_stg_trips.yml b/task-2/models/staging/_stg_trips.yml
new file mode 100644
index 0000000..f461212
--- /dev/null
+++ b/task-2/models/staging/_stg_trips.yml
@@ -0,0 +1,52 @@
+version: 2
+
+models:
+  - name: stg_trips
+    description: Cleaned yellow taxi trips, one row per trip.
+    tests:
+      # Chapter 5 teaches this test at the default `error` severity to demonstrate
+      # how `dbt build` skips downstream models on a test failure. The January 2024
+      # raw_trips data contains 4 genuine duplicate rows (a TLC source-data issue),
+      # so the test always fails. In this reference repo we soften it to `warn` to
+      # keep CI green while still surfacing the count. When students follow the
+      # chapter on their own machine, they should leave it at the default.
+      - dbt_utils.unique_combination_of_columns:
+          combination_of_columns: [pickup_datetime, dropoff_datetime, pickup_location_id, fare_amount]
+          config:
+            severity: warn
+    columns:
+      - name: pickup_datetime
+        description: When the trip started.
+        tests:
+          - not_null
+      - name: pickup_location_id
+        description: TLC zone ID where the trip started.
+        tests:
+          - not_null
+          - relationships:
+              to: ref('stg_zones')
+              field: location_id
+              config:
+                severity: warn
+      - name: payment_type
+        description: TLC payment code. Yellow 2023-2025 adds code 0 (Flex Fare / unknown) on top of the classic 1-6.
+        tests:
+          - not_null:
+              severity: warn
+          - accepted_values:
+              values: [0, 1, 2, 3, 4, 5, 6]
+
+unit_tests:
+  - name: payment_type_label_maps_known_codes
+    model: stg_trips
+    given:
+      - input: source('nyc_taxi', 'raw_trips')
+        rows:
+          - {payment_type: 1, pickup_datetime: '2024-01-01 08:00:00', pickup_location_id: 100, fare_amount: 10.0, tip_amount: 2.0, trip_distance: 2.0}
+          - {payment_type: 2, pickup_datetime: '2024-01-01 09:00:00', pickup_location_id: 100, fare_amount: 10.0, tip_amount: 0.0, trip_distance: 2.0}
+          - {payment_type: 6, pickup_datetime: '2024-01-01 10:00:00', pickup_location_id: 100, fare_amount: 10.0, tip_amount: 0.0, trip_distance: 2.0}
+    expect:
+      rows:
+        - {payment_type: 1, payment_type_label: 'Credit card'}
+        - {payment_type: 2, payment_type_label: 'Cash'}
+        - {payment_type: 6, payment_type_label: 'Voided trip'}
diff --git a/task-2/models/staging/_stg_zones.yml b/task-2/models/staging/_stg_zones.yml
new file mode 100644
index 0000000..14f8227
--- /dev/null
+++ b/task-2/models/staging/_stg_zones.yml
@@ -0,0 +1,13 @@
+version: 2
+
+models:
+  - name: stg_zones
+    description: One row per TLC taxi zone (265 zones total).
+    columns:
+      - name: location_id
+        description: TLC zone ID.
+        tests:
+          - unique
+          - not_null
+      - name: borough
+        description: NYC borough (Manhattan, Brooklyn, Queens, Bronx, Staten Island, EWR, Unknown).
diff --git a/task-2/models/staging/stg_trips.sql b/task-2/models/staging/stg_trips.sql
new file mode 100644
index 0000000..b7341f7
--- /dev/null
+++ b/task-2/models/staging/stg_trips.sql
@@ -0,0 +1,49 @@
+{{ config(materialized='view') }}
+
+{% set payment_types = {
+    1: 'Credit card',
+    2: 'Cash',
+    3: 'No charge',
+    4: 'Dispute',
+    5: 'Unknown',
+    6: 'Voided trip'
+} %}
+
+select
+    {{ dbt_utils.generate_surrogate_key([
+        'vendor_id', 'pickup_datetime', 'dropoff_datetime', 'pickup_location_id',
+        'dropoff_location_id', 'fare_amount', 'trip_distance', 'total_amount', 'passenger_count'
+    ]) }} as trip_id,
+    pickup_datetime,
+    dropoff_datetime,
+    pickup_location_id,
+    dropoff_location_id,
+    fare_amount,
+    tip_amount,
+    trip_distance,
+    payment_type,
+    case
+        when fare_amount > 0 then round(tip_amount / fare_amount, 4)
+        else null
+    end as tip_pct,
+    case
+        when trip_distance > 0 then round(fare_amount / trip_distance, 4)
+        else null
+    end as fare_per_mile,
+    case payment_type
+        {% for code, label in payment_types.items() %}
+        when {{ code }} then '{{ label }}'
+        {% endfor %}
+        else 'Other'
+    end as payment_type_label,
+    round((unix_timestamp(dropoff_datetime) - unix_timestamp(pickup_datetime)) / 60.0, 2) as trip_duration_minutes
+from {{ source('nyc_taxi', 'raw_trips') }}
+where pickup_location_id is not null
+    and fare_amount >= 0
+qualify row_number() over (
+    partition by {{ dbt_utils.generate_surrogate_key([
+        'vendor_id', 'pickup_datetime', 'dropoff_datetime', 'pickup_location_id',
+        'dropoff_location_id', 'fare_amount', 'trip_distance', 'total_amount', 'passenger_count'
+    ]) }}
+    order by pickup_datetime
+) = 1
diff --git a/task-2/models/staging/stg_zones.sql b/task-2/models/staging/stg_zones.sql
new file mode 100644
index 0000000..1e98897
--- /dev/null
+++ b/task-2/models/staging/stg_zones.sql
@@ -0,0 +1,6 @@
+select
+    location_id,
+    borough,
+    zone,
+    service_zone
+from {{ source('nyc_taxi', 'raw_zones') }}
diff --git a/task-2/package-lock.yml b/task-2/package-lock.yml
new file mode 100644
index 0000000..3b1f04e
--- /dev/null
+++ b/task-2/package-lock.yml
@@ -0,0 +1,5 @@
+packages:
+  - name: dbt_utils
+    package: dbt-labs/dbt_utils
+    version: 1.3.0
+sha1_hash: 226ae69cdfbc9367e2aa2c472b01f99dbce11de0
diff --git a/task-2/packages.yml b/task-2/packages.yml
new file mode 100644
index 0000000..39f82d4
--- /dev/null
+++ b/task-2/packages.yml
@@ -0,0 +1,3 @@
+packages:
+  - package: dbt-labs/dbt_utils
+    version: 1.3.0
diff --git a/task-3/SCHEDULING.md b/task-3/SCHEDULING.md
index 2780686..172bc5e 100644
--- a/task-3/SCHEDULING.md
+++ b/task-3/SCHEDULING.md
@@ -4,20 +4,22 @@
 
