diff --git a/questions/186_one-sample-z-test-hypothesis-testing/description.md b/questions/186_one-sample-z-test-hypothesis-testing/description.md
new file mode 100644
index 00000000..754111c7
--- /dev/null
+++ b/questions/186_one-sample-z-test-hypothesis-testing/description.md
@@ -0,0 +1,18 @@
+Implement a function to perform a one-sample Z-test for a population mean when the population standard deviation is known. Your function must support both one-tailed and two-tailed alternatives.
+
+Implement a function with the signature:
+- one_sample_z_test(sample_mean, population_mean, population_std, n, alternative="two-sided")
+
+Where:
+- sample_mean: The observed sample mean (float)
+- population_mean: The hypothesized population mean under H0 (float)
+- population_std: The known population standard deviation (float > 0)
+- n: Sample size (int > 0)
+- alternative: One of {"two-sided", "greater", "less"}
+
+Return a dictionary with:
+- "z": the computed Z statistic rounded to 4 decimals
+- "p_value": the corresponding p-value rounded to 4 decimals
+
+Use the standard normal distribution for the p-value. Handle invalid inputs minimally by assuming valid types and values.
+
diff --git a/questions/186_one-sample-z-test-hypothesis-testing/example.json b/questions/186_one-sample-z-test-hypothesis-testing/example.json
new file mode 100644
index 00000000..d6f0d1a2
--- /dev/null
+++ b/questions/186_one-sample-z-test-hypothesis-testing/example.json
@@ -0,0 +1,6 @@
+{
+  "input": "sample_mean=103.0, population_mean=100.0, population_std=15.0, n=36, alternative='greater'",
+  "output": "{'z': 1.2, 'p_value': 0.1151}",
+  "reasoning": "Standard error = 15/sqrt(36)=2.5. Z=(103-100)/2.5=1.2. For a 'greater' test, p=1-CDF(1.2)=0.1151."
+}
+
diff --git a/questions/186_one-sample-z-test-hypothesis-testing/learn.md b/questions/186_one-sample-z-test-hypothesis-testing/learn.md
new file mode 100644
index 00000000..d2dfddc8
--- /dev/null
+++ b/questions/186_one-sample-z-test-hypothesis-testing/learn.md
@@ -0,0 +1,20 @@
+A one-sample Z-test assesses whether the mean of a population differs from a hypothesized value when the population standard deviation is known. It is appropriate for large samples (by CLT) or when normality is assumed and the population standard deviation is known.
+
+Test statistic:
+- z = (x̄ − μ0) / (σ / √n)
+  - x̄: sample mean
+  - μ0: hypothesized mean under H0
+  - σ: known population standard deviation
+  - n: sample size
+
+P-value computation uses the standard normal distribution:
+- Two-sided (H1: μ ≠ μ0): p = 2 · min(Φ(z), 1 − Φ(z))
+- Right-tailed (H1: μ > μ0): p = 1 − Φ(z)
+- Left-tailed (H1: μ < μ0): p = Φ(z)
+
+Decision at level α:
+- Reject H0 if p ≤ α; otherwise, fail to reject H0.
+
+Notes:
+- If σ is unknown, use a one-sample t-test with the sample standard deviation instead.
