Geometry Quiz: Zero And Identity Matrices And Determinants
Practice Zero And Identity Matrices And Determinants in Geometry with focused quiz questions that help you check what you know, review explanations, and build confidence with test-style prompts.
What this quiz covers
This quiz focuses on Zero And Identity Matrices And Determinants, giving you a quick way to practice the rules, question types, and explanations that matter most for Geometry.
How to use this quiz
Try each quiz question before looking at the correct answer. Use the explanations to review missed ideas, then come back to similar questions until the pattern feels familiar.
Question 1
A linear transformation is represented by the matrix A=(01−10). A unit square has vertices (0,0), (1,0), (1,1), and (0,1). Which claim about area scaling is correct?
The area becomes 0 because the matrix has a zero entry.
The area is multiplied by −1, so the square’s area becomes negative.
The area is multiplied by 1 because .
Explanation: Matrix interpretation geometrically includes rotations that preserve areas but change orientations based on det sign. Identity preserves, zero collapses. Determinant absolute value scales areas, here 1 meaning no change in size. A rotates the unit square 90 degrees, keeping area 1. Justified by ∣det(A)∣= and matrix form for rotation. Misconception: area becomes negative from sign, but areas are positive, using absolute value. Read determinant as area change, preserving it.
Question 2
A linear transformation is represented by the matrix Z=(00 A point is transformed to . Which statement describes the geometric effect?
Question 3
A linear transformation T has matrix M=(10 Which conclusion follows from the determinant value?
Question 4
A linear transformation is represented by D=(−10 The unit square is shown on the coordinate plane. Which claim about area scaling is correct?
Question 5
A linear transformation T has matrix M=(10 Which reasoning correctly interprets the matrix?
Question 6
A matrix A sends the basis vectors e and to the points shown on the coordinate plane. Which statement describes the geometric effect of on area?
Question 7
A linear transformation T in the plane is represented by the matrix A=(20 A rectangle in the coordinate plane has one corner at the origin and adjacent sides along the positive axes, with vertices , , , and . Which claim about area scaling is correct?
Question 8
A linear transformation T is defined by the matrix M=(00 Which transformation does the matrix represent?
Question 9
A transformation is represented by the matrix A=(200. Which claim about area scaling is correct for a square region in the plane?
Question 10
A transformation is given by A=(400 Which statement describes the geometric effect on the unit square (area 1)?
Question 11
A linear transformation is represented by G=(111 The parallelogram formed by the vectors and (the unit square) is shown on the coordinate plane. Which conclusion follows from the determinant value?
Question 12
A student claims that the matrix A=(000 “acts like the identity because it doesn’t rotate anything.” Which interpretation is NOT justified?
Question 13
A transformation T is defined by M=(00 On the coordinate plane, segment has endpoints and . Which statement describes the geometric effect the matrix produces on ?
Question 14
A linear transformation is represented by A=(300. Which statement describes the geometric effect of on the unit square?
Question 15
A linear transformation T has matrix M=(12 Which claim about area scaling is correct for any nondegenerate triangle in the plane?
Question 16
A linear transformation is represented by the matrix A=(300 Which claim about area scaling is correct for any region in the plane?
Question 17
A linear map has matrix A=(30 Which claim about area scaling is correct for any region in the plane (for example, a parallelogram)?
Question 18
A linear transformation in the plane is represented by the matrix A=(100 Which statement describes the geometric effect of applying to the triangle with vertices , , and ?
Question 19
On the coordinate plane, the segment from (−2,1) to (2,1) is shown. A transformation T is defined by the matrix Which transformation does the matrix represent?
Question 20
A linear transformation is represented by the matrix A=(000 A vector is drawn from the origin to the point on the coordinate plane. Which statement describes the geometric effect?
∣
det
(
A
)
∣
=
1
The area is multiplied by 2 because the matrix has two nonzero columns.
1
det
∣det∣=1
0
0
)
.
V(4,−1)
ZV
All points map to the origin, so V goes to (0,0).
All points stay fixed, so V remains (4,−1).
The point is reflected across the y-axis.
The point’s distance from the origin doubles.