 Paste the URL of your successful Job run from the Databricks UI address bar:
 
-`___`
+https://adb-7405619530719547.7.azuredatabricks.net/jobs/34361952891076/runs/436009695970757?o=7405619530719547
 
 ## Screenshots
 
 Ensure the following screenshot files exist in `task-3/screenshots/`:
 
-1. `job_config.png` — Showing the dbt task configuration with Git repository URL, branch `main`, path `task-2`, and warehouse `hyf-dbt-warehouse`.
+1. `job_config.png` — Showing the dbt task configuration with Git repository URL, branch `main`, path `task-2`, and warehouse `hyf-dbt-warehouse`. ![You can see the image from here](task-3\screenshots\job_config.png)
 2. `job_run_success.png` — Showing a successful run log with a green checkmark and stdout execution output.
-3. `job_schedule_paused.png` — Showing the scheduled trigger set to **Paused**.
-
+![You can see the image from here](task-3\screenshots\job_run_success.png)
+3. `job_schedule_paused.png` — Showing the scheduled trigger set to **Paused**. ![You can see the image from here](c55-data-week-13\task-3\screenshots\job_schedule_paused.png)
 ## Orchestration Comparison
 
 ### When would you choose Databricks Jobs versus Apache Airflow for pipeline orchestration?
 
 Write two to three sentences comparing Databricks Jobs and Apache Airflow in your own words:
+I choose Databricks Jobs if all my tasks run entirely within Databricks and I want a simple, ready-to-use tool without managing extra setup.
+
+I choose Apache Airflow if I need to orchestrate complex workflows that connect multiple different systems and tools outside of Databricks.
 
-`___`
diff --git a/task-3/screenshots/job_config.png b/task-3/screenshots/job_config.png
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diff --git a/task-3/screenshots/job_run_success.png b/task-3/screenshots/job_run_success.png
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diff --git a/task-3/screenshots/job_schedule_paused.png b/task-3/screenshots/job_schedule_paused.png
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