+
diff --git a/questions/187_mlops-etl-pipeline/meta.json b/questions/186_one-sample-z-test-hypothesis-testing/meta.json
similarity index 54%
rename from questions/187_mlops-etl-pipeline/meta.json
rename to questions/186_one-sample-z-test-hypothesis-testing/meta.json
index d31f85bb..1ea1e7f5 100644
--- a/questions/187_mlops-etl-pipeline/meta.json
+++ b/questions/186_one-sample-z-test-hypothesis-testing/meta.json
@@ -1,8 +1,8 @@
 {
-  "id": "187",
-  "title": "Build a Simple ETL Pipeline (MLOps)",
-  "difficulty": "medium",
-  "category": "MLOps",
+  "id": "186",
+  "title": "One-Sample Z-Test for Mean (One and Two-Tailed)",
+  "difficulty": "easy",
+  "category": "Statistics",
   "video": "",
   "likes": "0",
   "dislikes": "0",
@@ -10,3 +10,4 @@
     { "profile_link": "https://github.com/Jeet009", "name": "Jeet Mukherjee" }
   ]
 }
+
diff --git a/questions/186_one-sample-z-test-hypothesis-testing/solution.py b/questions/186_one-sample-z-test-hypothesis-testing/solution.py
new file mode 100644
index 00000000..7019e23e
--- /dev/null
+++ b/questions/186_one-sample-z-test-hypothesis-testing/solution.py
@@ -0,0 +1,40 @@
+from math import erf, sqrt
+
+def _standard_normal_cdf(x):
+    return 0.5 * (1.0 + erf(x / sqrt(2.0)))
+
+def one_sample_z_test(sample_mean, population_mean, population_std, n, alternative="two-sided"):
+    """
+    Perform a one-sample Z-test for a population mean with known population std.
+
+    Parameters
+    ----------
+    sample_mean : float
+    population_mean : float
+    population_std : float
+    n : int
+    alternative : str
+        One of {"two-sided", "greater", "less"}
+
+    Returns
+    -------
+    dict with keys:
+      - "z": Z-statistic rounded to 4 decimals
+      - "p_value": p-value rounded to 4 decimals
+    """
+    standard_error = population_std / sqrt(n)
+    z = (sample_mean - population_mean) / standard_error
+    cdf = _standard_normal_cdf(z)
+
+    if alternative == "two-sided":
+        p = 2.0 * min(cdf, 1.0 - cdf)
+    elif alternative == "greater":
+        p = 1.0 - cdf
+    elif alternative == "less":
+        p = cdf
+    else:
+        # Fallback to two-sided if unexpected input
+        p = 2.0 * min(cdf, 1.0 - cdf)
+
+    return {"z": round(z, 4), "p_value": round(p, 4)}
+
diff --git a/questions/186_one-sample-z-test-hypothesis-testing/starter_code.py b/questions/186_one-sample-z-test-hypothesis-testing/starter_code.py
new file mode 100644
index 00000000..66dd39e1
--- /dev/null
+++ b/questions/186_one-sample-z-test-hypothesis-testing/starter_code.py
@@ -0,0 +1,33 @@
+from math import erf, sqrt
+
+def _standard_normal_cdf(x):
+    return 0.5 * (1.0 + erf(x / sqrt(2.0)))
+
+def one_sample_z_test(sample_mean, population_mean, population_std, n, alternative="two-sided"):
+    """
+    Perform a one-sample Z-test for a population mean with known population std.
+
+    Parameters
+    ----------
+    sample_mean : float
+    population_mean : float
+    population_std : float
+    n : int
+    alternative : str
+        One of {"two-sided", "greater", "less"}
+
+    Returns
+    -------
+    dict with keys:
+      - "z": Z-statistic rounded to 4 decimals
+      - "p_value": p-value rounded to 4 decimals
+    """
+    # TODO: Implement the Z statistic and p-value computation
+    # z = (sample_mean - population_mean) / (population_std / sqrt(n))
+    # Use _standard_normal_cdf for CDF of standard normal.