Explanation: This question examines the zero matrix and its geometric interpretation. The zero matrix Z = [[0,0],[0,0]] maps every vector to the zero vector, effectively collapsing the entire plane to a single point at the origin. The determinant of the zero matrix is 0, which geometrically means all areas become 0—the transformation collapses 2D regions into lower dimensions. When we apply Z to the point V(4,-1), we get Z·V = [[0,0],[0,0]]·[[4],[-1]] = [[0],[0]], so V maps to the origin (0,0). Students might incorrectly think points stay fixed because they see zeros as "doing nothing," but zeros in a matrix mean "multiply by zero." The strategy is to recognize that det(Z) = 0 signals dimensional collapse—all points converge to one location.
0−1
)
.
All areas become 0 because the determinant is negative.
All lengths are multiplied by −1 because the determinant is −1.
Areas keep the same size, but orientation is reversed.
The transformation is the identity because ∣det(M)∣=1.
Explanation: This question explores how negative determinants affect transformations. The matrix (100−1) reflects across the x-axis, keeping x-coordinates the same while negating y-coordinates. The determinant is 1×(−1)=−1, where the magnitude |−1| = 1 tells us areas are preserved, but the negative sign indicates orientation reversal. When you transform a shape, it maintains its size but flips its orientation—clockwise becomes counterclockwise or vice versa. This is different from the identity matrix (det = +1) which preserves both area and orientation. Students often misinterpret negative determinants as making areas negative or shrinking shapes, but areas can't be negative—only orientation can reverse. The strategy is: |det| gives area scaling, and the sign of det tells whether orientation flips.
0
1
)
.
The area becomes negative, so the square disappears.
The area is multiplied by ∣det(D)∣=1, so the area stays the same.
The area is multiplied by −1, so the area doubles in size.
The area is multiplied by 2 because one axis is reflected.
Explanation: Geometric interpretation of matrices involves seeing their impact on orientation and size of shapes in the plane. Identity matrices maintain both, zero matrices eliminate them, but reflection matrices like this flip orientation while preserving size. The determinant's sign shows orientation (negative for flip), and its absolute value scales areas, here ∣det(D)∣=1, preserving area. Applied to the unit square, this matrix reflects it over the y-axis, keeping the area unchanged at 1. This is justified because the scaling factor is 1 in magnitude, despite the flip. A misconception is that negative det(D) makes area negative and thus disappear, but areas are positive measures. For transfer, read determinant as signed area change, useful for distinguishing reflections in various transformations.
01
)
.
It collapses the plane to the origin because all off-diagonal entries are 0.
It leaves every point fixed because it is the identity matrix.
It reverses orientation because the determinant is −1.
It changes area by a factor of 2 because there are two ones on the diagonal.
Explanation: This question tests recognizing the identity matrix and its properties. The matrix (1001) is the identity matrix, which leaves every point exactly where it is—it's the "do nothing" transformation. The determinant is 1×1−0×0=1, confirming that areas are preserved without any scaling or flipping. Every vector (xy) maps to itself, so geometric figures maintain their shape, size, and position. The identity matrix plays the same role in matrix multiplication that the number 1 plays in regular multiplication—it's the neutral element. Students might think that having 1s on the diagonal means doubling (since there are two 1s), but the identity matrix is special. The key insight is: identity matrix = no change transformation, with determinant 1 confirming area preservation.
1
=
(1,0)
e2=(0,1)
A
Matrix: A=(01−10)
It collapses all areas to zero because the determinant is 0.
It scales all areas by a factor of 2 because lengths double.
It preserves area because ∣det(A)∣=1.
It reverses area by making all areas negative in magnitude.
Explanation: This question examines area preservation in rotational transformations. The matrix A=(01−10) represents a 90° counterclockwise rotation, sending (1,0) to (0,1) and (0,1) to (−1,0). The determinant is det(A)=0(0)−(−1)(1)=1, and since ∣det(A)∣=1, areas are preserved exactly. This matrix rotates shapes without changing their size, demonstrating that rotations are area-preserving transformations. The positive determinant also tells us the transformation preserves orientation (no reflection occurs). A misconception might be thinking that because we see a zero in the matrix, areas become zero, but the determinant calculation shows otherwise. The key insight is that ∣det(A)∣=1 always means area preservation, whether through rotation, reflection, or their combination.
021
)
.
(0,0)
(4,0)
(4,2)
(0,2)
The area is multiplied by 2 because the x-direction is stretched by 2.
The area is multiplied by 1 because ∣det(A)∣=1.
The area is multiplied by 21 because the y-direction is shrunk by .
The area becomes 0 because one direction is reduced.
Explanation: The skill of matrix interpretation allows us to see how transformations affect shapes like rectangles through scaling in different directions. Identity matrices preserve shapes, while zero matrices squash them to points. The determinant's absolute value geometrically measures the area scaling factor of the transformation. For this rectangle, the matrix A stretches the x-direction by 2 and shrinks y by 21, but the overall area remains the same since ∣det(A)∣=1. This is because the scalings compensate each other, maintaining the original area of 8. A misconception is thinking area multiplies only by the x-stretch of 2, ignoring the y-shrink, but determinant combines them. To apply elsewhere, read the determinant as the net area change, here preserving it at 1.
00
)
.
It leaves every vector unchanged.
It sends every vector to the origin.
It rotates every vector 180∘ about the origin.
It doubles the length of every vector without changing direction.
Explanation: This question examines the zero matrix and its effect on vectors. The zero matrix (0000) multiplies every vector to produce the zero vector, effectively sending all points to the origin. The determinant of the zero matrix is 0, which geometrically means that all areas collapse to zero—the entire plane is squashed down to a single point. When you multiply any vector (xy) by this matrix, you get (00), regardless of the original coordinates. This is the most extreme form of linear transformation, where all information about position is lost. Students might incorrectly think that having zeros means rotation (like a 90° rotation matrix has zeros), but the zero matrix is unique in sending everything to the origin. The transfer strategy is: determinant = 0 means complete collapse of area, and the zero matrix specifically collapses everything to a point.
3
)
Areas are scaled by a factor of 5.
Areas are scaled by a factor of 6.
Lengths are scaled by a factor of 6 in every direction.
Areas are scaled by a factor of 6.
Explanation: This question tests understanding of how diagonal matrices scale areas through their determinant. The matrix A=(2003) is a diagonal scaling matrix that stretches the x-direction by factor 2 and the y-direction by factor 3. The determinant is det(A)=2×3=6, which tells us that areas are scaled by a factor of 6. For a unit square, the transformed shape becomes a rectangle with dimensions 2 × 3, giving area 6 times the original. This illustrates the fundamental principle that determinant measures area scaling factor. A common misconception is adding the diagonal entries (2 + 3 = 5) instead of multiplying them, or confusing length scaling with area scaling. The key insight is that determinant equals the product of eigenvalues for diagonal matrices, directly giving the area scaling factor.
0
)
.
It preserves the square’s area because one entry is 4.
It multiplies the area by 4 because the x-direction scales by 4.
It collapses the square to a line segment (area becomes 0).
It leaves the square unchanged because the determinant is not needed.
Explanation: This question examines a singular matrix that collapses one dimension. The matrix A=(4000) stretches the x-direction by 4 but completely collapses the y-direction to 0. The determinant is 4×0−0×0=0, indicating total area collapse. When applied to the unit square, all points get projected onto the x-axis: vertices like (1,1) map to (4,0), creating a line segment from (0,0) to (4,0). This line segment has zero area, confirming the determinant's prediction. Students might focus on the "4" and think area quadruples, but the zero in the second diagonal entry dominates—any factor times zero is zero. The geometric insight: if any eigenvalue is 0, the transformation collapses at least one dimension, making all areas zero.
1
)
.
a=(1,0)
b=(0,1)
Because det(G)=0, the unit square’s area becomes 0 under the transformation.
Because det(G)=0, the unit square’s perimeter becomes 0 under the transformation.
Because det(G)=0, the unit square keeps its area but changes orientation.
Because det(G)=0, the unit square’s area is multiplied by −1 and flips.