+    # For alternative:
+    # - "two-sided": p = 2 * min(P(Z<=z), P(Z>=z)) = 2 * min(cdf(z), 1-cdf(z))
+    # - "greater":   p = 1 - cdf(z)
+    # - "less":      p = cdf(z)
+    return {"z": 0.0, "p_value": 1.0}
+
diff --git a/questions/186_one-sample-z-test-hypothesis-testing/tests.json b/questions/186_one-sample-z-test-hypothesis-testing/tests.json
new file mode 100644
index 00000000..b1eac6b4
--- /dev/null
+++ b/questions/186_one-sample-z-test-hypothesis-testing/tests.json
@@ -0,0 +1,27 @@
+[
+  {
+    "test": "one_sample_z_test(103.0, 100.0, 15.0, 36, alternative='two-sided')",
+    "expected_output": "{'z': 1.2, 'p_value': 0.2296}"
+  },
+  {
+    "test": "one_sample_z_test(103.0, 100.0, 15.0, 36, alternative='greater')",
+    "expected_output": "{'z': 1.2, 'p_value': 0.1151}"
+  },
+  {
+    "test": "one_sample_z_test(103.0, 100.0, 15.0, 36, alternative='less')",
+    "expected_output": "{'z': 1.2, 'p_value': 0.8849}"
+  },
+  {
+    "test": "one_sample_z_test(97.0, 100.0, 10.0, 25, alternative='two-sided')",
+    "expected_output": "{'z': -1.5, 'p_value': 0.1336}"
+  },
+  {
+    "test": "one_sample_z_test(97.0, 100.0, 10.0, 25, alternative='less')",
+    "expected_output": "{'z': -1.5, 'p_value': 0.0668}"
+  },
+  {
+    "test": "one_sample_z_test(97.0, 100.0, 10.0, 25, alternative='greater')",
+    "expected_output": "{'z': -1.5, 'p_value': 0.9332}"
+  }
+]
+
diff --git a/questions/187_mlops-etl-pipeline/description.md b/questions/187_mlops-etl-pipeline/description.md
deleted file mode 100644
index 5adfa2df..00000000
--- a/questions/187_mlops-etl-pipeline/description.md
+++ /dev/null
@@ -1,14 +0,0 @@
-## Problem
-
-Implement a simple ETL (Extract-Transform-Load) pipeline for model-ready data preparation.
-
-Given a CSV-like string containing user events with columns: `user_id,event_type,value` (header included), write a function `run_etl(csv_text)` that:
-
-1. Extracts rows from the raw CSV text.
-2. Transforms data by:
-	- Filtering only rows where `event_type == "purchase"`.
-	- Converting `value` to float and dropping invalid rows.
-	- Aggregating total purchase `value` per `user_id`.
-3. Loads the transformed results by returning a list of `(user_id, total_value)` tuples sorted by `user_id` ascending.
-
-Assume small inputs (no external libs), handle extra whitespace, and ignore blank lines.
diff --git a/questions/187_mlops-etl-pipeline/example.json b/questions/187_mlops-etl-pipeline/example.json
deleted file mode 100644
index 84952417..00000000
--- a/questions/187_mlops-etl-pipeline/example.json
+++ /dev/null
@@ -1,5 +0,0 @@
-{
-  "input": "run_etl(\"user_id,event_type,value\\n u1, purchase, 10.0\\n u2, view, 1.0\\n u1, purchase, 5\\n u3, purchase, not_a_number\\n u2, purchase, 3.5 \\n\\n\")",
-  "output": "[('u1', 15.0), ('u2', 3.5)]",
-  "reasoning": "Keep only purchases; convert values; drop invalid; aggregate per user; sort by user_id."
-}
diff --git a/questions/187_mlops-etl-pipeline/learn.md b/questions/187_mlops-etl-pipeline/learn.md
deleted file mode 100644
index d523e6a1..00000000
--- a/questions/187_mlops-etl-pipeline/learn.md
+++ /dev/null
@@ -1,24 +0,0 @@
-## Solution Explanation
-
-This task mirrors a minimal MLOps ETL flow that prepares data for downstream modeling.
-
-### ETL breakdown
-- Extract: parse raw CSV text, ignore blanks, and split into header and rows.
-- Transform:
-	- Filter only relevant records (event_type == "purchase").
-	- Cast `value` to float; discard invalid rows to maintain data quality.
-	- Aggregate total purchase value per user to create compact features.
-- Load: return a deterministic, sorted list of `(user_id, total_value)`.
-
-### Why this design?