Explanation: Geometric matrix interpretation reveals transformations that preserve or destroy dimensionality in shapes. Zero matrices collapse to points, identity preserves, but this matrix projects onto a line due to dependent rows. Determinant zero geometrically signals area collapse, as the image loses a dimension. For the unit square (parallelogram from basis vectors), G maps it to a line segment with zero area. This is justified by linearly dependent columns, flattening the shape. A distractor might think det=0 affects perimeter instead, but it's area that's nullified. To transfer, read determinant as area multiplier: zero means degeneration, helpful for any basis-defined parallelogram.
0
)
Every vector is sent to the origin.
All areas become zero after the transformation.
The transformation is the same as the identity on all points.
The determinant is 0, so the transformation is not invertible.
Explanation: This question addresses misconceptions about the zero matrix versus the identity matrix. The zero matrix A=(0000) sends every vector to the origin, collapsing all geometric information. The determinant is 0, confirming the transformation is not invertible and reduces all areas to zero. The student's claim that it "acts like the identity because it doesn't rotate anything" is fundamentally flawed - while it's true that no rotation occurs, this is because everything collapses to a single point, not because points are preserved. The identity matrix leaves all points unchanged, while the zero matrix destroys all position information. Options A, B, and D are all correct interpretations of the zero matrix's behavior. The key insight is that "not rotating" is very different from "preserving" - the zero matrix achieves non-rotation through total annihilation rather than preservation.
00
)
.
PQ
P(2,1)
Q(4,3)
PQ
It maps both endpoints to (0,0).
It keeps the segment the same length and location.
It reflects the segment across the x-axis.
It doubles the segment’s length without changing direction.
Explanation: The skill involves interpreting matrices, especially identity and zero types, for their geometric impact on segments in the plane. The identity matrix preserves all positions and lengths, whereas the zero matrix maps every point to the origin, eliminating distances. The determinant geometrically captures area scaling, where 0 denotes a transformation that squashes areas to nothing by reducing dimensionality. For the segment from P(2,1) to Q(4,3), the zero matrix transforms both endpoints to (0,0), collapsing the entire segment to a point. This is justified as matrix multiplication by zeros yields zero vectors for any input, merging distinct points. A common distractor is thinking it doubles length, perhaps misreading zeros as scaling factors. To transfer this, view the determinant as area change: 0 here explains the loss of any enclosed area.
0
)
A
It preserves the unit square exactly.
It collapses the square into a line segment, so the image has zero area.
It scales the square’s area by a factor of 3.
It rotates the square 180∘ about the origin.
Explanation: This question explores partial collapse in transformations with zero determinant. The matrix A=(3000) scales the x-direction by 3 but completely eliminates the y-component. The determinant is det(A)=3(0)−0(0)=0, indicating dimensional collapse. When applied to the unit square, all points are projected onto the x-axis: vertices (0,0),(1,0),(1,1),(0,1) map to (0,0),(3,0),(3,0),(0,0) respectively, forming a line segment from (0,0) to (3,0). This demonstrates that zero determinant always means the transformation is not invertible and reduces dimension. A misconception might be focusing on the 3 and thinking it triples area, but the zero in the second diagonal entry ensures total collapse in that direction. The key insight is that any zero on the diagonal of a diagonal matrix guarantees zero determinant and dimensional collapse.
24
)
.
Triangle areas are multiplied by 0 because det(M)=0.
Triangle areas are multiplied by 5 because 1+4=5.
Triangle areas are multiplied by 6 because 1⋅4+2⋅2=8.
Triangle areas are unchanged because the entries are integers.
Explanation: This question tests recognizing when a matrix has zero determinant. The matrix (1224) has determinant 1(4)−2(2)=4−4=0, which means all areas become zero after transformation. Geometrically, this matrix collapses the entire plane onto a line—specifically, the line through the origin with direction vector (12). Any triangle, no matter its original area, gets flattened onto this line and thus has zero area. The rows of the matrix are proportional (row 2 = 2 × row 1), which is why the transformation loses a dimension. Students might incorrectly add matrix entries or assume integer entries preserve area, but the determinant calculation is what matters. The key principle is: determinant = 0 means dimensional collapse, turning 2D regions into 1D lines or 0D points.
3
)
.
Areas are multiplied by 3 because each coordinate is multiplied by 3.