-- Input sanitation prevents runtime errors and poor-quality features.
-- Aggregation compresses event-level logs into user-level features commonly used in models.
-- Sorting produces stable, testable outputs.
-
-### Complexity
-- For N rows, parsing and aggregation run in O(N); sorting unique users U costs O(U log U).
-
-### Extensions
-- Add schema validation and logging.
-- Write outputs to files or databases.
-- Schedule ETL runs and add monitoring for drift and freshness.
diff --git a/questions/187_mlops-etl-pipeline/solution.py b/questions/187_mlops-etl-pipeline/solution.py
deleted file mode 100644
index 19b9a275..00000000
--- a/questions/187_mlops-etl-pipeline/solution.py
+++ /dev/null
@@ -1,43 +0,0 @@
-from typing import List, Tuple
-
-
-def run_etl(csv_text: str) -> List[Tuple[str, float]]:
-	"""Reference ETL implementation.
-
-	- Extract: parse CSV text, skip header, strip whitespace, ignore blanks
-	- Transform: keep event_type == "purchase"; parse value as float; aggregate per user
-	- Load: return sorted list of (user_id, total_value) by user_id asc
-	"""
-	lines = [line.strip() for line in csv_text.splitlines() if line.strip()]
-	if not lines:
-		return []
-	# header
-	header = lines[0]
-	rows = lines[1:]
-
-	# indices from header (allow varying order and case)
-	headers = [h.strip().lower() for h in header.split(",")]
-	try:
-		idx_user = headers.index("user_id")
-		idx_event = headers.index("event_type")
-		idx_value = headers.index("value")
-	except ValueError:
-		# header missing required columns
-		return []
-
-	aggregates: dict[str, float] = {}
-	for row in rows:
-		parts = [c.strip() for c in row.split(",")]
-		if len(parts) <= max(idx_user, idx_event, idx_value):
-			continue
-		user_id = parts[idx_user]
-		event_type = parts[idx_event].lower()
-		if event_type != "purchase":
-			continue
-		try:
-			value = float(parts[idx_value])
-		except ValueError:
-			continue
-		aggregates[user_id] = aggregates.get(user_id, 0.0) + value
-
-	return sorted(aggregates.items(), key=lambda kv: kv[0])
diff --git a/questions/187_mlops-etl-pipeline/starter_code.py b/questions/187_mlops-etl-pipeline/starter_code.py
deleted file mode 100644
index 65002026..00000000
--- a/questions/187_mlops-etl-pipeline/starter_code.py
+++ /dev/null
@@ -1,9 +0,0 @@
-# Implement your function below.
-
-def run_etl(csv_text: str) -> list[tuple[str, float]]:
-	"""Run a simple ETL pipeline over CSV text with header user_id,event_type,value.
-
-	Returns a sorted list of (user_id, total_value) for event_type == "purchase".
-	"""
-	# TODO: implement extract, transform, and load steps
-	raise NotImplementedError
diff --git a/questions/187_mlops-etl-pipeline/tests.json b/questions/187_mlops-etl-pipeline/tests.json
deleted file mode 100644
index 781c1b28..00000000
--- a/questions/187_mlops-etl-pipeline/tests.json
+++ /dev/null
@@ -1,14 +0,0 @@
-[
-  {
-    "test": "print(run_etl('user_id,event_type,value\\n u1, purchase, 10.0\\n u2, view, 1.0\\n u1, purchase, 5\\n u3, purchase, not_a_number\\n u2, purchase, 3.5 \\n'))",
-    "expected_output": "[('u1', 15.0), ('u2', 3.5)]"
-  },
-  {
-    "test": "print(run_etl('user_id,event_type,value'))",
-    "expected_output": "[]"
-  },
-  {
-    "test": "print(run_etl('value,event_type,user_id\\n 1.0, purchase, u1\\n 2.0, purchase, u1\\n'))",
-    "expected_output": "[('u1', 3.0)]"
-  }
-]