Areas are multiplied by 6 because 3+3=6.
Areas are multiplied by 9 because ∣det(A)∣=9.
Areas are unchanged because the determinant is positive.
Explanation: This question examines uniform scaling matrices and their area effects. The matrix A = [[3,0],[0,3]] = 3I scales all vectors by factor 3—it's a dilation centered at the origin. The determinant is det(A) = 3·3 - 0·0 = 9, and |det(A)| = 9 tells us areas are multiplied by 9. This makes sense geometrically: if both dimensions scale by 3, then area scales by 3² = 9. Students often make the error of thinking area scales linearly with the scaling factor (multiplying by 3 instead of 9), or adding the diagonal entries. The key insight is that area scaling is quadratic in linear scaling: when all lengths multiply by k, areas multiply by k². Read the determinant as the area multiplier directly.
0
−2
)
.
The area is multiplied by 1 because one factor is negative.
The area is multiplied by −6, so the region has negative area.
The area is multiplied by 6 because ∣det(A)∣=6.
The area is multiplied by 5 because 3+2=5.
Explanation: This question tests understanding of how diagonal matrices with mixed signs affect area. The matrix A=(300−2) stretches the x-direction by 3 and the y-direction by 2 (while also reflecting across the x-axis). The determinant is 3×(−2)−0×0=−6, and the absolute value ∣det(A)∣=6 gives the area scaling factor. Any region's area is multiplied by 6, regardless of the negative sign in the determinant. The negative sign indicates orientation reversal, not "negative area"—area is always non-negative by definition. A common error is thinking the negative cancels out scaling or that we should add diagonal entries. Remember: for area scaling, always use ∣det(A)∣, treating the determinant's magnitude as the stretch factor.
1
)
.
A
(0,0)
(2,0)
(0,3)
It collapses the triangle to a single point at the origin.
It leaves the triangle unchanged in position and shape.
It doubles the area of the triangle but keeps its orientation.
It reflects the triangle across the x-axis.
Explanation: This question tests understanding of the identity matrix and its geometric interpretation. The identity matrix (1001) leaves every vector unchanged: it multiplies the x-component by 1 and the y-component by 1. The determinant of the identity matrix is 1×1−0×0=1, which means areas are multiplied by 1 (unchanged). When applied to the triangle with vertices (0,0), (2,0), and (0,3), each vertex maps to itself, so the triangle remains in the same position with the same shape. The correct answer recognizes that the identity transformation preserves both position and shape. A common misconception is thinking that a matrix with 1s must double something, but the identity matrix is the "do nothing" transformation—like multiplying by 1 in arithmetic.
I=(1001).
A reflection across the y-axis.
A collapse of all points to the origin.
A 90∘ rotation about the origin.
No change to the segment’s location or length.
Explanation: Geometric interpretation of matrices helps understand transformations like identities that maintain segments' positions. Identity matrix leaves points fixed, zero sends to origin. Determinant of 1 preserves area and orientation without scaling. For this segment, I keeps it unchanged in location and length. Justification is that identity multiplication returns the same coordinates. Misconception: confusing with zero matrix causing collapse, but identity does nothing. Read determinant as area change, here 1 meaning no alteration.
1
)
.
v
(2,−3)
The vector is unchanged because one diagonal entry is 1.
The vector is reflected across the x-axis because a diagonal entry is 0.
The vector collapses onto the y-axis, so the x-component becomes 0.
The vector’s length is multiplied by 0, so it becomes the zero vector.
Explanation: This question tests understanding of projection onto coordinate axes. The matrix A = [[0,0],[0,1]] projects vectors onto the y-axis: it zeros out x-components while preserving y-components. For the vector from origin to (2,-3), we compute A·[[2],[-3]] = [[0,0],[0,1]]·[[2],[-3]] = [[0],[-3]], so the vector collapses from (2,-3) to (0,-3), lying entirely on the y-axis. The determinant is 0, confirming dimensional collapse. Students might think the vector becomes the zero vector because they see zeros in the matrix, but the preserved y-component prevents total collapse. The key insight is that this matrix selectively preserves one dimension while eliminating the other—it's a projection, not a complete annihilation.